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Frontiers of Engineering Management

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Front. Eng    2019, Vol. 6 Issue (2) : 249-261    https://doi.org/10.1007/s42524-019-0014-4
RESEARCH ARTICLE
Industry effect of job hopping: an agent-based simulation of Chinese construction workers
Jide SUN1, Mian ZHENG1(), Martin SKITMORE2, Bo XIA2, Xincheng WANG3
1. School of Economics and Management, Tongji University, Shanghai 200082, China
2. School of Civil Engineering and Built Environment, Queensland University of Technology, Queensland, Australia
3. Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai 200240, China
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Abstract

Job hopping affects the development of industries in terms of efficiency and quality of work. It is a problem for the Chinese construction industry, where excessive job hopping is detrimental to meeting the current daunting challenges involved in the industry’s transformation and efficiency improvement. To provide an exhaustive analysis of this effect, game theory is combined with social relationship networks to create an agent-based simulation model. Simulation results indicate that the frequent job moves of Chinese construction workers have a negative effect on their skill development, employment, and worker relationships, as well as results in sharp increase in employer labor costs. The findings point to the need to act for the benefit of workers and employers and maintain the development of the industry.
Keywords job hopping      agent-based simulation      construction industry      effect      China     
Corresponding Author(s): Mian ZHENG   
Online First Date: 18 February 2019    Issue Date: 17 May 2019
 Cite this article:   
Jide SUN,Mian ZHENG,Martin SKITMORE, et al. Industry effect of job hopping: an agent-based simulation of Chinese construction workers[J]. Front. Eng, 2019, 6(2): 249-261.
 URL:  
https://academic.hep.com.cn/fem/EN/10.1007/s42524-019-0014-4
https://academic.hep.com.cn/fem/EN/Y2019/V6/I2/249
Fig.1  Labor interest game tree
Fig.2  Work guarantee game tree
Fig.3  Personal value-recognition game tree
Fig.4  Flowchart of the dynamic evolution simulation
Variable name Variable value
Number of workers, i 180
Number of employers, j 12
Run time, T 20
Initial monthly salary, Payment0 N (3,300, 150)
Scale of a workers’ initial social relationships 6
Scale of an employer’s initial social relationships 10
Strength of initial social relationships,RS0 N (0.6, 0.1)
Strength of workers’ new social interrelationships RSnw~N (0.25, 0.01)
Strength of workers’ new social interrelationships and relationships with employers RSnc~N (0.40, 0.02)
Rate of relationship strengthening 20%, RSt=1.2* RSt-1
Rate of relationship weakening 20%, RSt=0.8* RSt-1
Strength of relationship discontinuations 0.2
Limits of team scale, Wmax, Wmin 15, - 6
Employment movementcosts, MC 200
Salary increase, SI 350
Initial extent of the work guarantee system WorkGuarantee0~N (0.20, 0.01)
Baseline value of the extent of the work guarantee system 0.4
Increase in extent of the work guarantee system, WI 0.1
Personal value recognition variable, Personalvalue Personalvalue ~(0, 1)
Initial training costs, Traininggcost0 200
Tab.1  Variable configuration in the simulation experiment
Supply–demand situation Number of workers Average employment movement rate Maximum employment movement rate
Extremely short supply 60 0.10 0.15
Short supply 120 0.17 0.22
Balance 180 0.03 0.20
Excess supply 240 0.00 0.10
Tab.2  Number of workers and corresponding employment movement rates
Fig.5  Employment movement rates for four supply–demand situations
Fig.6  Average training costs for four supply–demand situations
Fig.7  Average payment in the four supply–demand situations
Fig.8  Extent of the work guarantee system in the four supply–demand situations
Fig.9  Social relationship strength
Fig.10  Social relationship scale and number of dead relationships under excess supply
Fig.11  Social relationship scale and number of dead relationships under short supply
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