Strategic Human Resource Management and Corporate Performance
- 1 Jinan University, Guangdong, China
Abstract
It is essential for success to build an organization filled with good, talented, well-trained people, which is also the center of human resource management. The author combined the complex network with human capital to solve the problems. The author employ ed fuzzy synthetic evaluation method to figure out the Euclidean distance between different departments in ICM, and then built the human capital network of various departments. Based on the Lyapunov stability theory, the author described the dynamic process of human capital in ICM. Through establishing the dynamic complex network model of human capital, the author drew the conclusion that the next two years’ budget of recruiting and training is 34.22 σ when the annual churn rate goes to 18%. By Pajek programming, the author used the visualization tools to simulate the staff flow. Through establishing the dynamic simulation model of the staff turnover, the author reached the position’s integrity condition of ICM when the job churn rate is 25% and 35%, respectively. Then the author explained the costs caused by high turnover rates and the indirect effects of high churn rates. The author simulate d the change of position’s integrity degree of junior managers and experienced supervisors in the next two years. The author concluded that the HR health of the organization is below the expectation. In addition, the author made sensitivity analysis on the 7 factors of human capital value and consequently drew that the amount of employees of each hierarchy has the greatest influence on human capital value. At last, to make further disc ussion on the model, the author established the multilayer complex network model including flow, trust, influence and friendship.
- Martins, T.V., et al. (2009) Network Effects in a Human Capital Based Economic Growth Model. Physica A, 388, 2207-2214. https://doi.org/10.1016/j.physa.2009.02.006
- Pozzi, F., Bottino, R.M. and Persico, D. (2014) Enhancing Human Capital in TEL Research: A Case Study from the STELLAR Network of Excellence. Computers in Human Behavior, 31, 425-431. https://doi.org/10.1016/j.chb.2013.10.062
- Fagan, J. and Ployhart, R.E. (2015) The Information Processing Foundations of Human Capital Resources: Leveraging Insights from Information Processing Approaches to Intelligence. Human Resource Management Review, 25, 4-11. https://doi.org/10.1016/j.hrmr.2014.09.003
- Kivela, M., Arenas, A., Barthelemy, M., Gleeson, J.P., Moreno, Y. and Porter, M.A. (2013) Multilayer Networks. Journal of Complex Networks, 2, 203-271. https://doi.org/10.1093/comnet/cnu016
- Wu, D.T. and Li, D.F. (2004) Shortcomings of Analytical Hierarchy Process and the Path to Improve the Method. Journal of Beijing Normal University (Natural Science), 40, 265-267.
- Zhang, L.L., Wang, Y.H. and Wang, Q.Y. (2015) Synchronization for Time-Varying Complex Dynamical Networks with Different-Dimensional Nodes and Non-Dissipative Coupling. Communications in Nonlinear Science and Numerical Simulation, 24, 64-74. https://doi.org/10.1016/j.cnsns.2014.12.012
- Zhang, L.L., Wang, Y.H., Wang, Q.Y., Wang, Q.R. and Zhang, Y. (2013) Synchronisation of Complex Dynamical Networks with Dynamics of Nodes via Decentralised Dynamical Compensation Conrollers. International Journal of Control, 86, 1766-1776. https://doi.org/10.1080/00207179.2013.796525
- Hrg, D. (2013) Synchronization of Two Hindmarsh-Rose Neurons with Unidirectional Coupling. Neural Networks, 40, 73-79. https://doi.org/10.1016/j.neunet.2012.12.010
- de Nooy, W., Mrvar, A. and Batagelj, V. Exploratory Social Network Analysis with Pajek. https://www.docin.com/p-1774451668.html