Analysis of the Economics of Talent Mobility Based on Logistic Model

[1] Lopina, E. C., Rogelberg, S. G., & Woznyj, H. (2019). Understanding older workers’ decisions to participate in voluntary training opportunities. Journal of Personnel Psychology, 18(4), 189-200. Search in Google Scholar

[2] Guan, S., & Frenkel, S. (2018). How perceptions of training impact employee performance: evidence from two chinese manufacturing firms. Personnel Review, 48(2). Search in Google Scholar

[3] Lovelace, Kathi, J., Gully, Stanley, & M., et al. (2017). Predicting readiness for diversity training the influence of perceived ethnic discrimination and dyadic dissimilarity. Journal of personnel psychology, 16(1), 25-35. Search in Google Scholar

[4] Miller, D., & Santos, R. M. (2020). The characteristics among maltreatment, special education service delivery, and personnel preparation:. The Journal of Special Education, (4). Search in Google Scholar

[5] Collings, D. G., & Isichei, M. (2017). The shifting boundaries of global staffing: integrating global talent management, alternative forms of international assignments and non-employees into the discussion. The International Journal of Human Resource Management, 1-23. Search in Google Scholar

[6] Hamilton, Lambert, Suss, & Biggs. (2019). Can cognitive training improve shoot/don’t-shoot performance? evidence from live fire exercises. The American Journal of Psychology, 132(2), 179. Search in Google Scholar

[7] Zelik, G., & Uyargil, C. B. (2021). Does hrm’s reality fit with those of others? exploring and understanding hr attributions. Personnel Review. Search in Google Scholar

[8] Meglich, P., Valentine, S., & Eesley, D. (2019). Perceptions of supervisor competence, perceived employee mobility, and abusive supervision: human capital and personnel investments as means for reducing maltreatment in the workplace. Personnel Review, 48(3), 691-706. Search in Google Scholar

[9] Aldawood, H., & Alhossein, A. (2018). The attitudes of school personnel toward using positive behavior support and the obstacles to implement it. The Journal of Special Education, 8, 81-105. Search in Google Scholar

[10] Lunkes, R. J., Rosa, F., Monteiro, J. J., & Bortoluzzi, D. A. (2020). Interactions among environmental training, environmental strategic planning and personnel controls in radical environmental innovation. Sustainability, 12. Search in Google Scholar

[11] Zhou, Y., Guo, Y., & Liu, Y. (2018). High-level talent flow and its influence on regional unbalanced development in china. Applied Geography, 91, 89-98. Search in Google Scholar

[12] Zhang, H., Deng, T., Wang, M., & Chen, X. (2019). Content analysis of talent policy on promoting sustainable development of talent: taking sichuan province as an example. Sustainability, 11. Search in Google Scholar

[13] Makarius, E. E., & Srinivasan, M. (2017). Addressing skills mismatch: utilizing talent supply chain management to enhance collaboration between companies and talent suppliers. Business Horizons, 60(4), 495-505. Search in Google Scholar

[14] Kjus, & Yngvar. (2017). Harmonious or out of tune: cooperation between the television industry and the music business in talent contests of the 2000s. Media Culture & Society, 016344371668667. Search in Google Scholar

[15] Vasilakis, C. (2017). Does talent migration increase inequality? a quantitative assessment in football labour market. Journal of Economic Dynamics & Control, 85(dec.), 150-166. Search in Google Scholar

[16] Yan, L., Huang, Z., Zhang, Y., Zhang, L., & Ran, B. (2017). Driving risk status prediction using bayesian networks and logistic regression. IET Intelligent Transport Systems, 11(7), 431-439. Search in Google Scholar

[17] Salas-Eljatib, C., Fuentes-Ramirez, A., Gregoire, T. G., Altamirano, A., & Yaitul, V. (2018). A study on the effects of unbalanced data when fitting logistic regression models in ecology. Ecological Indicators, 85, 502-508. Search in Google Scholar

[18] Jiang, Y., Tian, G. L., & Fei, Y. (2019). A robust and efficient estimation method for partially nonlinear models via a new mm algorithm. Statistical Papers, 60(6), 2063-2085. Search in Google Scholar

[19] Sharma, R., Kewat, S., & Singh, B. (2020). Robust mmsogi-fll control algorithm for power quality improvement of solar pv- pico hydro-bes based islanded microgrid with dynamic load. IET Power Electronics, 13(8). Search in Google Scholar

[20] Lee, N. Y., Lo, C. L., Chen, P. L., Syue, L. S., & Ko, W. C. (2020). Clinical impact of cefepime breakpoint in patients with carbapenem-resistant klebsiella pneumoniae bacteremia. International Journal of Antimicrobial Agents, 57(2), 106250. Search in Google Scholar

[21] Yang, LJ, Liu, SS, Tsoka, & Papageorgiou, et al. (2017). A regression tree approach using mathematical programming. EXPERT SYST APPL, 2017, 78(-), 347-357. Search in Google Scholar