The evaluation of it specialists’ performance based on the grading system and it’s forecasting by means of regression models
AbstractThe article is devoted to the actual problem of the substantiation of the laws and conditions of the relationship between the personal development and human potential of IT specialists and the results of their activities in the organization. The purpose of the article is the analysis and prediction of the performance of IT specialists by the grading system using regression analysis, where the predictors are the individual potentials of IT specialists. This problem is considered on the example of IT-professionals (n = 182) from four countries of Central and Eastern Europe. The author’s grading system was used to determine the level of professional efficiency during the trial period. The models for forecasting the professional performance of IT professionals were created, confirmed by various methods of regression analysis. It is determined that such aspects of personal development and human potential as social intelligence, abstract thinking, decisiveness, dominance, intrinsic motivation, communication, resistance to emotional stress, openness, and susceptibility to any kind of knowledge are predictors of effective performance in the IT industry.
Stepanova, A. A., Bilokrinitsʹka, K. V. (2017). Greyduvannya yak suchasna systema oplaty pratsi na Ukrayinsʹkykh pidpriyemstvakh na prikladi PAT "KYYIVENERHO", 1, 40-43.
Shakhno, A. Yu. (2013). Systema greyduvannya yak suchasna forma oplaty pratsi na pidpryyemstvi/ Innovatsiyna ekonomika. - 2013. - № 6. - S. 187-194.
Cowling, A. (2013). Managing Human Resources. Abingdon-on-Thames: Routledge.
Gutiérrez, P.A., Pérez-Ortiz, M., Sánchez-Monedero, J., Fernández-Navarro, F., Hervás-Martínez, C. (2016). Ordinal Regression Methods: Survey and Experimental Study. IEEE Transactions on Knowledge and Data Engineering, 28 (1), 127–146.
Hay, E.N. (1946). The Characteristics of Salary Grades. Personnel, 23, 3, 14.
Keith, T. Z. (2015). Multiple Regression and Beyond: An Introduction to Multiple Regression and Structural Equation Modeling. New York, NY: Taylor & Francis.
Shashua, А., Levin, A. (2002). Ranking with Large Margin Principle: Two Approaches. Advances in Neural Information Processing Systems, 15, 937-944.
Abstract views: 58 PDF Downloads: 76