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Modeling the Effectiveness of Employee Compensation Based on Financial Resources

Author

Listed:
  • L. B. Sungatullina
  • E. I. Kadochnikova
  • G. R. Faizrahmanova

Abstract

Modeling the effectiveness of employee compensation by evaluating the relationship with the factors of the labor intensity of products, work experience, and incentive payments based on a linear model of multiple regression on the main components. In this paper, several methods are utilized, including the classical least squares method, variation inflation factor, principal component method. It is expected with theoretical representations that the labor intensity of products reduces the efficiency of employee remuneration, the experience and incentive payments in the General Fund of remuneration positively contribute to the increase in the efficiency of employee remuneration. The expediency of applying linear regression to the main components for measuring internal corporate factors of the employee remuneration system is shown since the linear model of multiple regression can give incorrect estimates due to collinear regressors. A methodological way to modeling employee remuneration effectiveness based on a regression on individual determinants of the motivation and remuneration system has been developed. The developed methodological means to modeling employee remuneration effectiveness has been tested on a poultry enterprise's data for the period from January 2015 to March 2020. The article's main conclusions can be used in the scientific and practical activities of agricultural enterprises in measuring and evaluating the effectiveness of using financial resources to pay.

Suggested Citation

  • L. B. Sungatullina & E. I. Kadochnikova & G. R. Faizrahmanova, 2020. "Modeling the Effectiveness of Employee Compensation Based on Financial Resources," International Journal of Financial Research, International Journal of Financial Research, Sciedu Press, vol. 11(6), pages 63-72, December.
    • Handle: RePEc:jfr:ijfr11:v:11:y:2020:i:6:p:63-72
    DOI: 10.5430/ijfr.v11n6p63
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    References listed on IDEAS

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    3. L. B. Sungatullina* & A. Y. Sokolov & D. G. Yankovskaya & T. V. Elsukova & E. I .Kadochnikova, 2018. "Ways of Performance Improvement in Petrochemical Enterprise," The Journal of Social Sciences Research, Academic Research Publishing Group, pages 345-349:5.
    4. C.K. Chandrasekhar & H. Bagyalakshmi & M.R. Srinivasan & M. Gallo, 2016. "Partial ridge regression under multicollinearity," Journal of Applied Statistics, Taylor & Francis Journals, vol. 43(13), pages 2462-2473, October.
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