Research Article

PREDICTION of WORKER MOTIVATION with ARTIFICIAL NEURAL NETWORKS and LINEAR MODELING

Number: 051 December 31, 2022
EN

PREDICTION of WORKER MOTIVATION with ARTIFICIAL NEURAL NETWORKS and LINEAR MODELING

Abstract

Organizational justice is a motivation tool that can produce positive results for the organization and employees in working life. The decrease in the perception of justice can cause moral disorders of the employees, may lead them to leave the organization and even to engage in negative behaviors towards the organization. This study was carried out to determine the effect of organizational justice perceived by employees on employee motivation and to predict organizational justice and motivation. The research was carried out with 294 participants working in public institutions serving in Isparta. Firstly, multiple regression analysis was conducted to test the effect of organizational justice on employee motivation. Within the scope of the study, linear modeling and artificial neural networks (ANN) models were also compared in order to contribute to the literature. Multiple regression analysis results showed that interactional and distributive justice had a significant and direct effect on motivation. In addition, it was determined that the highest predictive power was ANN (R² = 0.88) according to motivation models. As a result of the study, the predictability of the organizational justice phenomenon perceived by the employees and the motivation of the employee has emerged.

Keywords

Thanks

The authors declare that there is no conflict of interest. Also, thanks to Emre KUZUGUDENLI and Canpolat KAYA for their model suggestions. This study was presented as a summary paper with the title "The Effect of Organizational Justice on Employee Motivation: An Application with Linear and Artificial Neural Network Models" at the 3rd International Conference on Applied Engineering and Natural Sciences held on 20-23 July 2022

References

  1. [1] Mueller, C.W., and Wynn T., (2000), The degree to which justice is valued in the workplace, Social Justice Research, 13(1):1–24.
  2. [2] Folger, R., and Cropanzano R., (1998), Organizational justice and human resource management, Thousand Oaks / California: SAGE Publications.
  3. [3] Colquitt, J.A., Lepine J., and Wesson M., (2018), Organizational behavior: Improving performance and commitment in the workplace (6th Edition), New York: McGraw-Hill Education.
  4. [4] Colquitt, J.A., (2012), Organizational justice, In: Kozlowski, S. W. (Ed.), The Oxford handbook of organizational psychology, Vol 1., New York: Oxford University Press, pp.526–547.
  5. [5] Adams, J.S., (1963), Towards an understanding of inequity. Journal of Abnormal and Social Psychology, 67(5):422–436.
  6. [6] Lambert, E., (2003), The impact of organizational justice on correctional staff, Journal of Criminal Justice, 31(2):155–168.
  7. [7] Chen, W., and Lee Y., (2022), Revisiting proficiency pairing in collaborative writing from an equity theory perspective: Voices from high-proficiency efl learners, SAGE Open. April:1–11.
  8. [8] Folger, R., (1993), Justice, motivation, and performance beyond role requirements, Employee Responsibilities and Rights Journal, 6(3):239–248.

Details

Primary Language

English

Subjects

-

Journal Section

Research Article

Publication Date

December 31, 2022

Submission Date

July 17, 2022

Acceptance Date

August 11, 2022

Published in Issue

Year 2022 Number: 051

APA
Erdemir, A., Seyran, F., & Batırer, T. (2022). PREDICTION of WORKER MOTIVATION with ARTIFICIAL NEURAL NETWORKS and LINEAR MODELING. Journal of Scientific Reports-A, 051, 330-339. https://izlik.org/JA47DY38LE
AMA
1.Erdemir A, Seyran F, Batırer T. PREDICTION of WORKER MOTIVATION with ARTIFICIAL NEURAL NETWORKS and LINEAR MODELING. JSR-A. 2022;(051):330-339. https://izlik.org/JA47DY38LE
Chicago
Erdemir, Akın, Fatih Seyran, and Tuğrul Batırer. 2022. “PREDICTION of WORKER MOTIVATION With ARTIFICIAL NEURAL NETWORKS and LINEAR MODELING”. Journal of Scientific Reports-A, nos. 051: 330-39. https://izlik.org/JA47DY38LE.
EndNote
Erdemir A, Seyran F, Batırer T (December 1, 2022) PREDICTION of WORKER MOTIVATION with ARTIFICIAL NEURAL NETWORKS and LINEAR MODELING. Journal of Scientific Reports-A 051 330–339.
IEEE
[1]A. Erdemir, F. Seyran, and T. Batırer, “PREDICTION of WORKER MOTIVATION with ARTIFICIAL NEURAL NETWORKS and LINEAR MODELING”, JSR-A, no. 051, pp. 330–339, Dec. 2022, [Online]. Available: https://izlik.org/JA47DY38LE
ISNAD
Erdemir, Akın - Seyran, Fatih - Batırer, Tuğrul. “PREDICTION of WORKER MOTIVATION With ARTIFICIAL NEURAL NETWORKS and LINEAR MODELING”. Journal of Scientific Reports-A. 051 (December 1, 2022): 330-339. https://izlik.org/JA47DY38LE.
JAMA
1.Erdemir A, Seyran F, Batırer T. PREDICTION of WORKER MOTIVATION with ARTIFICIAL NEURAL NETWORKS and LINEAR MODELING. JSR-A. 2022;:330–339.
MLA
Erdemir, Akın, et al. “PREDICTION of WORKER MOTIVATION With ARTIFICIAL NEURAL NETWORKS and LINEAR MODELING”. Journal of Scientific Reports-A, no. 051, Dec. 2022, pp. 330-9, https://izlik.org/JA47DY38LE.
Vancouver
1.Akın Erdemir, Fatih Seyran, Tuğrul Batırer. PREDICTION of WORKER MOTIVATION with ARTIFICIAL NEURAL NETWORKS and LINEAR MODELING. JSR-A [Internet]. 2022 Dec. 1;(051):330-9. Available from: https://izlik.org/JA47DY38LE