Investigation and Prioritizing the Effective Factors on Increasing the Human Resources Productivity in Agriculture Bank Using Multi-Attribute Decision Making Model


  •  Amir Abachi    

Abstract

As organizations are going to develop, the need for efficient manpower becomes more apparent. Obviously, productivity of the manpower requires the attention of managers to the complexity of human behavior and appropriate utilization of the principles, techniques and skills of the management. This study aims to prioritize the effective factors on productivity of human resources in the Agriculture Bank. Productivity is beyond the performance, it also contains the effectiveness concept, and in other words, productivity is not just doing the right things. An activity may be done correctly and in the best way, while it has no role in achieving the goal. In this case, the performance is available but there is no productivity. Difference between the performance and is rooted in the effectiveness or in the direction of doing a work. The current paper is a descriptive survey. Statistical population includes all experts in the Research and Strategic Planning center of the Agricultural Bank (33 persons). The data obtained from the questionnaire were analyzed using descriptive statistics in the form of frequency table. Questions were examined based on the one-group- t-test and using SPSS.  Effective factors on increasing the human resources productivity were prioritized using Multi- Attribute Decision Making (MADM). After comparison of the alternatives, the related tables were prepared and prioritizing or ranking were done by determining the weight of each factor indexes and finally determining the weight of the four main factors. TOPSIS was used to evaluate the results of the MADM. Our research aims to prioritize the four factors according to the MADM.


This work is licensed under a Creative Commons Attribution 4.0 License.
  • Issn(Print): 1913-1844
  • Issn(Onlne): 1913-1852
  • Started: 2007
  • Frequency: monthly

Journal Metrics

(The data was calculated based on Google Scholar Citations)

Google-based Impact Factor (2018): 6.49

h-index (January 2018): 30

i10-index (January 2018): 163

h5-index (January 2018): 19

h5-median(January 2018): 25

Contact