Identification and Prioritization of Criteria affecting the Productivity of Production Factors in Broiler Industry Using Fuzzy Best-Worst Method: A Case Study of West Azerbaijan Province of Iran

Document Type : Original Article

Authors

1 Assistant Professor of Industrial Management, Imam Khomeini International University (IKIU), Qazvin, Iran

2 Associate Professor of Industrial Management, University of Tehran, Tehran, Iran

Abstract

The economic, industrial, social and cultural conditions of Iran are in a way that solving different problems requires new patterns and solutions. Broiler chicken farming is one of the country's most important agricultural sub-sectors, which has a substantial role in agricultural production and employment. Considering that productivity plays an important role in economic activities, it is important to determine the productivity and effective factors on it. Therefore, in this research, firstly, the most important criteria affecting the productivity of the factors of production in the broiler chicken industry were identified using experts' opinions. Then, using the best-worst-fuzzy method, these criteria were weighted. For this purpose, 260 questionnaires were given to the broiler production units of West Azerbaijan province through the available sampling method; and after data collection, the data analysis were done on 209 usable questionnaires. The results showed that a total of 21 indicators affected the productivity of the factors of production in the broiler industry, standing within the four main categories of human resources, costs, capital, and materials. The most important indicator was found to be the live poultry selling price, followed by the indicator of breeding period. Finally, it was suggested that identifying such criteria would lead to more effective ways to improve productivity.

Keywords


  1. Ahmadpour daryani, M. (2014). Entrepreneurship definitions, theories, models. Tehran: Sako Publications. (Persian)
  2. Rodríguez, L., Díaz, J., Garbajosa, J., Pérez, J. & Yague, A. (2014). A framework for positioning and assessing innovation capability from an organizational perspective. In System Sciences (HICSS). 2014 47th Hawaii International Conference on (pp. 3564-3573). IEEE.
  3. Ahadi, S., Dermani Kohi, H., Ghavi Hosseinzadeh, N. (2017). Effect of nutrients density on growth performance, carcass parts and growth curve characteristics in Japanese quails. Animal Sciences Journal, 29(113): 123-134. (Persian)
  4. Motamed, M., Pourkand, S. (2011). Productivity of production factors in broilers production: case study of Gilan province. Agricultural Economics Research, 3(12): 97-114. (Persian)
  5. Haji Rahimi, M. & Karimi, A. (2009). Factors productivity analysis of broiler chicken industry in Kurdistan province. Agricultural Economics and Development, 17(2): 1-18. (Persian)
  6. Ghabezi, R. (2013). Investigation of human resource productivity for research center (case study: research institute of petroleum industry). Quarterly Journal of Innovation and Entrepreneurship, 1 (3) :111-122. (Persian)
  7. Livestock Production Department of the Ministry of Agriculture Jahad (2017). Livestock Production Statistics from 2008 to 2016. Tehran: Ministry of Agriculture Jahad. http://dla.maj.ir/Dorsapax/userfiles/Sub5/ tolid87-95.pdf. (Persian)
  8. Vatankhah, M. (1998). Investigating ways to increase productivity in rural livestock and its role in sustainable development. Third National Iranian Productivity Congress. Tehran.
  9. Dezhpasand, F. (2005). Factors affecting Iran's economic growth. Economics Research, 5(18): 13-47. (Persian)
  10. Abtahi, H. & Kazemi, B. (2016). Productivity (Principles, Foundations, Means of Increase and Measurement). Tehran: Foozhan Publications. (Persian)
  11. Long, R., Shao, T. & Chen, H. (2016). Spatial econometric analysis of China’s province-level industrial carbon productivity and its influencing factors. Applied Energy, 166: 210-219.
  12. Choi, K., Lee, D., & Olson, D. L. (2015). Service quality and productivity in the US airline industry: a service quality-adjusted DEA model. Service Business, 9(1): 137-160.
  13. Gollin, D. & Rogerson, R. (2014). Productivity, transport costs and subsistence agriculture. Journal of Development Economics, 107: 38-48.
  14. Khaledi, K. & Shirazi, A. H. (2014). Estimates of factors affecting economic growh in the agricultural sector in the fifth development plan (emphasis on investment). Extensive Journal of Applied Sciences, 3(5): 137-143.
  15. Rezaei J., Tavakoli Baghdadabad, M.R. & Faghih Nasiri, M. (2008). An evaluation of changes in total productivity of factors of production in Iran's agricultural sector using non parametric methods. Village and Development, 11(3): 97-122. (Persian)
  16. Nabieooni, A. (2011). Calculation of the productivity of production factors (labor, land and capital) in agricultural sector of Markazi province. Work and Society, 141: 67-79. (Persian)
  17. Tahamipour, M. & Shahmoradi, M. (2008). Measuring the growth of total factor productivity of agricultural sector and its contribution to the growth of value added sector. Agricultural Economics, 1(2): 10-20. (Persian)
  18. FAO (2009). The state of food and agriculture: Livestock in balance. Rome. Retrieved from http://www.fao.org/docrep/012/i0680e/ i0680e.pdf
  19. Ebadzadeh, H., Ahmadi, K., Mohamadnia Afroozi, Sh., Taghani, R., Moradi Eslami, A., Abbasi, M. & Yari, Sh. (2017). Agricultural statistics year 1394. Tehran: Ministry of Agriculture Jahad. http://amar.maj.ir/Portal/File/ShowFile.aspx?ID=d6a3d890-3510-4bf1-b0ee-1377027834c1. (Persian)
  20. Mohaghar, A. & Amin Naseri, M.R. (2010). Determining and explaining the indicators of the decisions of the Islamic Consultative Assembly. The Modares Journal of Management Research in Iran, 5(2): 155-177. (Persian)
  21. Asgharizadeh, E. & Nasrollahi, M. (2007). Ranking the firms based on excellency model Criteria – PROMETHEE Method. The Modares Journal of Management Research in Iran, 11(3): 59-84.
  22. Sarmad, Z., Bazargan, A. & Hejazi, E. (2017). Rasearch methods in social sciences. Tehran: Agah Publications. (Persian)
  23. Werts, C. E., Linn, R. L. & Jöreskog, K. G. (1974). Intraclass reliability estimates: Testing structural assumptions. Educational and Psychological Measurement, 34(1): 25-33.
  24. Nunnally, J. (1978). Pschomertric theory 2nd Ed. .New York: MCGraw Hill.
  25. Barclay, D., Higgins, C. & Thompson, R. (1995). The partial least square (PLS) approach to causal modeling: personal computer adoption and as an illustration. Technology Studies, 2(2): 285-309.
  26. Fornell, C. & Larcker, D. F. (1981). Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research, 18: 39-50.
  27. Van de Kaa, G., Kamp, L. & Rezaei, J. (2017). Selection of biomass thermochemical conversion technology in the Netherlands: a best worst method approach. Journal of Cleaner Production, 166: 32-39.
  28. Rezaei, J. (2015). Best-worst multi-criteria decision-making method. Omega, 53: 49-57.
  29. Guo, S. & Zhao, H. (2017). Fuzzy best-worst multi-criteria decision-making method and its applications. Knowledge-Based Systems, 121: 23-31.
  30. Mosavi, S. N. & Varz, S. M. D. (2014). The investigation of influential factors on the productivity of broiler farming units (case study: Markazi province). Advances in Environmental Biology, 8(25): 383-395.
  31. Asadabadi, E. & Abdpour, A. (2014). An evaluation of production factors productivity in agricultural holdings producing Mazafati dates: A case study. International Journal of Scientific & Engineering Research, 5(1).