Bakhshi, M., Pourtahery, M., Roknadin Eftekhari, A. (2016). Developing a Model to Predict Success of Agricultural Production Enterprises Based on Their Capitals. Journal of Agricultural Science and Technology, 18(6), 1443-1454.
M. Bakhshi; M. Pourtahery; A. Roknadin Eftekhari. "Developing a Model to Predict Success of Agricultural Production Enterprises Based on Their Capitals". Journal of Agricultural Science and Technology, 18, 6, 2016, 1443-1454.
Bakhshi, M., Pourtahery, M., Roknadin Eftekhari, A. (2016). 'Developing a Model to Predict Success of Agricultural Production Enterprises Based on Their Capitals', Journal of Agricultural Science and Technology, 18(6), pp. 1443-1454.
Bakhshi, M., Pourtahery, M., Roknadin Eftekhari, A. Developing a Model to Predict Success of Agricultural Production Enterprises Based on Their Capitals. Journal of Agricultural Science and Technology, 2016; 18(6): 1443-1454.
Developing a Model to Predict Success of Agricultural Production Enterprises Based on Their Capitals
1Agricultural Planning, Economic and Rural Development Research Institute, Ministry of Jihad-e- Agricultural, Tehran, Islamic Republic of Iran.
2Geography and Rural Planning Department, Faculty of Humanities, Tarbiat Modares University, Tehran, Islamic Republic of Iran.
Receive Date: 13 April 2015,
Revise Date: 07 November 2015,
Accept Date: 13 July 2016
Abstract
This study aimed to develop a recognition model in order to classify success of agricultural enterprises. To this end, the study investigated the relationship between capitals owned by the enterprise and the success level by using "Neural Network" model. This study was conducted during 2013-2014 in Zanjan County, Islamic Republic of Iran. Data was obtained through a structured questionnaire and holding interviews with 92 enterprise owners, out of 125, involved in producing agrifood. According to the results of data analysis, Multilayer Perceptron Neural Network with Backpropagation algorithm was the appropriate algorithm to cope with the whole scope of the study. Empirical analysis by SPSS indicated that the Multilayer Perceptron consisting of one hidden layer with 6 nodes was an appropriate architecture. Classification Accuracy Rate (CAR) and "Receiver Operating Characteristic (ROC)" curve were used to assess the model. Based on CAR of holdout data, the model was able to classify 86.4% of the samples correctly. Also, the study intended to reveal the relative importance of explanatory factors on enterprise success. Results indicated that human and social capitals were the more influential factors, followed by economic and environmental capitals. Therefore, to promote agricultural enterprises, policy makers and managers need to improve software and hardware assets, simultaneously.
1. Agarwal, S., Rahman, S. and Errington, A. 2009. Measuring the Determinants of Relative Economic Performance of Rural Areas. J. Rural Stud., 25(3): 309-321. 2. Anandarajan, M., Lee, P. and Anandarajan, A. 2001. Bankruptcy Prediction of Financially Stressed Firms: An Examination of the Predictive Accuracy of Artificial Neural Networks. ISAFM, 10(2): 69-81. 3. Anderson, A. R. and Miller, C. J. 2003. “Class Matters”: Human and Social Capital in the Entrepreneurial Process. J. Socio. Econ., 32(1): 17-36. 4. Beale, M., Hagan, M. T. and Demuth, H. B. 2010. Neural Network Toolbox™ 7 User’s Guid. www.mathworks.com. 5. Brouthers, L. E., Mukhopadhyay, S., Wilkinson, T. J. and Brouthers, K. D. 2009. International Market Selection and Subsidiary Performance: A Neural Network Approach. J. World Bus., 44(3): 262-273. 6. Cao, Y., Chen, X., Wu, D. D. and Mo, M. 2011. Early Warning of Enterprise Decline in a Life Cycle Using Neural Networks and Rough Set Theory. Expert Syst. Appl., 38(6): 6424-6429. 7. Charalambous, C., Charitou, A. and Kaourou, F. 2000. Comparative Analysis of Artificial Neural Network Models: Application in Bankruptcy Prediction. Ann. Oper. Res., 99(4): 403–425. 8. Darroch, M. A. and Clover, T. A. 2005. The Effects of Entrepreneurial Quality on the Success of Small, Medium and Micro Agribusinesses in KwaZulu-Natal, South Africa. Agrekon, 44(3): 321-343. 9. Greiner, M., Pfeiffer, D. and Smith, R. D. 2000. Principles and Practical Application of the Receiver-Operating Characteristic Analysis for Diagnostic Tests. Prev. Vet. Med., 45(1): 23-41. 10. Svendsen, G. L. H., Kjeldsen, C. and Noe, E. 2010. How Do Private Entrepreneurs Transform Local Social Capital into Economic Capital? Four Case Studies from Rural Denmark. J. Socio. Econ., 39(6): 631-644. 11. Guo, W., Xiao, H. and Yang, X. 2012. An Empirical Research on the Correlation between Human Capital and Career Success of Knowledge Workers in Enterprise. Phys. Procedia, 25: 715-725. 12. Javalgi, R. R. G. and Todd, P. R. 2011. Entrepreneurial Orientation, Management Commitment, and Human Capital: The Internationalization of SMEs in India. J. Bus. Res., 64(9): 1004-1010. 13. Jiuju, C. 2013. Modeling and Optimization of Marketing Based on Artificial Neural Network. In: “Information Technology and Industrial Engineering (Set)”, (Eds.): Ren, P. and Du, Z.. WIT Press, UK, PP. 357–364. 14. Kaasa, A. and Parts, E. 2008. Human Capital and Social Capital as Interacting Factors of Economic Development: Evidence from Europe. In Working Paper IAREG WP2/04. 15. Keyvanpour, M. R., Javideh, M. and Ebrahimi, M. R. 2011. Detecting and Investigating Crime by Means of Data Mining: A General Crime Matching Framework. Procedia. Comput. Sci., 3: 872-880. 16. Khataee, A. R. and Kasiri, M. B. 2011. Modeling of Biological Water and Wastewater Treatment Processes Using Artificial Neural Networks. CLEAN–Soil Air Water, 39(8): 742-749. 17. Krishna, A. 2004. Understanding, Measuring and Utilizing Social Capital: Clarifying Concepts and Presenting a Field Application from India. Agric. Syst., 82(3): 291-305. 18. Li-Fang, Q., Yi-Chuan, Z., An-Guo, Q. and Xin-Zheng, L. 2012. Optimizing Rank of Landscape Planning Works of Urban Wetland Park. Northeast Agric. Univ., 19(3): 87-91. 19. Liu, Q., Cui, X., Chou, Y. C., Abbod, M. F., Lin, J. and Shieh, J. S. 2015. Ensemble Artificial Neural Networks Applied to Predict the Key Risk Factors of Hip Bone Fracture for Elders. Biomed. Signal Process. Control, 21: 146-156. 20. Lu, C. J. and Wu, J. Y. 2011. An Efficient CMAC Neural Network for Stock Index Forecasting. Expert Syst. Appl., 38(12): 15194-15201. 21. Magnani, N. and Struffi, L. 2009. Translation Sociology and Social Capital in Rural Development Initiatives. A Case Study from the Italian Alps. J. Rural Stud., 25(2): 231-238 22. Magoulas, G. D., Plagianakos, V. P. and Vrahatis, M. N. 2004. Neural Network-based Colonoscopic Diagnosis Using On-line Learning and Differential Evolution. Appl. Soft Comput., 4(4): 369-379. 23. Martin, B. C., McNally, J. J. and Kay, M. J. 2013. Examining the Formation of Human Capital in Entrepreneurship: a Meta-analysis of Entrepreneurship Education Outcomes. J. Bus. Venturing, 28(2): 211-224. 24. Martin-Collado, D., Soini, K., Mäki-Tanila, A., Toro, M. A. and Díaz, C. 2014. Defining Farmer Typology to Analyze the Current State and Development Prospects of Livestock Breeds: The Avileña-Negra Ibérica Beef Cattle Breed as a Case Study. Livest. Sci., 169: 137-145. 25. Mauerhofer, V. 2008. 3-D Sustainability: An Approach for Priority Setting in Situation of Conflicting Interests towards a Sustainable Development. Ecol. Econ., 64(3): 496-506. 26. Min, J. H. and Lee, Y. C. 2005. Bankruptcy Prediction Using Support Vector Machine with Optimal Choice of Kernel Function Parameters. Expert Syst. Appl., 28(4): 603-614. 27. Nakama, T. 2009. Theoretical Analysis of Batch and On-line Training for Gradient Descent Learning in Neural Networks. Neurocomputing, 73(1): 151-159. 28. Nichter, S., and Goldmark, L. 2009. Small Firm Growth in Developing Countries. World Dev., 37(9): 1453-1464. 29. Nishantha, B. and Kawamura, Y. 2011. The Role of Human and Social Capital on Small Enterprise Growth: Evidence from Sri Lanka. Ryukoku J. Econ. Stud., 51(1): 69-89. 30. Paige, R. C. and Littrell, M. A. 2002. Craft Retailers’ Criteria for Success and Associated Business Strategies. J. Small Bus. Manage., 40(4): 314-331. 31. Pishbahar, E., Ghahremanzadeh, M., Ainollahi, M. and Ferdowsi, R. 2015. Factors Influencing Agricultural Credits Repayment Performance among Farmers in East Azarbaijan Province of Iran. Agr. Sci. Tech., 17(5): 1095-1101. 32. Van Praag, C. M. 2003. Business Survival and Success of Young Small Business Owners. Small Bus. Econ., 21(1): 1-17. 33. Reijonen, H. and Komppula, R., 2007. Perception of Success and Its Effect on Small Firm Performance. JSBED, 14(4): 689-701. 34. Van Rijn, F., Bulte, E. and Adekunle, A. 2012. Social Capital and Agricultural Innovation in Sub-Saharan Africa. Agric. Syst., 108: 112-122. 35. Safa, M., Samarasinghe, S. and Nejat, M., 2015. Prediction of Wheat Production Using Artificial Neural Networks and Investigating Indirect Factors Affecting It: Case Study in Canterbury Province, New Zealand. J. Agr. Sci. Tech., 17(4): 791-803. 36. Sandberg, K. W. 2003. An Exploratory Study of Women in Micro Enterprises: Gender-related Differences. J. Small Bus. Enterprise Dev., 10(4): 408-417. 37. Setsirichok, D., Piroonratana, T., Wongseree, W., Usavanarong, T., Paulkhaolarn, N., Kanjanakorn, C., Sirikong, M., Limwongse, C. and Chaiyaratana, N. 2012. Classification of Complete Blood Count and Haemoglobin Typing Data by a C4. 5 Decision Tree, a Naïve Bayes Classifier and a Multilayer Perceptron for Thalassaemia Screening. Biomed. Signal Process. Control, 7(2): 202-212. 38. Svendsen, G. L. H., Kjeldsen, C., and Noe, E. (2010). How do Private Entrepreneurs Transform local Social Capital into Economic Capital? Four Case Studies from Rural Denmark. J. Socec., 39(6): 631-644. 39. Tang, T. C. and Chi, L. C. 2005. Neural Networks Analysis in Business Failure Prediction of Chinese Importers: A Between-countries Approach. Expert Syst. Appl., 29(2): 244-255. 40. Unger, J.M., Rauch, A., Frese, M. and Rosenbusch, N. 2011. Human Capital and Entrepreneurial Success: A Meta-analytical Review. J. Bus. Venturing, 26(3): 341-358. 41. Wang, Y., Li, J., Gu, J., Zhou, Z. and Wang, Z.,2015. Artificial Neural Networks for Infectious Diarrhea Prediction Using Meteorological Factors in Shanghai (China). Appl. Soft. Comput., 35: 280-290. 42. Wilson, D. R. and Martinez, T. R. 2003. The General Inefficiency of Batch Training for Gradient Descent Learning. Neural Netw., 16(10): 1429-1451. 43. Wu, W., Wang, J., Cheng, M. and Li, Z. 2011. Convergence Analysis of Online Gradient Method for BP Neural Networks. Neural Netw., 24(1): 91-98. 44. Youn, H. and Gu, Z. 2010. Predicting Korean Lodging Firm Failures: An Artificial Neural Network Model along with a Logistic Regression Model. Int. J. Hosp. Manag., 29(1): 120-127. 45. Zhao, C., Gao, Y., He, J. and Lian, J. 2012. Recognition of Driving Postures by Multiwavelet Transform and Multilayer Perceptron Classifier. Eng. Appl. Artif. Intell., 25(8): 1677-1686. 46. Zhu, X., Li, J., Wu, D., Wang, H. and Liang, C. 2013. Balancing Accuracy, Complexity and Interpretability in Consumer Credit Decision Making: A C-TOPSIS Classification Approach. Knowl.-Based Syst., 52: 258-267.
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