The Economic Factors Affecting Internet Diffusion in Iran Using the Fuzzy Regression

Document Type : Research Paper

Authors

1 Professor of Economics, University of Tabriz

2 Ph.D. Student in Economics, University of Tabriz

Abstract

Internet by creating new conditions and opportunities for economic growth, allows the less developed countries to reduce their deep economic gap with developed countries. The purpose of this study is to investigate the effects of economic factors on the internet diffusion in Iran over the period of 1994-2015. Using a fuzzy regression, this study is going to estimate the type of relationship between variables which estimates a range of possible values of parameters. Results of the model show that, per capita income, urbanization rate, openness, human capital, and share of the service sector in GDP have a positive effect on the internet diffusion. But the impact of agriculture sector’s share in GDP is negative on the internet diffusion.

Keywords

Main Subjects


  1.  

    1. Andres, L., Cuberes, D., Diouf, M. & Serebrisky, T. (2010). The diffusion of the internet: A cross-country analysis. Telecommunications Policy, 34(5-6), 323–340.
    2. Arellno, M. & Bond, S. (1991). Same tests of specification for panel data: monte carlo evidence and an application to employment equations. The Review of Economic Studies, 58(2), 277-297.
    3. Baliamoune, M. N. (2002). The new economy and developing countries: Assessing the role of ICT diffusion. World institute for development economics (UNU-WIDER), 77(2002), 1-24.
    4. Dimitrova, V. (2003). An exploratory model of inter-country internet diffusion. Telecommunications Policy, 27(2), 237–252.
    5. Birba, O. & Diagne, A. (2012). Determinants of adoption of internet in Africa: case of 17 sub-Saharan countries. Structural Change and Economic Dynamics, 23(3), 463– 472.  
    6. Brown, L.A. (1981). Innovation diffusion: A new perspective. New York, Methuen.
    7. Caselli, F. & Coleman, W.J.II. (2001). Cross-country technology diffusion: the case of computers. American Economic Review, 91(2), 328–335.
    8. Chinn, M.D. & Fairlie, R.W. (2007). The determinants of the global digital divide: A cross-country analysis of computer and internet penetration. OxfordEconomic Papers, 59(1), 16–44.
    9. Corehpzan Dezfooli, A. (2005). Theoretical principles of fuzzy sets and its applications in modeling of water engineering. Tehran, Publications Jahad Daneshgahi (SID.) (In Persian).
    10. Davis, F.D., Bagozzi, R.P. & Warshaw, P.R. (1986). User acceptance of computer technology: A comparison of two theoretical models. Management Science, 35(8), 982-1003.
    11. Estache, A., Manacorda, M. & Valletti, T.M. (2002). Telecommunications reform, access regulation and internet adoption in Latin America. EconomiesPublicans, 2(4), 153–217.
    12. Guillen, M.F. & Suarez, S.L. (2005). Explaining the global digital divide: economic. Political and Sociological Drivers of Cross-National Internet Use. Social Forces, 84(2), 681–708.
    13. Guangchao, Ch.F. (2015). Factors affecting internet diffusion in china: A multivariate time series analysis. Journal of Telematics and Informatics, 32(4), 681–693.
    14. Guangchao, Ch.F. (2015). Determinants of internet diffusion: A focus on china. Technological Forecasting & Social Change, 100(3), 176-186.
    15. Hargittai, E. (1999). Weaving the western web: explaining difference in internet connectivity among OECD countries. Telecommunications Policy, 23, 701–718.
    16. International Telecommunication Union (ITU). (2015). Retrieved from/http: www.itu.int/, accessed online 7 July 2015.
    17. Jabbari, M., Jabbari, G., Sharifi, F. & Momen, M. (2012). Fuzzy linear regression and its application in economics research. First International Conference on Econometrics of Methods and Applications, Islamic Azad University, Sanandaj Branch (In Persian).
    18. Kazemi, M. (2014). The effect of income inequality on global digital divide. Master's thesis, Faculty of Economics, Management and Business of Tabriz University (In Persian).
    19. Kiiski, S. & Pohjola, M. (2002). Cross country diffusion of the internet. Information Economics and Policy, 14(2), 297–310.
    20. Khodei, A. (2009). Fuzzy linear regression and its application in the study of social sciences. Journal of social studies, 4(8), 82-99(In Persian).
    21. Kim, S. (2011). The diffusion of the internet: trend and causes. Social Science Research, 40, 602–613
    22. Lawson, R., Alcock, C. & Cooper, J. (2002). Diffusion of electronic commerce in small and medium enterprises. Australasian Journal of Information Systems, 9(2), 123-134.
    23. Lee, H., O'Keefe R.M. & Yun, K. (2003). The growth of broadband and electronic commerce in South Korea: contributing factors. Information society, 19(1), 81-93.
    24. Li, R. & Shiu, A. (2012). Internet diffusion in china: A dynamic panel data analysis. Telecommunications Policy, 36(10-11), 872–887.
    25. Lina, M.S. & Wu, F.S. (2013). Identifying the determinants of broadband adoption by diffusion stage in OECD countries. Telecommunications Policy, 37, 241–251.
    26. Lotfizadeh, A. (1965). Fuzzy sets. Information and control, 8(3), 338-353.
    27. McCoy, S., Everard, A. & Jones, B. (2005). An  examination  of  the  technology  acceptance model  in  Uruguay  and  the  US:  A  Focus  on  Culture. Journal of Global Information Technology Management, 8(2), 27-45.
    28. McCoy, S.S., Cha, H. & Durcikova, A. (2012). Modeling internet diffusion in developing countries. Australasian Journal of Information Systems, 17(2), 47-59.
    29.  Miner, L. (2015). The unintended consequences of internet diffusion: evidence from Malaysia. Journal of Public Economics, 132, 66–78.
    30. Moutafides, G.M. & Economidesn, A.A. (2011). Demand for broadband access in Greece. Telematics and Informatics, 28, 125-141.
    31. Nazari, M. & Tabatabai Kljahy, V. (2015). Estimating of the monetary value in the Internet. Journal of Information Technology Management, 7(1), 185-198 (In Persian).
    32. Oyeyinka, B.O., & Lal, K. (2005). Internet diffusion in sub-Saharan Africa:  A cross-country analysis. Telecommunications Policy, 29, 507–527.
    33. Pohjola, M. (2003). The adoption and diffusion of ICT across countries: patterns and determinants. New Economy Handbook, Academic Press, 77–100.
    34. Penard, T., Poussing, N., Mukoko, B. & Tamokwe Piaptie, G.B. (2015). Internet adoption and usage patterns in Africa: evidence from Cameroon. Technology in Society, 42(5), 71-80.
    35. Penard, T., Poussing, N., Yebe, G.Z. & Nsiella. P. (2012). Centre de recherché en economies and management. Working Paper, University of Caen and Rennes, Available from: Nicolas Pausing.
    36. Press, L., Burkhart, G., Foster, W., Goodman, S., Wolcott, P. & Woodard, J. (1998). An internet diffusion framework. Communications of the ACM, 41(10), 21–26.
    37. Rogers, E.M.  (1995). Diffusion of innovations: Modifications of a model for telecommunications. New York: The Free Press, 25-38.
    38. Rogers, E.M. (2003). Diffusion of innovations. New York: The Free Press5th Edition.
    39. Tanaka, H. (1987). Fuzzy data analysis by possibility linear models. Fuzzy Sets and Systems, 24(3), 363- 375.
    40. Sabbagh Kermani, M. & Najafi, N. (2005). Estimating the function of internet demand: A case study city of Tehran. Journal of Commercial Research, 9(36), 53–74 In Persian).
    41. Salmani, B., Mohammadzadeh, P. & Zolghadr, H. (2015). The effect of economic factors on Internet penetration in developing countries. Quarterly Journal of Applied Economics Theory, 2(2), 102-81 (In Persian).
    42. Salman, A. & Hasim, M.S. (2011). Internet usage in a Malaysian sub-urban community: A study of diffusion of ICT innovation. The Innovation Journal: The Public Sector Innovation Journal, 16(2), 88-106.
    43. Sobhani, H. & Mohammadi, E. (2005). Estimating impact of economic variables on the information technology development. Journal of Economics Research, 16(71), 1-38 (In Persian).
    44. Taheri, M. & Masheinchi, M. (2008). An introduction to probability and fuzzy statistics. Kerman, University of shaheid Bahonar publishing (In Persian).
    45. Tanaka, H. & Ishibuchi, H. (1992). Possibility regression analysis based on linear programming. Journal of European research, 2(3), 44-60.
    46. World Bank. (2016). World development indicators. Available at: www.worldbank. Org, Accessed online November.
    47. Tsai, J-M. & Hung, S-W. (2014). A novel model of technology diffusion: system dynamics perspective for cloud computing. Journal of Engineering and Technology Management (J. Eng. Technol. Manage), 33, 47–62.
    48. Wunnava, P.V. & Leiter, D.B. (2009). Determinants of inter-country internet diffusion rates.American Journal of Economics and Sociology, 68(2), 413–426.