تحلیل عوامل مؤثر بر انتشار فناوری کدهای دستوری تلفن ‌همراه در ایران

نوع مقاله : مقاله پژوهشی

نویسندگان

1 استادیار، پژوهشکده مطالعات بنیادین علم و فناوری، دانشگاه شهید بهشتی، تهران، ایران.

2 کارشناس ارشد مدیریت تکنولوژی، دانشگاه شهید بهشتی، تهران، ایران.

چکیده

هر فناوری مسیر خاصی در فرآیند پذیرش و انتشار طی می‌‌کند. ظهور و بروز عوامل بازدارنده و یا تسهیل‌کننده در بستر فنی-اجتماعی فناوری، تعیین‌کننده مسیر و سرعت انتشار فناوری است. در این راستا هدف از این پژوهش، تحلیل منحنی انتشار فناوری کدهای دستوری تلفن همراه (USSD) در ایران است. قلمرو موضوعی و زمانی این تحقیق شامل مشترکین تلفن همراه بر بستر فناوری USSD از سال 1389 تا 1398 می‌باشد. این مقاله با استفاده از چارچوب انتشار فناوری راجرز و داده‌های تاریخی مشترکین فناوری USSD در اپراتور همراه اول، منحنی‌های انتشار کاربردهای مختلف این فناوری را ترسیم و تحلیل می‌نماید. برای فهم و توضیح نمودارها و عوامل مؤثر بر انتشار در زمینه ایران، با 14 خبره و مطلع این حوزه در نهادهای تنظیم‌گر، سیاست‌گذار و اپراتور تلفن همراه مصاحبه‌ نیمه ساختاریافته صورت گرفت. بر اساس یافته‌های این پژوهش، USSD در خدمات اپراتوری و خدمات بانکی بیشترین کاربرد را داشته و در سایر حوزه‌ها کاربرد آن محدود بوده است. یافته‌‌ها همچنین نشان می‌دهد مزیت نسبی،پیچیدگی و آزمون‌‌پذیری بیشترین تأثیر را در تقویت انتشار USSD دارند و عوامل قیمت، مقررات و تعامل بازیگران، بیشترین تأثیر را بر محدود شدن انتشار این فناوری داشته‌اند. یافته‌های این مقاله دلالت‌های سیاستی و مدیریتی برای تنظیم‌گری و فعالیت‌های کسب‌وکاری در حوزه فناوری‌های مالی و خصوصاً USSD ارائه نموده است.

کلیدواژه‌ها

موضوعات


عنوان مقاله [English]

Investigating Diffusion of USSD Technology in Iran

نویسندگان [English]

  • Kiarash Fartash 1
  • Tooba Baramaki 2
  • Mohammad sadegh khayyatian 1
  • Nima Rahimi salekdeh 2
1 Faculty Member, Institute for Science and Technology Studies, Shahid Beheshti University, Tehran, Iran
2 Master Student of Technology Management, Shahid Beheshti University, Tehran, Iran
چکیده [English]

A given technology goes through a unique path in the process of its diffusion. Factors residing in a socio-technical environment either inhibit or strengthen the path and speed of technology diffusion. In this regard, the aim of this research analyzes the diffusion curve of unstructured supplementary service data (USSD) technology in Iran. The scope of this paper includes mobile phone subscribers who have adopted USSD technology from 2010 to 2019. By adopting Roger’s technology diffusion framework, this article depicts and analyzes the diffusion curves of different applications of USSD technology through the historical data of USSD technology subscribers in the Hamrahe Avval operator in Iran. To better understand and explain the diffusion and the influential factors in the diffusion in the context of Iran, 14 semi-structured interviews were conducted with USSD experts affiliated to the regulatory bodies, policymakers, and mobile phone operators. Our findings reveal that USSD was mostly used in the operator and banking services while its adoption in other survives was been limited. We also found that relative advantage, complexity and Trialability have the highest strengthening effect on the diffusion of USSD; in the same vein price, regulations, and the interaction of actors are the key barriers to the diffusion of USSD in Iran. The findings of this paper provide policy and management implications for regulation and business activities of financial technologies and in particular USSD. To this end, the significant influence of regulation needs to be taken into account which could be a barrier to financial technologies diffusion as the case of USSD in Iran indicates.

کلیدواژه‌ها [English]

  • Unstructured Supplementary Service Data
  • Financial Technology
  • Technology Diffusion
  • Technology Acceptance
  • USSD
[1] Rogers, E. (1962). Diffusion of Innovations. The Free Press. New York, United States.
[2] Mansfield, E. (1968). Industrial Research and Technological Innovation: An Econometric Analysis. Norton, New York, United States.
[3] Oyinloye, O., Thompson, A., & Oluwaseyi, A. (2020). Implementating a Dynamic Data Collector Using Data Collector Using Unstructured supplementary service data (USSD). International Journal of Computer Science and Information Security, 18(1), 95-109.
[4] Sanganagouda, J. (2011). USSD-A Potential Communication Technology that can Ouster SMS Dependency. International Journal of Research and Reviews in Computer Science, 2(2), 295.
[5] Perlman, L. (2017). Technology inequality: Opportunities and challenges for mobile financial services. Columbia Business School Research Paper, No. 17-49. doi: http://dx.doi.org/10.2139/ssrn.2957143.
[6] Thusi, p., & Maduku, D. (2020). South African millennials’ acceptance and use of retail mobile banking apps: An integrated perspective. Computers in Human Behavior. doi: https://doi.org/10.1016/j.chb.2020.106405.
[7] Fartash, K., Khayatian, M., Rahimi Salekdeh, N., & Eskandari, F. (2022). Institutional Analysis of USSD Technology Development and Application in Iran. Improvment management, 16(1), 1-29. doi: 10.22034/jmi.2022.282858.2544.  {In Persion}.
[8] Peres, R., Muller, E., & Mahajan, V. (2010). Innovation diffusion and new product growth models: A critical review and research directions. International journal of research in marketing, 91–106.­ doi: https://doi.org/10.1016/j.ijresmar.2009.12.012.
[9] Comin, D., & Mestieri, M. (2014). Technology diffusion: Measurement, causes, and consequences. Handbook of economic growth, 2, 565-622. doi: https://doi.org/10.1016/B978-0-444-53540-5.00002-1
[10] Zamora, J. (2016). Mobile as a Means to Electrification in Uganda. In Proceedings of the First African Conference on Human Computer Interaction, 187-191. doi: https://doi.org/10.1145/2998581.2998603.
[11] Ali, A. (2020). Assessing the Impact of IT Governance Mechanisms, Service Innovation Adoption and Quality on Performance, Customer Satisfaction and Accessibility Case: Nigerian Mobile Banking Services". PhD diss., Seoul National University.
[12] Lakshmi, K., Gupta, H., & Ranjan, J. (2017). USSD—Architecture analysis, security threats, issues and enhancements. 2017 international conference on infocom technologies and unmanned systems, 798-802. doi: 10.1109/ICTUS.2017.8286115.
[13] Bonus, H. (1973). Quasi-Engel curves, diffusion and the ownership of major consumer durables. Journal of Political Economy, 81, 655– 677. https://www.jstor.org/stable/1831030
[14] Russell, T. (1980). Comments on ‘The relationship between diffusion rates, experience curves and demand elasticities for consumer durable technological innovations. Journal of Business, 55(3), 69–73. https://www.jstor.org/stable/2352213
[15] Liebermann, Y., & Paroush, J. (1982). Economic aspects of diffusion models. Economics and Business, 34(1), 95–100. doi: https://doi.org/10.1016/0148-6195(82)90021-2 .
[16] Baptista, R. (2000). The diffusion of process innovations: A selective review. International Journal of Industrial Organization, 18, 515– 535. doi: https://doi.org/10.1016/S0167-7187(99)00045-4.
[17] Hoseinpour, B., Jabbari, A., & Alipour, H. (2023). Evaluation and Assessment of Drip Irrigation Acceptance and Development Challenges in Urmia Apple Orchards Using Rogers’ Diffusion of Innovation model. Water research in agriculture, 37(1), 49-72. doi: https://doi.org/10.22092/jwra.2023.360564.955.
[18] Rogers, E. (2010). Diffusion of innovations. Simon and Schuster. United States.
[19] Bharadwaj, S., & Deka, S. (2021). Behavioural intention towards investment in cryptocurrency: an integration of Rogers’ diffusion of innovation theory and the technology acceptance model. Forum Scientiae Oeconomia, 9(4), 137-159. doi: https://doi.org/10.23762/FSO_VOL9_NO4_7.
[20] Lee, s., & Kim, J. K. (2007). Factors affecting the implementation success of Internet-based information systems. Computers in Human Behavior, 23(4), 1853–1880. doi: https://doi.org/10.1016/j.chb.2005.12.001.
[21] Al-Rahmi, W. M., Yahaya, N., Aldraiweesh, A. A., Alamri, M. M., Aljarboa, N. A., Alturki, U., & Aljeraiwi, A. A. (2019). Integrating technology acceptance model with innovation diffusion theory: An empirical investigation on Students’ Intention to Use E-Learning Systems. IEEE, 7, 26797-26809. doi: 10.1109/ACCESS.2019.2899368.
[22] Rosenberg, N. (1972). Factors affecting the diffusion of technology. The University of Wisconsin.
[23] Wu, X., & Subramaniam, C. (2011). Understanding and predicting radio frequency identification (RFID) adoption in supply chains. Organizational Computing and Electronic Commerce, 21(4), 348-367. doi: https://doi.org/10.1080/10919392.2011.614203.
[24] Huh, J. H., Kim, T. T., & Law, R. (2009). A comparison of competing theoretical models for understanding acceptance behavior of information systems in upscale hotels. International Journal of Hospitality Management, 28(1), 121–134. doi: https://doi.org/10.1016/j.ijhm.2008.06.004.
[25] Faghih, H., Ghazinoory, S., & Elyasi, M. (2020). A Manual for Technology Acquisition Method Selection: The Three-dimensional Model of the Interaction of Factors Related to Owner, Receiver and the Nature of Technology. Science & Technology Policy, 13(3), 83-100. doi: https://doi.org/10.22034/jstp.2020.12.3.1263. {In Persion}.
[26] Chang, H. H., Wang, H.-W., & Kao, T. W. (2010). The determinants of long-term relationship on inter-organizational systems performance. Journal of Business & Industrial Marketing, 25(2), 106–118. doi: https://doi.org/10.1108/08858621011017732.
[27] Kelly, D., Feller, J., & Finnegan, P. (2006). Complex Network-Based Information Systems (CNIS) Standards: Toward an Adoption Model. In The transfer and diffusion of information technology for organizational resilience, 3-20.
[28] Lou, A., & Li, E. (2017). Integrating innovation diffusion theory and the technology acceptance model: The adoption of blockchain technology from business managers’ perspective. ICEB 2017 Proceedings, Dubai, UAE. https://aisel.aisnet.org/iceb2017/44/.
[29] Min, S., So, K., & Jeong, M. (2019). Consumer adoption of the Uber mobile application: Insights from diffusion of innovation theory and technology acceptance model. Travel & Tourism Marketing, 36(7), 770–783. doi: https://doi.org/10.1080/10548408.2018.1507866.
[30] Borg, F., & Persson, M. (2010). Assessing Factors influencing the Diffusion of Mobile Banking in South Africa –A case study on the company Wizzit. Essay, School of Business, Economics and Law University of Gothenburg.https://gupea.ub.gu.se/handle/2077/21967.
[31] Andrews, D., Nicoletti, G., & Timiliotis, C. (2018). Digital technology diffusion: a matter of capabilities, incentives or both. OECD Economics Department Working Papers. doi: https://doi.org/10.1787/18151973.
[32] Fartash, K., Baramaki, T., Saremi, M., & Sadabadi, A. (2023). The Growth Challenges of Pioneer Knowledge-based Firms of ICT. Science & Technology Policy, 15(3), 41-54. doi: 10.22034/jstp.2022.13957.  {In Persion}.
[33] Comin, D., & Hobijn, B. (2004). Cross-country technology adoption: making the theories face the facts. Journal of monetary Economics, 1 (39-83), 51. doi: https://doi.org/10.1016/j.jmoneco.2003.07.003.
[34] Ruholahi, M. (2011). Identification and prioritization of factors affecting the diffusion of technology at the level of industrial clusters (a case study of cluster development projects supported by the Organization of Small Industries and Industrial Towns of Iran). Master's Thesis of Executive Management, Faculty of Economics, Management and Administrative Sciences, Semnan University, Semnan, Iran {In Persion}.
[35] Waarts, E., & van Everdingen, Y. (2005). The Influence of National Culture on the Adoption Status of Innovations: An Empirical Study of Firms Across Europe. European Management Journal, 23(6), 601–610. doi: https://doi.org/10.1016/j.emj.2005.10.007.
[36] Fisher, J. C., & Pry, R. H. (1971). A Simple Substitution Model of Technological Change. Technological Forecasting and Social Change, 3, 75-88. doi: https://doi.org/10.1016/S0040-1625(71)80005-7.
[37] Norton, J., & Bass, F. (1987). A Diffusion Theory Model of Adoption and Substitution for Successive Generations of High Technology Products. Management Science, 1069–1086. doi: https://doi.org/10.1287/mnsc.33.9.1069.
[38] Sadler, G. R., Lee, H. C., Lim, R. S., & Fullerton, J. (2010). Recruitment of hard-to-reach population subgroups via adaptations of the snowball sampling strategy. Nursing & health sciences, 12(3), 369–374. doi: https://doi.org/10.1111/j.1442-2018.2010.00541.x.
[39] Padgett, D. (2016). Qualitative methods in social work research. Sage publications, 36. United States.
[40] Flick, U. (2018). Designing qualitative research. Sage publications. United States.