Artefacts and Producers Mapping of Iran's Artificial Intelligence Ecosystem based on Transformational Levels
Subject Areas : ICT
hamed ojaghi
1
()
Iman Zohoorian Nadali
2
(Part AI Research Center)
Fatemeh Soleymani Roozbahani
3
(09120640468)
Keywords: Artificial intelligence, disruptive innovation, transformation, ecosystem, artifacts, Producers,
Abstract :
As an emerging technological field, artificial intelligence has received increasing attention from companies and governments. The development of artificial intelligence both at business and country levels depends on knowing the current situation. This paper identifies the artifacts and producers presented in this field and maps them to transformational levels. Products/services and producers are achieved through capabilities provided by artificial intelligence. Then, based on the classification methodology and meta-characteristics, the transformational levels of the artifacts of Iran's artificial intelligence ecosystem have been extracted. 562 products/services were identified, which were offered by 112 companies. Machine vision and natural language processing have been at the top of the technologies used, with 44 and 27 percent of the products allocated to them, respectively. Artifacts and producers were classified into seven transformative levels: individual, organization, industry, electronic chip/hardware, society, platform, code/algorithm/library, and infrastructure. Iran's artificial intelligence productions have not grown in a balanced way. The three levels of platform, code/algorithm/library, and infrastructure as the main generator of other artificial intelligence products/services have had the lowest amount of production. It is suggested that a specialized marketplace for the supply of artificial intelligence application programming interfaces should be put on the agenda to stimulate the formation of the ecosystem.
1. Makridakis, S., The forthcoming Artificial Intelligence (AI) revolution: Its impact on society and firms. Futures, 2017. 90: p. 46-60.
2. Dwivedi, Y.K., et al., Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy. International Journal of Information Management, 2021. 57: p. 101994.
3. Dan, Y. and H.C. Chieh. A reflective review of disruptive innovation theory. in PICMET'08-2008 Portland International Conference on Management of Engineering & Technology. 2008. IEEE.
4. Schumpeter, J.A., Capitalism, socialism, and democracy. 1942.
5. Freeman, C., Economics of industrial innovation. 1982: MIT Press, Cambridge, MA.
6. Bodrožić, Z. and P. S. Adler, Alternative futures for the digital transformation: A macro-level Schumpeterian perspective. Organization Science, 2022. 33(1): p. 105-125.
7. Bower, J.L. and C.M. Christensen, Disruptive technologies: catching the wave. 1995.
8. Christensen, C.M., R. Bohmer, and J. Kenagy, Will disruptive innovations cure health care? Harvard business review, 2000. 78(5): p. 102-112.
9. Guo, J., et al., Measurement framework for assessing disruptive innovations. Technological Forecasting and Social Change, 2019. 139: p. 250-265.
10. Corsi, S. and A. Di Minin, Disruptive innovation… in reverse: Adding a geographical dimension to disruptive innovation theory. Creativity and Innovation Management, 2014. 23(1): p. 76-90.
11. Christensen, C.M., et al., Disruptive innovation: An intellectual history and directions for future research. Journal of management studies, 2018. 55(7): p. 1043-1078.
12. King, A.A. and B. Baatartogtokh, How useful is the theory of disruptive innovation? MIT Sloan management review, 2015. 57(1): p. 77.
13. Utterback, J.M. and H.J. Acee, Disruptive technologies: An expanded view. International journal of innovation management, 2005. 9(01): p. 1-17.
14. Torugsa, N. and W. O’Donohue, Progress in innovation and knowledge management research: From incremental to transformative innovation. Journal of Business Research, 2016. 69(5): p. 1610-1614.
15. فیض, د., et al., نقش بازیوارسازی به عنوان استراتژی خلق رشد در مدل پذیرش نوآوری تحول آفرین در نرم افزارهای کاربردی پرداخت موبایلی (موردمطالعه: دانشجویان دانشگاه گیلان). فصلنامه مدیریت توسعه فناوری, 2019. 7(3): p. 63-90.
16. Sen, A., Totally radical: From transformative research to transformative innovation. Science and Public Policy, 2014. 41(3): p. 344-358.
17. Loorbach, D., et al., Transformative innovation and translocal diffusion. Environmental Innovation and Societal Transitions, 2020. 35: p. 251-260.
18. Ho, J.C., Disruptive innovation from the perspective of innovation diffusion theory. Technology Analysis & Strategic Management, 2022. 34(4): p. 363-376.
19. hakimjavadi, a., Journal of Information and Communication Technology, 1395. 24(7): p. 37-52.
20. Granstrand, O. and M. Holgersson, Innovation ecosystems: A conceptual review and a new definition. Technovation, 2020. 90-91: p. 102098.
21. Yin, R.K., Case study research and applications. 2018: Sage.
22. Nickerson, R.C., U. Varshney, and J. Muntermann, A method for taxonomy development and its application in information systems. European Journal of Information Systems, 2013. 22(3): p. 336-359.
23. DIN, D., German Standardization Roadmap on Artificial Intelligence, DIN/DKE, Editor. 2020: Berlin/Frankfurt
24. Gartner. Hype Cycle for Artificial Intelligence, 2020. 2020 27 July 2020; Available from: https://www.gartner.com/smarterwithgartner/2-megatrends-dominate-the-gartner-hype-cycle-for-artificial-intelligence-2020.
25. Ernst, E. and S. Mishra, AI Efficiency Index: Identifying Regulatory and Policy Constraints for Resilient National AI Ecosystems. Available at SSRN 3800783, 2021.
26. Kumar, A., et al., Sketching an ai marketplace: Tech, economic, and regulatory aspects. IEEE Access, 2021. 9: p. 13761-13774.
27. Zhou, Z., et al., Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing. Proceedings of the IEEE, 2019. 107(8): p. 1738-1762.
