Ecological Analysis of Knowledge Development in the Economy: Iranian Lessons from Global Data

Document Type : Research paper

Author

Associate Professor of Higher Education Economics, Institute for Higher Education Research and Planning,Tehrn.Iran

Abstract

Knowledge development, as a major driver of development at the individual, organizational, and economic levels, has been examined from various psychological, epistemological, managerial, economic, and sociological perspectives. In economics and management, endogenous growth theory, evolutionary economics, knowledge economy theory, knowledge-based economic theory, and knowledge-based development theory collectively emphasize that economic development and competitiveness depend directly on knowledge development. According to this perspective, economies seeking to improve their level of development and competitiveness must be capable of developing the knowledge they require in a dynamic, efficient, and effective manner.
The motivation for this study arises from both a scientific and a policy-related problem concerning knowledge development in Iran. Existing studies have largely neglected ecological factors that may influence knowledge development, creating a significant research gap. Moreover, the application of scientific findings in policymaking for knowledge development in developing countries, including Iran, has not been sufficiently effective. A review of Iran’s economic development trajectory compared with developed economies indicates that over the past four decades Iran has not succeeded in becoming an efficiency-based economy and has remained primarily dependent on material resources. This raises a critical question: why has Iran’s economy failed to maintain and improve its competitiveness and achieve sustainable economic development? While knowledge development appears to be the obvious solution, the Iranian economy itself faces substantial weaknesses in this area. Recent global competitiveness, innovation, and knowledge indices indicate that, compared with at least 75 developed and developing economies, Iran lacks the necessary dynamism, efficiency, and effectiveness required for knowledge development.
Accordingly, this study aims to theoretically and empirically examine the effects of ecological factors—particularly the performance and conditions of knowledge ecosystems—on macro-level knowledge development. The analysis draws upon the theoretical foundations of knowledge ecology, knowledge ecosystems, and the national knowledge ecosystem approach.
To empirically investigate the effects of ecological factors on knowledge development, the study employs descriptive and normative approaches, a quantitative documentary method, global secondary data derived from national knowledge ecosystems, and structural equation modeling (SEM). The model consists of one latent dependent variable with four observable indicators, three latent mediating variables with nine observable indicators, and five latent independent variables with fourteen observable indicators. Data were extracted from the QS World University Rankings, the Human Freedom Index, the Economic Freedom Index, and the Global Innovation Index 2025 reports.
The findings reveal a strong influence of ecological factors—including personal freedom, economic freedom, political environment, regulatory environment, and soft infrastructure—on knowledge development in the economy. These factors contribute to knowledge development by shaping the behavior and performance of cognitive agents (such as firms) and metacognitive agents (such as universities), as well as their interactions within the ecosystem. Statistical analyses further indicate that although ecological factors improve the national performance and international interactions of universities, the national performance of universities does not significantly contribute to knowledge development in the economy, while international interactions of universities may even weaken it.
Three major conclusions emerge from these findings. First, the knowledge developed within universities differs fundamentally from the knowledge developed in the economy. University knowledge is primarily subjective and explicit, whereas economic knowledge is more objective and tacit. Universities can therefore contribute more effectively to economic knowledge development when they transform subjective and explicit knowledge into objective and tacit knowledge. Second, ecological factors influence knowledge development through interactions among cognitive and metacognitive agents. Universities contribute to knowledge development mainly through interaction with other agents, and because the role of other actors is not fully represented in the model, the actual contribution of universities may not be fully captured. Third, because universities primarily generate global public explicit knowledge, such knowledge may leave the national ecosystem through international interactions before being transformed into objective economic knowledge within the domestic economy. Consequently, universities need both to develop objective knowledge and to transform subjective and explicit knowledge into objective and tacit forms.
The study therefore recommends gradual improvement of ecological factors—including personal freedom, economic freedom, political and regulatory environments, and soft infrastructure—through a transition toward knowledge-based and wise governance. It also suggests that universities should expand beyond their traditional missions and actively engage in the creation, absorption, diffusion, and transformation of objective knowledge in order to contribute more effectively to knowledge development in the economy.

Keywords

Main Subjects

References

Alkistis, E. G. (2024). Ecology- the Stanford Encyclopedia of philosophy (Spring Ed.). Retrieved from: https://plato.stanford.edu/archives/spr2024/entries/ecology/
Bhatt G. D. (2000). Organizing knowledge in the knowledge development cycle. Journal of Knowledge Management, 4(1), 15-26.
Blute, M. (2002). The evolutionary ecology of science. Journal Of Memetics - Evolutionary Models of Information Transmission, 7.
Boschma, R., Heimeriks, G. & Balland, P. A. (2014). Scientific knowledge dynamics and relatedness in biotech cities. Reseach Policy, 43, 107-114. DOI: 10.1016/j.respol.2013.07.009
Bowen, M. (1985). The ecology of knowledge: Linking the natural and social sciences. Geoform, 16(2), 213-225. DOI: 10.1016/0016-7185(85)90030-2.
Bradie, M. (1986). Assessing evolutionary epistemology. Biology and Philosophy, 1(4), 401-459.  DOI: 10.1007/BF00140962
Bradie, M. (2011). Evolutionary epistemology, In S. Bernecker, & D. Pritchard (Eds.), The Routledge companion to epistemology (848-858). Abingdon: Routledge.  DOI: 10.4324/9780203839065
Bronfenbrenner, A. U. (1979). The ecology of human development: Experiments by nature and design. Cambridge: Harvard University Press. DOI: 10.2307/j.ctv26071r6
Bruxvoort, D. (2025). Knowledge management and information science. In B. V. Elsevier (Ed.), Reference module in Social Sciences, Encyclopedia of Libraries, Librarianship,and Information Science. London: Elsevier. DOI: 10.1016/B978-0-323-95689-5.00178-4
Budtz, D. (2023). Rethinking evaluation of complex ecosystems of science for policy. Blog post | 04 Dec 2023.
Byerly, J. R., & Roger, P. A. (1995). The changing ecology of united states science. Science, 269(5230), 1531-1532.
Campbell, D. T. (1960). Blind variation and selective retentions in creative thought as in other knowledge processes. Psychological Review, 67(6), 380-400.  DOI:10.1037/h0040373
Carrillo, F. J. (2015). Knowledge-based development as a new economic culture. Journal of Open Innovation: Technology, Market, and Complexity, 1(2), 1-17. DOI: 10.1186/s40852-015-0017-5
Chinazzi, M., Gonçalves, B., Zhang, Q., & Vespignani, A. (2019). Mapping the physics research space: A machine learning approach. EPJ Data Science, 8(1), 33-47.  DOI: 10.1140/epjds/s13688-019-0210-z
Cimini, G., Gabrielli, A. & Labini, F. S. (2014). The scientific competitiveness of nations. PLoS ONE, 9(12), e113470. DOI: 10.1371/journal.pone.0113470
Crawford, M. (2020). Ecological systems theory: Exploring the development of the theoretical framework as conceived by Bronfenbrenner. Journal of Public Health Issues and Practices, 4(2), 170. doi: https://doi.org/10.33790/jphip1100170
Cummings, S., Powell, M., & Pels, J. (2011). Development knowledge ecology: Metaphors and meanings. Knowledge Management for Development Journal, 7(1), 125-135. DOI: 10.1080/19474199.2011.593878
 Devi T. V. Geetha (2019). Understanding human ecology: Knowledge, ethics and politics. New Delhi: Routledge India.  DOI: 10.4324/9780429029783
Ellison Peter, T. (2017). Reproductive ecology and human evolution. Abingdon: Taylor & Francis.  DOI: 10.4324/9781315128467
Entezari, Y. (2014). Analysis of the impact of economic freedom components on university performance components. Science and Technology Policy, 17(2), 89-106. (Persian) DOI: 10.22034/jstp.2024.11743.1800
Entezari, Y., & Largani, H. M. (2024). Ecological analysis of interactions and international performance of universities. Journal of Management and planning in Educational Systems, 17(1), 289-314. (Persian) DOI: 10.48308/mpes.2024.235339.1453
Finke, P. (2014). The ecology of science and its consequences for the ecology of language. Language Sciences, 41(Part A), 71-82. DOI: 10.1016/j.langsci.2013.08.008
Foley, R. (1988). Another unique species. Patterns in human evolutionary ecology. London: Longman.
Fox, C. W, Roff, D. A., & Fairbairn, D. J. (2020). Evolutionary ecology: Concepts and case studies get access arrow. Oxford: Oxford University Press. DOI: 10.1093/oso/9780195131543.001.0001.
Fox, J. (2024). The ecology of ecologists: Harnessing diverse approaches for a stronger science. Chicago: University Chicago Press.
Fukuda, K. (2020). Science, technology and innovation ecosystem transformation toward society 5.0. International Journal of Production Economics, 220(2020), 107460.  DOI: 10.1016/j.ijpe.2019.07.033
GCI (2024): Global competitiveness index,2024, IMD.
GII (2025): Global innovation index,2024, WIPO.
GKI (2024): Global knowledge index, 2024, United Nations Development Programme
Guevara, M. R., Hartmann, D., Aristarán, M., Mendoza, M. & Hidalgo, C. A. (2016). The research space: Using career paths to predict the evolution of the research output of individuals, institutions, and nations. Scientometrics, 109, 1695-1709.  DOI: 10.1007/s11192-016-2125-9
Hayashi, T. (1993). Ecology of technology: A perspective. AI & Society, 7, 109-116. DOI: 10.1007/BF01908608
Heilbron, J. L. (2003). The Oxford companion to the history of modern science. New York: Oxford University Press.
Hendry, A. P. (2016). Eco-evolutionary dynamics, Princeton and Oxford. Retrieved from: https://www.jstor.org/stable/j.ctt1q1xr0c
Hill, S. B., Wilson, S., & Watson, K. (2004). Learning ecology. A new approach to learning and transforming ecological consciousness. In: O’Sullivan, E.V., Taylor, M.M. (Eds.), Learning toward an ecological consciousness: Selected transformative practices. New York: Palgrave Macmillan. DOI: 10.1007/978-1-349-73178-74.
Ikazaki, D., & Ikeshita, K. (2021). Globalization, population, and regional growth in the knowledge-based economy. Singapore: Springer Nature Singapore. DOI: 10.1007/978-981-16-0885-8
King, D. A. (2004). The scientific impact of nations. Nature, 430, 311-316. DOI: 10.1038/430311a
Koster, F. (2023). Organizations in the knowledge economy. An investigation of knowledge-intensive work practices across 28 European countries. Journal of Advances in Management Research, 20(1), 140-159. DOI: 10.1108/JAMR-05-2021-0176
Kozlowski, J., Radosevic, S. & Ircha, D. (1999). History matters: The inherited disciplinary structure of the post-communist science in countries of central and eastern Europe and its restructuring. Scientometrics, 45, 137-166.  DOI: 10.1007/BF02458473
Kumar, R. R., Stauvermann, P. J. & Patel, A. (2016). Exploring the link between research and economic growth: An empirical study of China and USA. Quality and Quantity, 50, 1073-1091.  DOI: 10.1007/s11135-015-0191-3
Lave, J., & Wenger, E. (1991). Situated development: Legitimate peripheral participation. Cambridge: Cambridge University Press.
Lee, L. C., Lin, P. H., Chuang, Y. W. & Lee, Y. Y. (2011). Research output and economic productivity: a Granger causality test. Scientometrics, 89, 465-478. DOI: 10.1007/s11192-011-0476-9
Leydesdorff, L., Perevodchikov, E., & Uvarov, A. (2014). Measuring triple-helix synergy in the Russian innovation systems at regional, provincial, and national levels. Journal of the Association for Information Science and Technology, 66(6), 1229-1238.  DOI: 10.1002/asi.23258
Livingstone, D. N. (2010). Putting science in its place: Geographies of scientific knowledge. Chicago: University Chicago Press.
Lundvall, B. A., & Johnson, B. (1994). The learning economy. Journal of Industry Studies, 1(2), 23-42. DOI: 10.1080/13662719400000002
Marten ByGerald, G. (2010). Human ecology: Basic concepts for sustainable development. Abingdon: Routledge. DOI: 10.4324/9781849776028.
May, R. M. (1997). The scientific wealth of nations. Science, 275, 793-796.
Mazzoleni, R., & Nelson, R. R. (2007). Public research institutions and economic catch-up Reseach. Policy, 36(2007), 1512-1528. DOI: 10.1016/j.respol.2007.06.007.
Miao, L., Murray, D., Jung, W. S. , Larivière, V., Sugimoto, C. R., Ahn, Y. Y. (2022). The latent structure of global scientific development. Nature Human Behaviour, 6(9), 1206-1217. DOI: 10.48550/arXiv.2104.10812
Öberg, C., & Lundberg, H. (2022). Mechanisms of knowledge development in a knowledge ecosystem. Journal of Knowledge Management, 26(11), 293-307. DOI: 10.1108/JKM-11-2021-0814
OECD. (2000). Knowledge management in the learning society. Paris: OECD Publishing.
Phillips, F., Yu, Ch. Y. Hameed, T., El Akhdary, M. A. (2017). The knowledge society's origins and current trajectory. International Journal of Innovation Studies, 1(3), 175-191. DOI:10.1016/j.ijis.2017.08.001
Popper, K. (1985). Evolutionary epistemology. In G. Tarozzi, & A. Van der Merwe (Eds.), Open questions in quantum physics (pp. 395- 413). New York City: Springer Nature Link. DOI: 10.1007/978-94-009-5245-4_27
Popper, K. (1994). All life is problem solving. (P. Camiller, English Trans.). Abingdon: Routledge. DOI: 10.4324/9780203431900
Root, H. L. (2023). The ecology of innovation: The evolution of a research paradigm. In: Z. J. Acs, E. Lafuente, & L. Szerb (Eds.), The entrepreneurial ecosystem. Palgrave Studies in Entrepreneurship and Society. London: Palgrave Macmillan. DOI: 10.1007/978-3-031-25931-9_12
Roy, R. (2025). Governance of knowledge development in a public-private partnership: NASA’s efforts to design the Space Shuttle. Research Policy, 54(2), 105151. DOI: 10.1016/j.respol.2024.105151
Salter Ammon, J., & Martin Ben, R. (2001). The economic benefits of publicly funded basic research: A critical review. Research Policy, 30(3), 509-532. DOI: 10.1016/S0048-7333(00)00091-3
Seth, S. (2009). Putting knowledge in its place: Science, colonialism, and the postcolonial. Postcolonial Studies, 12, 373-388.  DOI: 10.1080/13688790903350633
Singh K., Nirmala, Ch., Trikha, R., Bhardwaj, M., & Kaur, S. (2024). Science, technology and innovation ecosystem: An Indian and global perspective. Chennai: Springer Nature. DOI: 10.1007/978-981-97-2815-2
Smith Eric, A. (2017). Evolutionary ecology and human behavior. New York: Taylor & Francis. DOI: 10.4324/9780203792704.
Stephan, P. (2012). How Economics Shapes Science. Massachusetts: Harvard University Press.
Voda, A. I., Bortos, S., & & Şoitu D. T. (2023). Knowledge ecosystem: A sustainable theoretical approach. European Journal of Sustainable Development, 12(2), 47-66. DOI: 10.14207/ejsd. 2023.v12n2p47
Wagner, C. S., & Leydesdorff, L. (2005). Network structure, self-organization, and the growth of international collaboration in science. Research Policy, 34, 1608-1618. DOI: 10.1016/j.respol.2005.08.002
Wagner, C.S., & Jonkers, K. (2017). Open countries have strong science. Nature, 550, 32-33. DOI: 10.1038/550032a
Wilson, E. O. (1999). Consilience: The Unity of Knowledge. New York: Vintage. 
Wojciechowski, J. A. (2010). Ecology of knowledge. Lithuania: The Council for Research in Values and Philosophy

Files

Share

How to cite

Statistics