İnovasyon ve Refah İlişkisi: Ülkelerin Gelişmişlik Düzeyine Göre Bir Uygulama

Öz

İnovasyon, uzun dönemli ekonomik büyüme ve sorun çözme kapasitesi üzerinde belirleyici bir rol oynarken, refah daha geniş bir çerçevede insan gelişimi, sağlık ve toplumsal iyi oluşu yansıtır. Bu iki kavramın önemi açık olmakla birlikte, aralarındaki yapısal ve çift yönlü ilişkiyi doğrudan inceleyen çalışmalar oldukça sınırlıdır. Bu çalışma, 2011-2021 dönemine ait 111 ülkeyi kapsayan yıllık veriler kullanılarak farklı gelişmişlik düzeylerindeki ülkelerde inovasyon ile refah arasındaki dinamik nedensel ilişkiyi analiz etmektedir. Analizde çok yöntemli bir yaklaşım benimsenmiştir. İlişkilerin belirlenmesinde Tree Augmented Naive Bayes algoritması, bu ilişkilerin test edilmesinde Kısmi En Küçük Kareler Yapısal Eşitlik Modellemesi ve öncelikli politika alanlarının belirlenmesinde Önem-Performans Haritası Analizi kullanılmıştır. Elde edilen bulgular, kurumsal kalitenin tüm ülke gruplarında refahı artırmada istikrarlı biçimde temel bir unsur olduğunu göstermektedir. Bununla birlikte, inovasyonun belirleyicileri ülkelerin gelişmişlik düzeyine göre farklılık göstermektedir. Gelişmiş ve gelişmekte olan ekonomilerde sosyal sermaye, altyapı ve pazara erişim ile girişimcilik koşulları inovasyonu daha güçlü biçimde desteklerken, daha az gelişmiş ülkelerde eğitim, yönetişim ve ekonomik kalite ön plana çıkmaktadır. Bu sonuçlar, inovasyon ve refahın birbirinden bağımsız ele alınmaması gerektiğine işaret etmektedir. Politika geliştirmede ülke koşulları dikkate alınmalı; daha az gelişmiş ülkelerde eğitim ve kurumsal kaliteye öncelik verilirken, daha ileri ekonomilerde sosyal ağların ve girişimcilik ekosisteminin güçlendirilmesine odaklanılmalıdır.

Anahtar Kelimeler

• İnovasyon
• refah
• kısmi en küçük kareler yapısal eşitlik modellemesi

Innovation and Prosperity: Evidence from Countries by Development Level

Öz

Innovation has important effects on long-term economic growth and problem-solving, while prosperity reflects a much broader measure of human development, health, and overall societal well-being. Although these concepts are of great importance, research explicitly exploring the structural, bidirectional relationship between these two critical concepts remains scarce. This study examines the dynamic causal relationship between innovation and prosperity across countries at different stages of development using annual data for 111 countries from 2011 to 2021. A multi-method framework is employed, combining Tree Augmented Naive Bayes networks to identify relationships, Partial Least Squares Structural Equation Modeling to test these relationships, and Importance-Performance Map Analysis to determine priority areas. The findings show that institutional quality consistently plays a key role in promoting prosperity across all country groups. However, the main drivers of innovation differ by development level. In developed and developing economies, social capital, infrastructure and market access, and enterprise conditions play a more prominent role in supporting innovation. In contrast, in less developed countries, education, governance, and economic quality emerge as the primary determinants. These results suggest that innovation and prosperity should not be addressed separately. Policy strategies should be context-specific, prioritizing education and institutional quality in less developed countries, while strengthening social networks and entrepreneurial ecosystems in more advanced economies.

Anahtar Kelimeler

• Innovation
• prosperity
• partial least squares structural equation modeling

Kaynakça

1. Acemoglu, D., Akcigit, U., & Kerr, W. R. (2016). Innovation network (NBER Working Paper No. 22783). National Bureau of Economic Research. https://ssrn.com/abstract=2861715
2. Acemoglu, D., Naidu, S., Restrepo, P., & Robinson, J. A. (2019). Democracy does cause growth. Journal of Political Economy, 127(1), 47-100. https://doi.org/10.1086/700936
3. Acemoglu, D. (2025). Institutions, technology and prosperity (NBER Working Paper No. 33442). National Bureau of Economic Research. https://www.nber.org/papers/w33442
4. Adler, M. W., Pasidis, I., Levkovich, O., Lembcke, A. C., & Ahrend, R. (2020). Roads, market access and regional economic development (OECD Working Paper). https://www.oecd.org/content/dam/oecd/en/publications/reports/2020/12/roads-market-access-and-regional-economic-development_eaadc814/8b9eca17-en.pdf
5. Agénor, P.-R., & Neanidis, K. C. (2015). Innovation, public capital, and growth. Journal of Macroeconomics, 44, 252-275. https://doi.org/10.1016/j.jmacro.2015.03.003
6. Aghion, P., Akcigit, U., Bergeaud, A., Blundell, R., & Hemous, D. (2019). Innovation and top income inequality. The Review of Economic Studies, 86(1), 1-45. https://doi.org/10.1093/restud/rdy027
7. Al Nuairi, A., Simsekler, M. C. E., Qazi, A., & Sleptchenko, A. (2024). A data-driven Bayesian belief network model for exploring patient experience drivers in healthcare sector. Annals of Operations Research, 342, 1797-1817. https://doi.org/10.1007/s10479-023-05437-9
8. Alshamrani, A. M., & Hezam, I. M. (2023). Integrating rough-entropy and rough-TOPSIS methods for evaluating the legatum prosperity pillars of weakest performing countries. Measurement and Control, 56(9-10), 1670-1683. https://doi.org/10.1177/00202940231174427

9. BayesFusion, LLC. (2024). GeNIe Modeler (Version 2.0) [Computer software]. http://www.bayesfusion.com/
10. Bottazzi, G., & Dindo, P. (2013). Globalizing knowledge: How technological openness affects output, spatial inequality, and welfare levels. Journal of Regional Science, 53(4), 631-655. https://doi.org/10.1111/jors.12034
11. Brynjolfsson, E. & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W.W. Norton & Company.
12. Chen, M.-H., Chang, Y.-C., & Hung, S.-C. (2008). Social capital and creativity in R&D project teams. R&D Management, 38(1), 21-34. https://doi.org/10.1111/j.1467-9310.2007.00494.x
13. Delen, D., Topuz, K., & Eryarsoy, E. (2020). Development of a Bayesian belief network-based DSS for predicting and understanding freshmen student attrition. European Journal of Operational Research, 281(3), 575-587. https://doi.org/10.1016/j.ejor.2019.03.037
14. Ding, H. (2012). Economic growth and welfare state: A debate of econometrics (MPRA Paper No. 41327). Munich Personal RePEc Archive. https://mpra.ub.uni-muenchen.de/41327
15. Donges, A., Meier, J.-M., & Silva, R. (2019, June 25). The impact of institutions on innovation. SSRN Electronic Journal. http://dx.doi.org/10.2139/ssrn.2815541
16. Dutta, S., Lanvin, B., & Wunsch-Vincent, S. (Eds.). (2020). Global innovation index 2020. Economy Reports & Analysis, 1-9. https://tind.wipo.int/record/42316
17. Ekici, Ş. Ö., Kabak, Ö., & Ülengin, F. (2019). Improving logistics performance by reforming the pillars of Global Competitiveness Index. Transport Policy, 81, 197-207. https://doi.org/10.1016/j.tranpol.2019.06.014
18. Etzkowitz, H. (2008). The triple helix: University-industry-government innovation in action. Routledge. https://doi.org/10.4324/9780203929605
19. Feng, Y., Kugler, J., Swaminathan, S., & Zak, P. J. (2008). Path to prosperity: The dynamics of freedom and economic development. International Interactions, 34(4), 423-441. https://doi.org/10.1080/03050620802561488
20. Friedman, N., Geiger, D., & Goldszmidt, M. (1997). Bayesian network classifiers. Machine Learning, 29(2), 131-163. https://doi.org/10.1023/A:1007465528199
21. Fukuyama, F. (1995). Social capital and the global economy. Foreign Affairs, 74(5), 89-103. https://doi.org/10.2307/20047302
22. Hajighasemi, A., Oghazi, P., Aliyari, S., & Pashkevich, N. (2022). The impact of welfare state systems on innovation performance and competitiveness: European country clusters. Journal of Innovation & Knowledge, 7(4), 100236. https://doi.org/10.1016/j.jik.2022.100236
23. Heeks, R., Foster, C., & Nugroho, Y. (2014). New models of inclusive innovation for development. Innovation and Development, 4(2), 175-185. https://doi.org/10.1080/2157930X.2014.928982
24. Henkel, J., & von Hippel, E. (2004). Welfare implications of user innovation. The Journal of Technology Transfer, 30, 73-87. https://doi.org/10.1007/s10961-004-4359-6
25. Jakusenko, A., & Kalashnikova, A. (2012). Creativity as a shortcut to economic prosperity (Master's thesis). GRIN Verlag. https://www.grin.com/document/212337
26. Jiang, X., Fu, W., & Li, G. (2020). Can the improvement of living environment stimulate urban innovation? Analysis of high-quality innovative talents and foreign direct investment spillover effect mechanism. Journal of Cleaner Production, 255, 120212. https://doi.org/10.1016/j.jclepro.2020.120212
27. Jitmaneeroj, B. (2016). A new approach to prioritizing SME regulation reforms. Journal of Small Business and Enterprise Development, 23(3), 854-872. https://doi.org/10.1108/JSBED-11-2015-0161
28. Koo, J., Choi, Y. J., & Park, I. (2020). Innovation and welfare: The marriage of an unlikely couple. Policy and Society, 39(2), 189-207. https://doi.org/10.1080/14494035.2019.1641380
29. Legatum Institute. (2021). The Legatum Prosperity Index 2021. https://www.prosperity.com/
30. Linder, C., Lechner, C., & Pelzel, F. (2020). Many roads lead to Rome: How human, social, and financial capital are related to new venture survival. Entrepreneurship Theory and Practice, 44(5), 909-932. https://doi.org/10.1177/1042258719867558
31. Lindert, P. H. (2003). Why the welfare state looks like a free lunch? (NBER Working Paper No. 9869). National Bureau of Economic Research. https://www.nber.org/papers/w9869
32. Nordin, N. N., & Nordin, N. H. (2016). Determinants of innovation in developing countries: A panel generalized method of moments analysis. Jurnal Ekonomi Malaysia, 50(2), 93-105. http://dx.doi.org/10.17576/JEM-2016-5002-08
33. Ozili, P. K. (2023). The acceptable R-square in empirical modelling for social science research. In Social research methodology and publishing results: A guide to non-native English speakers (pp. 134-143). IGI Global.
34. Richter, N. F., & Tudoran, A. A. (2024). Elevating theoretical insight and predictive accuracy in business research: Combining PLS-SEM and selected machine learning algorithms. Journal of Business Research, 173, 114453. https://doi.org/10.1016/j.jbusres.2023.114453
35. Ringle, C. M., & Sarstedt, M. (2016). Gain more insight from your PLS-SEM results: The importance-performance map analysis. Industrial Management & Data Systems, 116(9), 1865-1886. https://doi.org/10.1108/IMDS-10-2015-0449
36. Ringle, C. M., Wende, S., & Becker, J.-M. (2024). SmartPLS 4 [Computer software]. SmartPLS. https://www.smartpls.com
37. Rodrik, D. (2007). One economics, many recipes. Princeton University Press. https://doi.org/10.2307/j.ctvcm4jbh
38. Romer, P. M. (1990). Endogenous technological change. Journal of Political Economy, 98(5), S71-S102. https://doi.org/10.1086/261725
39. Rothwell, J., Lobo, J., Strumsky, D., & Muro, M. (2013). Patenting prosperity: Invention and economic performance in the United States and its metropolitan areas [Report]. Brookings Institution. https://www.brookings.edu/wp-content/uploads/2016/06/patenting-prosperity-rothwell.pdf
40. Ruiu, M. L., Seddaiu, G., & Roggero, P. P. (2017). Developing adaptive responses to contextual changes for sustainable agricultural management: The role of social capital in the Arborea district (Sardinia, Italy). Journal of Rural Studies, 49, 162-170. https://doi.org/10.1016/j.jrurstud.2016.11.017
41. Schumpeter, J. A. (1934). The theory of economic development: An inquiry into profits, capital, credit, interest, and the business cycle. Harvard University Press.
42. Sen, A. (1999). Development as freedom. Oxford University Press.
43. Sharma, P. (2019). K means clustering algorithm. Analytics Vidhya. Retrieved January 15, 2024, from https://www.analyticsvidhya.com/blog/2019/08/comprehensive-guide-k-means-clustering/
44. Shmueli, G., Ray, S., Estrada, J. M. V., & Chatla, S. B. (2016). The elephant in the room: Predictive performance of PLS models. Journal of Business Research, 69(10), 4552-4564. https://doi.org/10.1016/j.jbusres.2016.03.049
45. Shmueli, G., Sarstedt, M., Hair, J. F., Cheah, J.-H., Ting, H., Vaithilingam, S., & Ringle, C. M. (2019). Predictive model assessment in PLS-SEM: Guidelines for using PLSpredict. European Journal of Marketing, 53(11), 2322-2347. https://doi.org/10.1108/EJM-02-2019-0189
46. Sivak, R., Caplanova, A., & Hudson, J. (2011). The impact of governance and infrastructure on innovation. Post-Communist Economies, 23(2), 203-217. https://doi.org/10.1080/14631377.2011.570050
47. Subramaniam, M., & Youndt, M. A. (2005). The influence of intellectual capital on the types of innovative capabilities. Academy of Management Journal, 48(3), 450-463. https://doi.org/10.5465/amj.2005.17407911
48. Terzić, L. (2021). Revealing the effects of competitiveness and innovation on economic growth: Evidence from European leading and following countries. Journal of Theoretical and Applied Economics, 29(4), 19-30. https://store.ectap.ro/articole/1564.pdf
49. Thompson, M. (2018). Social capital, innovation and economic growth. Journal of Behavioral and Experimental Economics, 73, 46-52. https://doi.org/10.1016/j.socec.2018.01.005
50. Vinzi, V. E., Trinchera, L., & Amato, S. (2010). PLS path modeling: From foundations to recent developments and open issues for model assessment and improvement. In V. Esposito Vinzi, W. W. Chin, J. Henseler, & H. Wang (Eds.), Handbook of partial least squares: Concepts, methods and applications (pp. 47-82). Springer. https://doi.org/10.1007/978-3-540-32827-8_3
51. WIPO. (2021). Global Innovation Index 2021: Tracking innovation through the COVID-19 crisis. https://www.wipo.int/en/web/global-innovation-index/2021/index