Структурни ПЦА-МЛР модел иновационог окружења у земљама БРИКС-а

  • Ivana Petkovski Mathematical Institute SASA
Ključne reči: Inovacije, BRIKS, Analiza glavnih komponenti, Regresija

Sažetak


Процес глобализације форсира промене на тржишту у облику интензивне конкуренције. Економије могу опстати стицањем конкурентске предности на глобалном тржишту развојем иновација. Главни циљ овог емпиријског истраживања је откривање најважнијих компонената иновација које чине структуру глобалног индекса иновација (ГИИ) и евалуацију њиховог утицаја на ово рангирање у економијама БРИKС-а. О иновационом процесу се расправља на основу резултата рангирања ГИИ акумулираних од 2011. до 2019. Предложени методолошки оквир за проналажење компонената које су снажно повезане са ГИИ је анализа главних компонената (ПЦА). ПЦА се користи за смањење различитих компонената ГИИ на једнофакторско или двофакторско решење у свакој од ГИИ димензија. Ишод истраживања ПЦА предлаже девет компоненти које представљају седам димензија. Димензије које указују на институције и инфраструктуру обухватају двофакторска решења. Издвојене компоненте се даље користе у регресионој анализи како би се успоставила једначина вишеструке линеарне регресије (МЛР) за предвиђање ГИИ резултата који се користи у укупном рангирању. Изведено регресијско решење указало је на вредне МЛР резултате са високим коефицијентом детерминације, где се 96,1% вредности ГИИ објашњава издвојеним компонентама. Доминантни ефекти на ГИИ постижу се у компонентама иновационог учинка које укључују утицај знања и нематеријалну имовину. Штавише, упоредна анализа стварних и израчунатих ГИИ резултата показала је преклапање 98,9% између две вредности. Процењени резултати ПЦА-МЛР анализе служе за испитивање успеха у развоју иновационих перформанси у економијама упоређивањем индекса иновација који постижу БРИKС земље.

Reference

Abrougui, K., Gabsi, K., Mercatoris, B., Khemis, C., Amami, R., & Chehaibi, S. (2019). Prediction of organic potato yield using tillage systems and soil properties by artificial neural network (ANN) and multiple linear regressions (MLR). Soil and Tillage Research, 190, 202-208.

Afrifa, G.A., Tingbani, I., Yamoah, F., & Appiah, G. (2020). Innovation input, governance and climate change: Evidence from emerging countries. Technological Forecasting and Social Change, 161, 120256.

Aguirre-Bastos, C., & Weber, M.K. (2018). Foresight for shaping national innovation systems in developing economies. Technological Forecasting and Social Change, 128, 186-196.

Ahmed, N., Roy, S., & Islam, M.A. (2020). Forecasting Supply Chain Sporadic Demand Using Principal Component Analysis (PCA).

Băzăvan, A. (2019). Chinese government's shifting role in the national innovation system. Technological Forecasting and Social Change, 148, 119738.

Boubakri, N., Chkir, I., Saadi, S., & Zhu, H. (2021). Does national culture affect corporate innovation? International evidence. Journal of Corporate Finance, 66, 101847.

Cai, Z., Tan, K.H., Zhang, L., Du, J., Song, M., & Zhou, X. (2021). Technological Innovation and Structural Change for Economic Development in China as an Emerging Market. Technological Forecasting and Social Change, 167, 120671.

Chkir, I., Hassan, B.E.H., Rjiba, H., & Saadi, S. (2021). Does corporate social responsibility influence corporate innovation? International evidence. Emerging Markets Review, 46, 100746.

Coccia, M. (2014). Driving forces of technological change: the relation between population growth and technological innovation: analysis of the optimal interaction across countries. Technological Forecasting and Social Change, 82, 52-65.

Cui, Y., Jiao, J., & Jiao, H. (2016). Technological innovation in Brazil, Russia, India, China, and South Africa (BRICS): an organizational ecology perspective. Technological Forecasting and Social Change, 107, 28-36.

Datta, S., Saad, M., & Sarpong, D. (2019). National systems of innovation, innovation niches, and diversity in university systems. Technological Forecasting and Social Change, 143(C), 27-36.

De Silva, C.C., Beckman, S.P., Liu, S., & Bowler, N. (2019). Principal component analysis (PCA) as a statistical tool for identifying key indicators of nuclear power plant cable insulation degradation. In Proceedings of the 18th International Conference on Environmental Degradation of Materials in Nuclear Power Systems–Water Reactors (pp. 1227-1239). Springer, Cham.

do Carmo Silva, M., Gavião, L.O., Gomes, C.F.S., & Lima, G.B.A. (2017). A proposal for the application of multicriteria analysis to rank countries according to innovation using the indicators provided by the World Intellectual Property Organization. RAI Revista de Administração e Inovação, 14(3), 188-198.

Erciş, A., & Ünalan, M. (2016). Innovation: A comparative case study of Turkey and South Korea. Procedia-Social and Behavioral Sciences, 235, 701-708.

Esteves, K., & Feldmann, P.R. (2016). Why Brazil does not innovate: a comparison among nations. RAI Revista de Administração e Inovação, 13(1), 29-38.

Feng, G.F., Zheng, M., Wen, J., Chang, C.P., & Chen, Y.E. (2019). The assessment of globalization on innovation in Chinese manufacturing firms. Structural Change and Economic Dynamics, 50, 190-202.

Filippetti, A., & Guy, F. (2020). Labor market regulation, the diversity of knowledge and skill, and national innovation performance. Research Policy, 49(1), 103867.

Franco, C., & de Oliveira, R. H. (2017). Inputs and outputs of innovation: analysis of the BRICS: Theme 6–innovation technology and competitiveness. RAI Revista de Administração e Inovação, 14(1), 79-89.

Global Innovation index (2011). The Global Innovation Index 2011: Accelerating Growth and Development. World Intellectual Property Organization (WIPO). Geneva, Switzerland. Retrieved from: https://www.wipo.int/edocs/pubdocs/en/economics/gii/gii_2011.pdf 

Global Innovation index (2012). The Global Innovation Index 2012: Stronger Innovation Linkages for Global Growth. World Intellectual Property Organization (WIPO). Geneva, Switzerland. Retrieved from: https://www.globalinnovationindex.org/userfiles/file/GII-2012-Report.pdf 

Global Innovation index (2013). The Global Innovation Index 2013: The Local Dynamics of Innovation. World Intellectual Property Organization (WIPO). Geneva, Switzerland. Retrieved from: https://www.wipo.int/edocs/pubdocs/en/economics/gii/gii_2013.pdf 

Global Innovation index (2014). The Global Innovation Index 2014: The Human Factor in Innovation. World Intellectual Property Organization (WIPO). Geneva, Switzerland. Retrieved from: https://www.globalinnovationindex.org/userfiles/file/reportpdf/GII-2014-v5.pdf 

Global Innovation index (2015). The Global Innovation Index 2015: Effective Innovation Policies for Development. World Intellectual Property Organization (WIPO). Geneva, Switzerland. Retrieved from: https://www.wipo.int/edocs/pubdocs/en/wipo_gii_2015.pdf 

Global Innovation index (2016). The Global Innovation Index 2016: Winning with Global Innovation. World Intellectual Property Organization (WIPO). Geneva, Switzerland. Retrieved from: https://www.wipo.int/edocs/pubdocs/en/wipo_pub_gii_2016.pdf 

Global Innovation index (2017). The Global Innovation Index 2017: Innovation Feeding the World. World Intellectual Property Organization (WIPO). Geneva, Switzerland. Retrieved from: https://www.wipo.int/edocs/pubdocs/en/wipo_pub_gii_2017.pdf 

Global Innovation index (2018). The Global Innovation Index 2018: Energizing the World with Innovation. World Intellectual Property Organization (WIPO). Geneva, Switzerland. Retrieved from: https://www.wipo.int/edocs/pubdocs/en/wipo_pub_gii_2018.pdf 

Global Innovation index (2019). The Global Innovation Index 2019: Creating Healthy Lives – The Future of Medical Innovation. World Intellectual Property Organization (WIPO). Geneva, Switzerland. Retrieved from: https://www.wipo.int/edocs/pubdocs/en/wipo_pub_gii_2019.pdf 

Global Innovation index (2020). The Global Innovation Index 2020: Who Will Finance Innovation? World Intellectual Property Organization (WIPO). Geneva, Switzerland. Retrieved from: https://www.wipo.int/edocs/pubdocs/en/wipo_pub_gii_2020.pdf 

Global Innovation index (2021). The Global Innovation Index 2021: What is the Future of Innovation-driven Growth? World Intellectual Property Organization (WIPO). Geneva, Switzerland. Retrieved from: https://www.wipo.int/edocs/pubdocs/en/wipo_pub_gii_2021.pdf

Hameed, K., Arshed, N., Yazdani, N., & Munir, M. (2021). Motivating business towards innovation: A panel data study using dynamic capability framework. Technology in Society, 65, 101581.

Hu, G.G. (2021). Is knowledge spillover from human capital investment a catalyst for technological innovation? The curious case of fourth industrial revolution in BRICS economies. Technological Forecasting and Social Change, 162, 120327.

Intarakumnerd, P., & Goto, A. (2018). Role of public research institutes in national innovation systems in industrialized countries: The cases of Fraunhofer, NIST, CSIRO, AIST, and ITRI. Research Policy, 47(7), 1309-1320.

Jolliffe, I.T., & Cadima, J. (2016). Principal component analysis: a review and recent developments. Philosophical Transactions of the Royal Society A: Mathematical, Physical and Engineering Sciences, 374(2065), 20150202.

Khedhaouria, A., & Thurik, R. (2017). Configurational conditions of national innovation capability: A fuzzy set analysis approach. Technological Forecasting and Social Change, 120, 48-58.

Kim, S., Parboteeah, K.P., Cullen, J.B., & Liu, W. (2020). Disruptive innovation and national cultures: Enhancing effects of regulations in emerging markets. Journal of Engineering and Technology Management, 57, 101586.

Lacasa, I.D., Jindra, B., Radosevic, S., & Shubbak, M. (2019). Paths of technology upgrading in the BRICS economies. Research Policy, 48(1), 262-280.

Lamichhane, S., Eğilmez, G., Gedik, R., Bhutta, M.K.S., & Erenay, B. (2021). Benchmarking OECD countries’ sustainable development performance: A goal-specific principal component analysis approach. Journal of Cleaner Production, 287, 125040.

Lee, C.C., Wang, C.W., & Ho, S.J. (2020). Country governance, corruption, and the likelihood of firms’ innovation. Economic Modelling, 92, 326-338.

Lu, W.M., Kweh, Q.L., & Huang, C.L. (2014). Intellectual capital and national innovation systems performance. Knowledge-based systems, 71, 201-210.

Maaouane, M., Zouggar, S., Krajačić, G., & Zahboune, H. (2021). Modelling industry energy demand using multiple linear regression analysis based on consumed quantity of goods. Energy, 225, 120270.

Mahroum, S., & Al-Saleh, Y. (2013). Towards a functional framework for measuring national innovation efficacy. Technovation, 33(10-11), 320-332.

Mamipour, S., Yahoo, M., & Jalalvandi, S. (2019). An empirical analysis of the relationship between the environment, economy, and society: Results of a PCA-VAR model for Iran. Ecological Indicators, 102, 760-769.

Mavi, R.K., & Mavi, N.K. (2021). National eco-innovation analysis with big data: A common-weights model for dynamic DEA. Technological Forecasting and Social Change, 162, 120369.

Mehmanpazir, F., Khalili-Damghani, K., & Hafezalkotob, A. (2019). Modeling steel supply and demand functions using logarithmic multiple regression analysis (case study: Steel industry in Iran). Resources Policy, 63, 101409.

Nair, H., Kumar, A., & Ahmed, O. (2014). Neural Network Modelling, Simulation and Prediction of Innovation Growth in United Arab Emirates (UAE). Procedia Computer Science, 36, 269-275.

Perez, L.V. (2017). Principal Component Analysis to Address Multicollinearity. Walla Walla: Whitman College.

Prokop, V., Hajek, P., & Stejskal, J. (2021). Configuration Paths to Efficient ‘‚National Innovation Ecosystems. Technological Forecasting and Social Change, 168, 120787.

Richardson, M. (2009). Principal component analysis. URL: http://people.maths. ox.ac.uk/richardsonm/SignalProcPCA.pdf (last access: 3.5. 2013). 

Rodionova, O., Kucheryavskiy, S., & Pomerantsev, A. (2021). Efficient tools for principal component analysis of complex data—A tutorial. Chemometrics and Intelligent Laboratory Systems, 104304.

Samara, E., Georgiadis, P., & Bakouros, I. (2012). The impact of innovation policies on the performance of national innovation systems: A system dynamics analysis. Technovation, 32(11), 624-638.

Shlens, J. (2014). A tutorial on principal component analysis. arXiv preprint arXiv:1404.1100.

Si, S., Zahra, S.A., Wu, X., & Jeng, D.J.F. (2020). Disruptive innovation and entrepreneurship in emerging economics. Journal of Engineering and Technology Management, 58, 101601.

Tabrizi, S.S., & Sancar, N. (2017). Prediction of Body Mass Index: A comparative study of multiple linear regression, ANN and ANFIS models. Procedia computer science, 120, 394-401.

Tian, Y., Wang, Y., Xie, X., Jiao, J., & Jiao, H. (2019). The impact of business-government relations on firms' innovation: Evidence from Chinese manufacturing industry. Technological Forecasting and Social Change, 143, 1-8.

Veiga, P.M., Teixeira, S.J., Figueiredo, R., & Fernandes, C.I. (2020). Entrepreneurship, innovation and competitiveness: A public institution love triangle. Socio-Economic Planning Sciences, 72, 100863.

Wang, C., Qiao, C., Ahmed, R.I., & Kirikkaleli, D. (2021). Institutional Quality, Bank Finance and Technological Innovation: A way forward for Fourth Industrial Revolution in BRICS Economies. Technological Forecasting and Social Change, 163, 120427.

Wiseman, A.W., & Anderson, E. (2012). ICT-integrated education and national innovation systems in the Gulf Cooperation Council (GCC) countries. Computers & Education, 59(2), 607-618.

World data bank (2021a). Population database. Retrieved from: https://data.worldbank.org/indicator/SP.POP.TOTL

World data bank (2021b). GDP database. Retrieved from: https://data.worldbank.org/indicator/NY.GDP.MKTP.CD

Xu, B., Chen, X., & Wang, Y. (2020). A new dynamic classification of enterprises for implementing precise industrial policies. Journal of Business Research, 118, 463-473.

Zabala-Iturriagagoitia, J.M., Aparicio, J., Ortiz, L., Carayannis, E.G., & Grigoroudis, E. (2021). The productivity of national innovation systems in Europe: Catching up or falling behind?. Technovation, 102, 102215.

Zhao, C., Qu, X., & Luo, S. (2019). Impact of the InnoCom program on corporate innovation performance in China: Evidence from Shanghai. Technological Forecasting and Social Change, 146, 103-118.

Zheng, M., Feng, G.F., Feng, S., & Yuan, X. (2019). The road to innovation vs. the role of globalization: A dynamic quantile investigation. Economic Modelling, 83, 65-83.

Objavljeno
2023/06/20
Rubrika
Originalni naučni članak