COVID-19 Salgını ve Teknolojik Değişim: Patent Başvurularının Analizi

Yıl 2023, Cilt: 57 Sayı: 3, 549 - 562, 31.07.2023

Metin Yıldırım

https://doi.org/10.51551/verimlilik.1261654

Öz

Amaç: Bu çalışmanın amacı, COVID-19'un inovasyon üzerindeki etkisinin ölçülmesi ve Pandemi sürecinde teknolojik değişimlerin izlenmesidir.
Yöntem: Avrupa Patent Ofisi’ne yapılan 2012-2021 yılları arasındaki patent başvuruları araştırmanın temel veri kaynağını oluşturmaktadır. İlgili veri seti içinde beş ana kategori ve 35 farklı teknolojik disipline ait yıllık patent başvuru değerleri yer almaktadır. Zaman serisine dayanan Prophet tahmin modeli, 2020 ve 2021 yılları için patent başvurusu sayılarını tahmin edilmesi amacıyla oluşturulmuştur. Pandeminin neden olduğu krizin teknolojik değişimler ve inovasyon üzerindeki etkisinin izlenmesi amacıyla gerçekleşen ve öngörülen patent başvuru değerleri üzerinden analiz gerçekleştirilmiştir.
Bulgular: Çalışmanın bulguları, 2020 ve 2021 yıllarında toplam patent başvuru değerinde önemli bir değişiklik olmadığını göstermektedir. 2020 ve 2021'de, sırasıyla 15 ve 16 teknolojik alanda tahmin edilenden daha fazla patent başvurusunun yapıldığı saptanmıştır. Yarı iletkenler, görsel-işitsel ve nanoteknoloji olmak üzere üç alanda Pandemi sırasında önemli ilerlemelerin kaydedilmiş olması çalışmanın ortaya koyduğu diğer önemli bulgulardan bir diğeridir.
Özgünlük: Bu çalışmanın özgünlüğü, 35 farklı teknolojik disiplin için pandeminin inovasyon ve teknolojik değişim üzerindeki etkilerinin analizinde Avrupa Patent Ofisine yapılan patent başvurularının Prophet tahmin modeli ile kullanılmasında yatmaktadır.

Anahtar Kelimeler

Teknolojik Değişim, İnovasyon, Patent, Prophet Modeli, COVID-19

COVID-19 Pandemic and Technological Change: Analysis of Patent Applications

Öz

Purpose: This study is aimed to measure COVID-19's impact on innovation and monitor technical change during the Pandemic through patent applications.
Methodology: Patent application annual total number of European Patent Office patent applications data on 35 different technological disciplines separated into five key categories from 2012 to 2021 were utilized in the analysis. The Prophet forecasting model to forecast patent applications for 2020 and 2021 has been developed. The technological advancements and Pandemic impact on innovation were then analyzed using the actual and forecasted values.
Findings: The study's findings indicate no apparent difference between actual numbers and forecasted values. It was found that in 2020 and 2021, more patent applications than expected were made in 15 and 16 technological areas, respectively. The study also found that semiconductors, audio-visual, and nanotechnology advancements have been notable during the Pandemic.
Originality: The originality of this study lies in the use of the Prophet forecasting model based on European Patent Office patent application values in the analysis of the effects of the pandemic on innovation and technological change for 35 different technological disciplines.

Anahtar Kelimeler

Technological Change, Innovation, Patent, Prophet Model, COVID-19

Kaynakça

• Acemoğlu, D. (2002). “Directed Technical Change”, The Review of Economic Studies, 69(4), 781-809.
• Altuntas, S., Dereli, T. and Kusiak, A. (2015). “Forecasting Technology Success Based on Patent Data”, Technological Forecasting and Social Change, 96, 202-214.
• Arslan, S. (2022). “A Hybrid Forecasting Model Using LSTM and Prophet for Energy Consumption with Decomposition of Time Series Data”, PeerJ Computer Science, 8, e1001.
• Atasever, S., Öztürk, B. and Bilgiç, G. (2022). “A New Approach to Short-term Wind Speed Prediction: The Prophet Model”, Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 44(4), 8826-8841.
• Bloom, N., Davis, S.J. and Zhestkova, Y. (2021). “Covid-19 Shifted Patent Applications Toward Technologies That Support Working from Home”, AEA Papers and Proceedings, 111, 263-266.
• Chang, S.B., Lai, K-K. and Chang, S.-M. (2009). “Exploring Technology Diffusion and Classification of Business Methods: Using the Patent Citation Network”, Technological Forecasting and Social Change, 76(1), 107-117.
• del Río González, P. (2009). “The Empirical Analysis of the Determinants for Environmental Technological Change: A Research Agenda”. Ecological Economics, 68(3), 861-878.
• Dikta, G. (2006). “Time Series Methods to Forecast Patent Filings. Forecasting Innovations: Methods for Predicting Numbers of Patent Filings”, Springer, 95-124, Berlin, DOI: 10.1007/3-540-35992-3_6.
• Dubey, A., Soni, P. and Sharma, R. (2022). “Patent Landscape of COVID-19 Innovations: A Comprehensive Review”, Journal of Intellectual Property Rights (JIPR), 27(3), 212-226.
• Durmuşoğlu, A. (2017). “Effects of Clean Air Act on Patenting Activities in Chemical Industry: Learning from Past Experiences”, Sustainability, 9(5), 862.
• Durmuşoğlu, A. (2018). “Updating Technology Forecasting Models Using Statistical Control Charts”, Kybernetes, 47(4), 672-688.
• Filippetti, A. and Archibugi, D. (2011). “Innovation in Times of Crisis: National Systems of Innovation, Structure and Demand”, Research Policy, 40(2), 179-192.
• Haegeman, K., Marinelli, E., Scapolo, F., Ricci, A. and Sokolov, A. (2013). “Quantitative and Qualitative Approaches in Future-oriented Technology Analysis (FTA): from Combination to Integration?”, Technological Forecasting and Social Change, 80(3), 386-397.
• Harvey, A.C. and Peters, S. (1990). “Estimation Procedures for Structural Time Series Models”, Journal of Forecasting, 9(2), 89-108, DOI: 10.1002/for.3980090203.
• Havermans, Q.A., Gabaly, S. and Hidalgo, A. (2017). “Forecasting European Trade Mark and Design Filings: An Innovative Approach Including Exogenous Variables and I.P. Offices' Events”, World Patent Information, 48, 96-108.
• Hidalgo, A. and Gabaly, S. (2013). “Optimization of Prediction Methods for Patents and Trademarks in Spain Through the Use of Exogenous Variables”, World Patent Information, 35(2), 130-140.
• Hingley, P. and Dikta, G. (2019). “Finding a Well Performing Box-Jenkins Forecasting Model for Annualised Patent Filings Counts”, International Symposium on Forecasting, Thessaloniki, Greece, June 2019.
• Hingley, P. and Park, W. (2016). “Forecasting Patent Filings at the European Patent Office (EPO) with a Dynamic Log Linear Regression Model: Applications and Extensions”, Selected Papers from the Asia Conference on Economics and Business Research 2015, 63-83.
• Huang, L.C. and Li, Y. (2010). “Research on Technology Trend Based on Patent Information, 2010 IEEE International Conference on Management of Innovation and Technology, 209-213, DOI: 10.1109/ICMIT.2010.5492922.
• Kisi, O., Latifoğlu, L. and Latifoğlu, F. (2014). “Investigation of Empirical Mode Decomposition in Forecasting of Hydrological Time Series”, Water Resources Management, 28, 4045-4057.
• Köker, A.R. and Alan, H. (2021). “COVID-19 Küresel Salgın Sürecinin İşletmelerin Teknik Yenilik Faaliyetlerine Yansımaları: Patent Başvuruları Üzerine Bir Araştırma”, İşletme Araştırmaları Dergisi, 13(1), 267-280.
• Ma, S.C., Xu, J.H. and Fan, Y. (2022). “Characteristics and Key Trends of Global Electric Vehicle Technology Development: A Multi-Method Patent Analysis”, Journal of Cleaner Production, 338, 130502.
• Martínez-Ardila, H., Corredor-Clavijo, A., del Pilar Rojas-Castellanos, V., Contreras, O. and Lesmes, J.C. (2022). “The Technology Life Cycle of Persian Lime. A Patent Based Analysis”, Heliyon, 8(11), e11781.
• Nagaoka, S., Motohashi, K. and Goto, A. (2010). “Patent Statistics as an Innovation Indicator”, Handbook of the Economics of Innovation, Elsevier, Vol. 2, 1083-1127.
• Onea, I.A. (2022). “Exploring the COVID-19 Pandemic Impact on Innovation and Entrepreneurship-Review and Evidence from Global Innovation Index”, Proceedings of the International Conference on Business Excellence, 16(1), 527-544.
• Pavitt, K. (1985). “Patent statistics as Indicators of Innovative Activities: Possibilities and Problems”, Scientometrics, 7(1-2), 77-99.
• Prophet | Forecasting at scale. (2023, February 18), https://facebook.github.io/prophet/, (Access Date: 18.02.2023).
• Rahman, A.S., Hosono, T., Kisi, O., Dennis, B. and Imon, A.R. (2020). “A Minimalistic Approach for Evapotranspiration Estimation using the Prophet Model”, Hydrological Sciences Journal, 65(12), 1994-2006.
• Rip, A. and Kemp, R. (1998). “Technological Change”, Human Choice and Climate Change, 2(2), 327-399.
• Romer, P.M. (1990). “Endogenous Technological Change”, Journal of Political Economy, 98 (5, Part 2), S71-S102.
• Shih, M.J., Liu, D.R. and Hsu, M.-L. (2010). “Discovering Competitive Intelligence by Mining Changes in Patent Trends”, Expert Systems with Applications, 37(4), 2882-2890.
• Sinigaglia, T., Martins, M.E.S. and Siluk, J.C.M. (2022). “Technological Evolution of Internal Combustion Engine Vehicle: A Patent Data Analysis”, Applied Energy, 306, 118003.
• Smith, M. and Agrawal, R. (2015). “A Comparison of Time Series Model Forecasting Methods on Patent Groups”, MAICS, 1353, 167-173.
• Statistics and Trends Centre | Epo.org. (2023). https://new.epo.org/en/statistics-centre#/customchart, (Access Date: 18.02.2023).
• Taylor, S.J. and Letham, B. (2018). “Forecasting at Scale”, The American Statistician, 72(1), 37-45.
• Toharudin, T., Pontoh, R.S., Caraka, R.E., Zahroh, S., Lee, Y. and Chen, R.C. (2023). “Employing Long Short-Term Memory and Facebook Prophet Model in Air Temperature Forecasting”, Communications in Statistics-Simulation and Computation, 52(2), 279-290.
• Wei, H., Xie, E. and Gao, J. (2022). “R&D Investment and Debt Financing of High-Tech Firms in Emerging Economies: The Role of Patents and State Ownership”, IEEE Transactions on Engineering Management, 1-18, DOI: 10.1109/TEM.2021.3133330.
• WHO. (2023, February 26). https://www.who.int/news/item/21-11-2022-who-to-identify-pathogens-that-could-cause-future-outbreaks-and-pandemics, (Access Date: 26.02.2023).