İhracat ve İnovasyon Temelli Markalaşma İlişkisi: Türkiye Örneği

Öz

Günümüzde ülkelerin ekonomik gelişiminde ve ihracat performansı artışında teknoloji yoğun üretim tek başına yeterli olmamaktadır. Rekabetin artmasıyla ihracat hacimlerini arttırmayı hedefleyen ülkeler ve işletmeler ürün ve pazarlama inovasyonu faaliyetleri ile ihracata konu olan ürün ve hizmetlerini geliştirmek ve yenilemek durumundadır. Bu çalışmada inovasyon faaliyetleri kapsamında markalaşma ve ihracat ilişkisi incelenmiş, ticarileşme düzeyinin ihracat üzerindeki etkisinin ortaya çıkarılması amaçlanmıştır. Pazarlama inovasyonu kapsamında marka kabul sayısının, ürün inovasyonu kapsamında patent kabul sayısına oranlanması ile elde edilen ticarileşme oranı bağımsız değişken; ihracat hacmi ise bağımlı değişken olarak modele dâhil edilmiştir. Bu çalışmada uzun dönemli ilişkilerin tespiti amacıyla Gregory-Hansen eşbütünleşme testi uygulanmıştır. Nedensellik ilişkilerinin ortaya çıkarılması amacıyla ise frekans alanı nedensellik testine başvurulmuştur. Analiz sonuçlarına göre değişkenler arasında uzun dönemli ilişkilerin bulunduğu tespit edilmiştir.

Anahtar Kelimeler

• İhracat
• Markalaşma
• Ticarileşme
• Gregory-Hansen Eşbütünleşme Testi
• Frekans Alanı Nedensellik Testi

The Relationship Between Export and Innovation-Based Branding: Case of Turkey

Abstract

Nowadays, technology-intensive production alone is not sufficient for the economic development of countries and the increase in export performance. Countries and enterprises aiming to increase their export volumes by increasing competition have to develop and renew export products and services that are subject to export through product and marketing innovation activities. In this study, the relationship between the branding and export within the scope of innovation activities are analysed and it is aimed to reveal the effect of the level of commercialization on exports. The rate of commercialization obtained by the ratio of the number of trademark registrations within the scope of marketing innovation to the number of patent grants within the scope of product innovation is included in the model as the independent variable while the volume of exports as the dependent variable. Gregory-Hansen cointegration test is applied to determine long-term relationships among the variables in the study. The frequency domain causality test is conducted in order to reveal causality relationships. According to the results of the analysis, it was determined that there were long-term relationships between the variables.

Keywords

• Export
• Branding
• Commercialization
• Gregory-Hansen Co-integration Test
• Frequency Domain Causality Test

References

1. Ahimbisibwe, G. M., Ntayi, J. M., & Ngoma, M. (2013). Export Market Orientation, Innovation and Performance of Fruit Exporting Firms in Uganda. European Scientific Journal, 9(4), 295-313.
2. Boehlje, M. (2004). Business Challenges in Commercialization of Agricultural Technology. International Food and Agribusiness Management Review, 7(1030-2016-82667), 91-104.
3. Breitung, J,. & Candelon, B. (2006) Testing for Short and Long-Run Causality: A Frequency Domain Approach. Journal of Econometrics, 132, 363-378.
4. Cassiman, B., & Martinez-Ros, E. (2007). Product Innovation and Exports - Evidence from Spanish Manufacturing, IESE Business School, Barcelona, 1-36.
5. Di Pietro, W. R., & Anoruo, E. (2006). Creativity, Innovation and Export Performance. Journal of Policy Modeling, 28(2), 133-139. Dickey, D. A. & Fuller, W. A. (1979). Distribution of the Estimators for Autoregressive Time Series with a Unit Root. Journal of the American Statistical Association, 74(366), 427–431.
6. Dickey, D. A. & Fuller, W. A. (1981). Likelihood Ratio Statistics for Autoregressive Time Series with a Unit Root. Econometrica, 49(4), 1057–1072.
7. Engle, R. F. & Granger, C. W. J. (1987). Co-Integration and Error Correction: Representation, Estimation, and Testing. Econometrica, 55, 251–276.
8. Filipescu, D. A., Prashantham, S., Rialp, A., & Rialp, J. (2013). Technological Innovation and Exports: Unpacking Their Reciprocal Causality. Journal of International Marketing, 21(1), 23-38.

9. Geweke, J. (1982). Measurement of Linear Dependence and Feedback between Multiple Time Series. Journal of the American Statistical Association, 77(378), 304-313.
10. Golovko, E. & Valentini, G. (2011). Exploring the Complementarity between Innovation and Export For SMEs’ Growth. Journal of International Business Studies, 42(3), 362-380.
11. Granger, C. W. J. (1969). Investigating Causal Relations by Econometric Models and Cross-Spectral Methods. Econometrica, 37(3), 424-438.
12. Granger, C. W. J. (1986). Developments in the Study of Cointegrated Economic Variables. Oxford Bulletin of Economics and Statistics, 48(3), 213–228.
13. Gregory, A. & Hansen, B. (1996). Residual-Based Tests for Cointegration in Models with Regime Shifts. Journal of Econometrics, 10, 321-335.
14. Gupta, S., Malhotra, N. K., Czinkota, M., & Foroudi, P. (2016). Marketing Innovation: A Consequence of Competitiveness. Journal of Business Research, 69(12), 5671-5681.
15. Hosoya, Y. (1991). The Decomposition and Measurement of the Interdependency between Secondorder Stationary Processes. Probability Theory Relat Fields, 88(4), 429-444.
16. Kongmanila, X., & Takahashi, Y. (2009). Innovation, Export Performance and Profitability of Lao Garment Exporters. International Journal of Economics and Management, 3(2), 225-236.
17. Moghaddam, B., & Armat, P. (2015). A Study on Effect of Innovation and Branding on Performance of Small and Medium Enterprises. Management Science Letters, 5(3), 245-250.
18. Mohr, J. J., Sengupta, S., & Slater, S. F. (2010). Marketing of High-Technology Products and Innovations. New Jersey: Pearson Prentice Hall.
19. OECD/Eurostat (2018), Oslo Manual 2018: Guidelines for Collecting, Reporting and Using Data on Innovation, 4th Edition, The Measurement of Scientific, Technological and Innovation Activities, OECD Publishing, Paris/Eurostat, Luxembourg. https://doi.org/10.1787/9789264304604-en.
20. Phillips, P.C. & Hansen, B.E. (1990). Statistical Inference in Instrumental Variables Regression with I(1) Processes. The Review of Economic Studies, 57, 99-125.
21. Phillips, P. & Perron, P. (1988). Testing for a Unit Root in Time Series Regression. Biometrika, 75, 335-346.
22. Pla-Barber, J., & Alegre, J. (2007). Analysing the Link between Export Intensity, Innovation and Firm Size in a Science-Based Industry. International Business Review, 16(3), 275-293.
23. Saikkonen, P. (1992): "Estimation and Testing of Cointegrated Systems by an Autoregressive Approximation, Econometric Theory, 8, 1-27.
24. Sok, P., O’Cass, A., & Sok, K. M. (2013). Achieving Superior SME Performance: Overarching Role of Marketing, Innovation, and Learning Capabilities. Australasian Marketing Journal (AMJ), 21(3), 161-167.
25. Stock, J.H. & Watson, M.W. (1993). A Simple Estimator of Cointegrating Vectors in Higher Order Integrated Systems. Econometrica: Journal of the Econometric Society, 783-820.
26. Śledzik, K. (2013). Schumpeter’s View on Innovation and Entrepreneurship. In Stefan Hittmar, Management Trends in Theory and Practice, Faculty of Management Science and Informatics, University of Zilina & Institute of Management by University of Zilina.
27. Van Beveren, I., & Vandenbussche, H. (2009). Product and Process Innovation and the Decision to Export: Firm-Level Evidence for Belgium. LICOS Discussion Paper Series. 247.
28. Yao, F. & Hosoya, Y. (2000). Inference on One-Way Effect and Evidence in Japanese Macroeconomic Data. Journal of Econometrics, 98(2), 225–255.
29. Zahra, S. A., & Nielsen, A. P. (2002). Sources of Capabilities, Integration and Technology Commercialization. Strategic Management Journal, 23(5), 377-398.
30. Zivot, E. & Andrews, D.W.K. (1992). Further Evidence on the Great Crash, the Oil Price Shock, and the Unit Root Hypothesis. Journal of Business & Economic Statistics, 10(3), 251-270.