Research Article
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The Role Of Networks In The Early Internationalization Of Emerging Market Firms: Evidence From Turkish Textile-Born Globals with Social-Business Networks Perspective

Year 2023, Volume: 20 Issue: Human Behavior and Social Institutions, 1029 - 1055, 30.10.2023
https://doi.org/10.26466/opusjsr.1347612

Abstract

Bu çalışma, gelişmekte olan ülke pazarlarından biri olarak görülen Türkiye’de, tekstil sektöründe faaliyet gösteren küresel doğan işletmelerin erken uluslararasılaşmasında ağ ilişkilerinin rolünü, sosyal ve iş ağı perspektifleri bakış açısıyla çift odaklı olarak, vaka temelli bir yaklaşım altında incelemektedir. Gelişmekte olan ülkeler ve düşük teknolojili sektörlerdeki KOBİ’lerin erken uluslararasılaşma davranışları ile ilgili araştırma boşluğundan yola çıkılarak organize edilen çalışmanın, gelişmekte olan bir ülke piyasası ve geleneksel bir sektörden konuyu ele alarak uluslararasılaşma literatürüne katkı sağlayacağı düşünülmektedir. Araştırma sonuçları, ağ ilişkilerinin küresel doğan işletmelerin erken uluslararasılaşmasında önemli rol oynadığını göstermektedir. Uluslararasılaşmada ağ ilişkilerinden işletmelerce elde edilenler; yabancı pazar fırsatlarının tanımlanması ve pazar bilgisi oluşturulması, yabancı pazar ve giriş yöntemi seçimi, kaynak kısıtlarının aşılması ve riskin azaltılması, operasyonel destek, önemli aktörlere erişim, manevi destek ile güven olmak üzere 7 başlık altında bulgulanmıştır. Hem sosyal hem de iş ağlarının erken ve hızlı uluslararasılaşmada etkili olduğu, bu etkinin daha önemlilik doğrultusunda kolayca kategorize edilemeyeceği bulunmuştur.

References

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  • Atar, B., & Aktan, D. Ç. (2013). Birey açıklayıcı madde tepki kuramı analizi: örtük regresyon iki parametreli lojistik modeli. Eğitim ve Bilim, 38(168).
  • Baker, F. B. (2001). The basics of item response theory. http://ericae. net/irt/baker.
  • Berberoğlu G. ve Kalender İ. (2005). Öğrenci Başarısının Yıllara, Okul Türlerine, Bölgelere Göre İncelenmesi: ÖSS ve PISA Analizi, ODTÜ Eğitim Bilimleri ve Uygulama Dergisi, Sayfa 27-28.
  • Blozis, S. A., Conger K. J., & Harring, J. R. (2007). Nonlinear latent curve models for multivariate longitudinal data. International Journal of Behavioral Development: Special Issue on Longitudinal Modeling of Developmental Processes, 31, 340-346
  • Boeck, P. de, Cho, S. J., & Wilson, M. (2011). Explanatory secondary dimension modeling of latent differential item functioning. Applied Psychological Measurement, 35(8), 583–603.
  • Boeck, P. de., &Wilson, M. (2004). Explanatory item response models. New York, NY: Springer New York.
  • Briggs, D. C. (2008). Using explanatory item response models to analyze group differences in science achievement. Applied Measurement in Education, 21(2), 89–118.
  • Bulut, O. (2021). eirm: Explanatory item response modeling for dichotomous and polytomous item responses, R package version 0.4. doi: 10.5281/zenodo.4556285 Available from https://CRAN.R-project.org/package=eirm.
  • Bulut, O., Palma, J., Rodriguez, M. C., & Stanke, L. (2015). Evaluating measurement invariance in the measurement of developmental assets in Latino English language groups across developmental stages. Sage Open, 5(2), 2158244015586238.
  • Büyükkıdık, S., & Bulut, O. (2022). Analyzing the Effects of Test, Student, and School Predictors on Science Achievement: An Explanatory IRT Modeling Approach. Journal of Measurement and Evaluation in Education and Psychology, 13(1), 40-53.
  • Cheema, J. R., & Galluzzo, G. (2013). Analyzing the gender gap in math achievement: Evidence from a large-scale US sample. Research in Education, 90(1), 98-112.
  • Chen, F., Yang, H., Bulut, O., Cui, Y., & Xin, T. (2019). Examining the relation of personality factors to substance use disorder by explanatory item response modeling of DSM-5 symptoms. PloS One, 14(6), e0217630. https://doi.org/10.1371/journal.pone.0217630
  • Chen, W. H., & Thissen, D. (1997). Local dependence indexes for item pairs using item response theory. Journal of Educational and Behavioral Statistics, 22(3), 265-289.
  • Chiu, T. (2016). Using Explanatory Item Response Models to Evaluate Complex Scientific Tasks Designed for the Next Generation Science Standards (Doctoral dissertation, UC Berkeley).
  • Crocker, L., & Algina, J. (1986). Introduction to classical and modern test theory. Holt, Rinehart and Winston, 6277 Sea Harbor Drive, Orlando, FL 32887.
  • De Ayala, R. J. (2013). The theory and practice of item response theory. Guilford Publications.
  • De Ayala, R. J. (2022). The theory and practice of item response theory, Second Edition. Guilford Publications.
  • DeMars, C. (2010). Item response theory. Oxford University Press. Desjardins, C. D., & Bulut, O. (2018). Handbook of educational measurement and psychometrics using R. CRC Press.
  • Ellison, G., & Swanson, A. (2018). Dynamics of the gender gap in high math achievement (No. w24910). National Bureau of Economic Research.
  • Embretson, S. E., & Reise, S. P. (2000). Item response theory for psychologists. Maheah.
  • Fleiss,J.L.(1971) "Measuring nominal scale agreement among many raters." Psychological Bulletin, Cilt 76, Sayi 5 say. 378-382
  • Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2012). How to design and evaluate research in education (8th edt.). New York: McGram-Hill Companies.
  • Hambleton, R. K., Swaminathan, H., & Rogers, H. J. (1991). Fundamentals of item response theory. Measurement methods for the social sciences series. Newbury Park, Calif.: Sage Publications.
  • Kahraman, N. (2014). An explanatory item response theory approach for a computer-based case simulation test. Eurasian Journal of Educational Research, 14(54), 117–134. https://doi.org/10.14689/ejer.2014.54.7
  • Kim, J., & Wilson, M. (2020). Polytomous item explanatory item response theory models. Educational and Psychological Measurement, 80(4), 726-755.
  • Landis, J. R. ve Koch, G. G. (1977) "The measurement of observer agreement for categorical data", Biometrics. Cilt. 33, say. 159-174
  • Kaiser, H. F. (1960). The application of electronic computers to factor analysis. Educational and Psychological Measurement, 20(1), 141–151.
  • Min, H., Zickar, M., & Yankov, G. (2018). Understanding item parameters in personality scales: An explanatory item response modeling approach. Personality and Individual Differences, 128, 1–6. https://doi.org/10.1016/j.paid.2018.02.012
  • Orlando, M., & Thissen, D. (2000). Likelihood-based item-fit indices for dichotomous Item Response Theory models. Applied Psychological Measurement, 24(1), 24-50
  • Petscher, Y., Compton, D. L., Steacy, L., & Kinnon, H. (2020). Past perspectives and new opportunities for the explanatory item response model. Annals of Dyslexia, 70(2), 160-179.
  • Randall, J., Cheong, Y. F., & Engelhard, G. (2010). Using explanatory item response theory modeling to investigate context effects of differential item functioning for students with disabilities. Educational and Psychological Measurement, 71(1), 129–147.
  • Sijtsma, K. (2020). Measurement models for psychological attributes: Classical test theory, factor analysis, item response theory, and latent class models. CRC Press.
  • Tat, O. (2020). Açıklayıcı Madde Tepki Modellerinin Bilgisayar Ortamında Bireye Uyarlanmış Testlerde Kullanımı. [Doktora Tezi]. Hacettepe Üniversitesi, Eğitim Bilimleri Enstitüsü, Ankara.
  • Yavuz, H. C. (2019). The effects of log data on students’ performance. Journal of Measurement and Evaluation in Education and Psychology, 10(4), 378-390.
  • Yen, W. M. (1981). Using simulation results to choose a latent trait model. Applied Psychological Measurement, 5, 245–262.
  • Yücel, Z., & Koç, M. (2011). İlköğretim öğrencilerinin matematik dersine karşı tutumlarının başarı düzeylerini yordama gücü ile cinsiyet arasındaki ilişki. İlköğretim Online, 10(1), 133-143.
Year 2023, Volume: 20 Issue: Human Behavior and Social Institutions, 1029 - 1055, 30.10.2023
https://doi.org/10.26466/opusjsr.1347612

Abstract

References

  • Atar, B. (2011). Tanımlayıcı ve açıklayıcı madde tepki modellerinin TIMSS 2007 Türkiye matematik verisine uyarlanması. Eğitim ve Bilim, 36(159).
  • Atar, B., & Aktan, D. Ç. (2013). Birey açıklayıcı madde tepki kuramı analizi: örtük regresyon iki parametreli lojistik modeli. Eğitim ve Bilim, 38(168).
  • Baker, F. B. (2001). The basics of item response theory. http://ericae. net/irt/baker.
  • Berberoğlu G. ve Kalender İ. (2005). Öğrenci Başarısının Yıllara, Okul Türlerine, Bölgelere Göre İncelenmesi: ÖSS ve PISA Analizi, ODTÜ Eğitim Bilimleri ve Uygulama Dergisi, Sayfa 27-28.
  • Blozis, S. A., Conger K. J., & Harring, J. R. (2007). Nonlinear latent curve models for multivariate longitudinal data. International Journal of Behavioral Development: Special Issue on Longitudinal Modeling of Developmental Processes, 31, 340-346
  • Boeck, P. de, Cho, S. J., & Wilson, M. (2011). Explanatory secondary dimension modeling of latent differential item functioning. Applied Psychological Measurement, 35(8), 583–603.
  • Boeck, P. de., &Wilson, M. (2004). Explanatory item response models. New York, NY: Springer New York.
  • Briggs, D. C. (2008). Using explanatory item response models to analyze group differences in science achievement. Applied Measurement in Education, 21(2), 89–118.
  • Bulut, O. (2021). eirm: Explanatory item response modeling for dichotomous and polytomous item responses, R package version 0.4. doi: 10.5281/zenodo.4556285 Available from https://CRAN.R-project.org/package=eirm.
  • Bulut, O., Palma, J., Rodriguez, M. C., & Stanke, L. (2015). Evaluating measurement invariance in the measurement of developmental assets in Latino English language groups across developmental stages. Sage Open, 5(2), 2158244015586238.
  • Büyükkıdık, S., & Bulut, O. (2022). Analyzing the Effects of Test, Student, and School Predictors on Science Achievement: An Explanatory IRT Modeling Approach. Journal of Measurement and Evaluation in Education and Psychology, 13(1), 40-53.
  • Cheema, J. R., & Galluzzo, G. (2013). Analyzing the gender gap in math achievement: Evidence from a large-scale US sample. Research in Education, 90(1), 98-112.
  • Chen, F., Yang, H., Bulut, O., Cui, Y., & Xin, T. (2019). Examining the relation of personality factors to substance use disorder by explanatory item response modeling of DSM-5 symptoms. PloS One, 14(6), e0217630. https://doi.org/10.1371/journal.pone.0217630
  • Chen, W. H., & Thissen, D. (1997). Local dependence indexes for item pairs using item response theory. Journal of Educational and Behavioral Statistics, 22(3), 265-289.
  • Chiu, T. (2016). Using Explanatory Item Response Models to Evaluate Complex Scientific Tasks Designed for the Next Generation Science Standards (Doctoral dissertation, UC Berkeley).
  • Crocker, L., & Algina, J. (1986). Introduction to classical and modern test theory. Holt, Rinehart and Winston, 6277 Sea Harbor Drive, Orlando, FL 32887.
  • De Ayala, R. J. (2013). The theory and practice of item response theory. Guilford Publications.
  • De Ayala, R. J. (2022). The theory and practice of item response theory, Second Edition. Guilford Publications.
  • DeMars, C. (2010). Item response theory. Oxford University Press. Desjardins, C. D., & Bulut, O. (2018). Handbook of educational measurement and psychometrics using R. CRC Press.
  • Ellison, G., & Swanson, A. (2018). Dynamics of the gender gap in high math achievement (No. w24910). National Bureau of Economic Research.
  • Embretson, S. E., & Reise, S. P. (2000). Item response theory for psychologists. Maheah.
  • Fleiss,J.L.(1971) "Measuring nominal scale agreement among many raters." Psychological Bulletin, Cilt 76, Sayi 5 say. 378-382
  • Fraenkel, J. R., Wallen, N. E., & Hyun, H. H. (2012). How to design and evaluate research in education (8th edt.). New York: McGram-Hill Companies.
  • Hambleton, R. K., Swaminathan, H., & Rogers, H. J. (1991). Fundamentals of item response theory. Measurement methods for the social sciences series. Newbury Park, Calif.: Sage Publications.
  • Kahraman, N. (2014). An explanatory item response theory approach for a computer-based case simulation test. Eurasian Journal of Educational Research, 14(54), 117–134. https://doi.org/10.14689/ejer.2014.54.7
  • Kim, J., & Wilson, M. (2020). Polytomous item explanatory item response theory models. Educational and Psychological Measurement, 80(4), 726-755.
  • Landis, J. R. ve Koch, G. G. (1977) "The measurement of observer agreement for categorical data", Biometrics. Cilt. 33, say. 159-174
  • Kaiser, H. F. (1960). The application of electronic computers to factor analysis. Educational and Psychological Measurement, 20(1), 141–151.
  • Min, H., Zickar, M., & Yankov, G. (2018). Understanding item parameters in personality scales: An explanatory item response modeling approach. Personality and Individual Differences, 128, 1–6. https://doi.org/10.1016/j.paid.2018.02.012
  • Orlando, M., & Thissen, D. (2000). Likelihood-based item-fit indices for dichotomous Item Response Theory models. Applied Psychological Measurement, 24(1), 24-50
  • Petscher, Y., Compton, D. L., Steacy, L., & Kinnon, H. (2020). Past perspectives and new opportunities for the explanatory item response model. Annals of Dyslexia, 70(2), 160-179.
  • Randall, J., Cheong, Y. F., & Engelhard, G. (2010). Using explanatory item response theory modeling to investigate context effects of differential item functioning for students with disabilities. Educational and Psychological Measurement, 71(1), 129–147.
  • Sijtsma, K. (2020). Measurement models for psychological attributes: Classical test theory, factor analysis, item response theory, and latent class models. CRC Press.
  • Tat, O. (2020). Açıklayıcı Madde Tepki Modellerinin Bilgisayar Ortamında Bireye Uyarlanmış Testlerde Kullanımı. [Doktora Tezi]. Hacettepe Üniversitesi, Eğitim Bilimleri Enstitüsü, Ankara.
  • Yavuz, H. C. (2019). The effects of log data on students’ performance. Journal of Measurement and Evaluation in Education and Psychology, 10(4), 378-390.
  • Yen, W. M. (1981). Using simulation results to choose a latent trait model. Applied Psychological Measurement, 5, 245–262.
  • Yücel, Z., & Koç, M. (2011). İlköğretim öğrencilerinin matematik dersine karşı tutumlarının başarı düzeylerini yordama gücü ile cinsiyet arasındaki ilişki. İlköğretim Online, 10(1), 133-143.
There are 37 citations in total.

Details

Primary Language English
Subjects Strategy, Management and Organisational Behaviour (Other)
Journal Section Research Articles
Authors

Şayan Berber 0000-0002-7897-7335

Esin Can 0000-0003-1754-4867

Early Pub Date October 26, 2023
Publication Date October 30, 2023
Published in Issue Year 2023 Volume: 20 Issue: Human Behavior and Social Institutions

Cite

APA Berber, Ş., & Can, E. (2023). The Role Of Networks In The Early Internationalization Of Emerging Market Firms: Evidence From Turkish Textile-Born Globals with Social-Business Networks Perspective. OPUS Journal of Society Research, 20(Human Behavior and Social Institutions), 1029-1055. https://doi.org/10.26466/opusjsr.1347612