TÜRKİYE'NİN DOĞU BÖLGESİNDE E-TİCARET KULLANIMINI ETKİLEYEN FAKTÖRLERİN KATEGORİK VERİ ANALİZİ İLE BELİRLENMESİ

Year 2021, Volume: 8 Issue: Special Issue on International Symposium of Sustainable Logistics, 22 - 36, 23.09.2021

Ömer Alkan Şeyda Ünver

Abstract

E-ticaret, bilgisayar ağları üzerinden ticari faaliyetlerin yürütülmesi, işlem görmesi ve olanak tanınması olarak tanımlanabilir. E-ticaret, küresel ölçekte ticaretin serbestleştirilmesinin yanı sıra, bilgi iletişimini daha da kolaylaştıran son yıllarda tanık olunan en son teknolojik gelişmelerin bir ürünüdür. Bu çalışmanın amacı, Türkiye’de doğu bölgesinde yaşayan bireylerin e-ticaret kullanım farklılıklarını araştırmak ve bireylerin demografik, ekonomik ve kişisel özellikleri ile e-ticaret kullanımı arasındaki ilişkiyi belirlemektir.
Çalışmada, Hanehalkı Bilişim Teknolojileri Kullanım Araştırmasından elde edilen mikro veri seti kullanılmıştır. Araştırmanın örnekleme yöntemi 2 aşamalı tabakalı küme örneklemesidir. Bireylerin e-ticaret kullanımı ile ilişkili faktörlerin belirlenmesi için binary logistic regresyon analizi kullanılmıştır. Çalışmaya göre doğu bölgesinde geliri ₺6001 ve üstünde olan bir bireyin referans gruba (₺2000 ve altı) göre e-ticaret kullanma olasılığı 54.5% daha fazladır.
Çalışmanın sonucunda eğitim durumu, gelir düzeyi, yaş, cinsiyet, meslek, sosyal medya kullanımı, internette mal ve hizmetler hakkında bilgi arama faaliyetinde bulunma, internette mal veya hizmet satışı faaliyetinde bulunma, internet bankacılığı kullanımı, e- devlet kullanımı, hanedeki bilişim ekipmanı sayısı ve hanehalkı büyüklüğü değişkenlerinin e-ticaret kullanımıyla ilişkili olduğu tespit edilmiştir.
Çalışmanın sonucuna göre gelişmişlik düzeyi düşük olan bölgelerde e-ticaretin geliştirilmesi kolaylaştırılarak ve bu bölgelerde bilgi ve iletişim teknolojisi altyapısı iyileştirilerek internet kullanımının yaygınlaştırılması gerekmektedir. Dolayısıyla e-ticaret ile ilgili bilgilere ulaşmada bölgeye özgü müdahalelerin hesaba katılması gerekmektedir.

Keywords

elektronik ticaret, online alış veriş, online satın alma, e-ticaret, Türkiye, binary lojistik regresyon

DETERMINATION OF FACTORS THAT AFFECT USE OF E-COMMERCE IN EASTERN TURKEY THROUGH CATEGORICAL DATA ANALYSIS

Abstract

E-commerce can be defined as carrying out, handling and facilitating commercial activities over computer networks. E-commerce is an output of the latest technological developments witnessed in recent years that further facilitate free trade on a global scale as well as communication of information. This study aimed to investigate the differences in the use of e-commerce by individuals living in eastern part of Turkey and determine the relationship between demographic, economic and personal characteristics of individuals and use of e-commerce.
Microdata set obtained from Household Information Technologies Use Survey was used in the study. Sampling method employed in the study was stratified 2-stage cluster sampling. Binary logistic regression analysis was used to determine factors associated with the individuals’ use of e-commerce. According to the study, the likelihood of an individual with an income level of ₺6001 and above in the eastern region to use e-commerce was found to be 54.5% higher compared to the reference group (₺2000 and below).
As a result of the study, variables such as income level, age, gender, occupation, use of social media, searching for information on goods and services on the internet, selling goods or services on the internet, use of internet banking, use of e-government, number of information equipment available in the household and household size were found to be associated with the use-of e-commerce.
Considering the findings of the study, it is necessary to make internet use widespread by facilitating development of e-commerce in less developed regions and improving infrastructure for information and communication technology in these regions. Therefore, interventions specific to region need to be taken into consideration for access to information on e-commerce.

Keywords

electronic commerce, online shopping, online purchase, e-commerce, Turkey, binary logistic regression

References

• Abar, H., & Alkan, Ö. (2020). What Factors Influence the Use of Electronic Commerce?: A Case in Turkey. In E. C. Idemudia (Ed.), Handbook of Research on IT Applications for Strategic Competitive Advantage and Decision Making (pp. 101-117): IGI Global
• Afsar, B., Qureshi, J. A., Rehman, A., & Bangash, R. U. (2011). Consumer panacea over internet usage in Pakistan. Management & Marketing Journal, 9(1), 43-52.
• Akman, I., & Mishra, A. (2010). Gender, age and income differences in internet usage among employees in organizations. Computers in Human Behavior, 26, 482–490.
• Akman, I., & Rehan, M. (2014). Online purchase behaviour among professionals: a socio-demographic perspective for Turkey. Economic ResearchEkonomska Istraživanja, 27(1), 689-699.
• Alkan, Ö., & Abar, H. (2020). Determination of factors influencing tobacco consumption in Turkey using categorical data analyses1. Archives of Environmental & Occupational Health, 75(1), 27-35. Alkan, Ö., & Ünver, Ş. (2020). Determinants of domestic physical violence against women in Turkey. Humanities & Social Sciences Reviews, 8(6), 55-67.
• Alkan, Ö., Oktay, E., & Ünver, Ş. (2020). Üniversite Gençliğinin Şiddete Eğiliminin Araştırılması. Ankara: Nobel Bilimsel Eserler.
• Alkan, Ö., Oktay, E., Ünver, Ş., & Gerni, E. (2020). Determination of Factors Affecting the Financial. Asian Economic and Financial Review Literacy of University Students in Eastern Anatolia;10(5), 536-546.
• Alqahtani, A. S., Goodwin, R. D., & de Vries, D. B. (2018). Cultural factors influencing e-commerce usability in Saudi Arabia. International Journal of Advanced and Applied Sciences, 5(6), 1-10.
• Barnes, S. J. and Richard T. V. (2002). An integrative approach to the assessment of e-commerce quality. Journal of Electronic Commerce Research., 3, 114-127.
• Beneke, J., Scheffer, M., & Du, W. (2010). Beyond Price – An Exploration into the Factors That Drive Young Adults to Purchase Online. International Journal of Marketing Studies, 2(2), 212-222.
• Bhatnagar, A., & Ghose, S. (2004). A latent class segmentation analysis of e-shoppers. Journal of Business Research, 57, 758-67.
• Changchit, C., Cutshall, R., Lonkani, R., Pholwan, K. & Pongwiritthon, R. (2019). Determinants of Online Shopping Influencing Thai Consumer’s Buying Choices. Journal of Internet Commerce, 18(1), 1-23.
• Cheung, C. M. K., Chan, G. W. W., & Limayem, M. A. (2005). Critical review of online consumer behavior: Empirical research. Journal of Electronic Commerce in Organizations, 3(1), 1–19.
• Corbitt, B. J., Thanasankit, T. and Yi, H. (2003). Trust and e-commerce: a study of consumer perceptions. Electronic commerce research and applications, 2, 203-215.
• Cristóbal-Fransi, E., Martín-Fuentes, E., & Daries-Ramon, N. (2015). Behavioural analysis of subjects interacting with information technology: categorising the behaviour of e-consumers. International Journal of Services Technology and Management, 21(1-3), 163-182.
• Çera, G., Phan, Q. P. T., Androniceanu, A., & Çera, E. (2020). Financial Capability and Technology Implications for Online Shopping. E&M Economics and Management, 23(2), 156–172.
• Donnermeyer, J.F. & Hollifield, C.A. (2003). Digital divide evidence in four rural towns. IT & Society, 1(4), 107-17.
• Duroy, D., Gorse, P., & Lejoyeux, M. (2014). Characteristics of online compulsive buying in Parisian students. Addictive Behaviors, 39(12), 1827–1830.
• Eurostatstatistics. (2018). E-commerce statistics for individuals. Retrieved 28.03.2020, from https://ec.europa.eu/eurostat/statistics-explained/index.php?title=E commerce_statistics_for_individuals#Context
• Gezici F., & Geoffrey J. D. Hewings (2007). Spatial Analysis of Regional Inequalities in Turkey. European Planning Studies, 15(3), 383-403.
• Global E-Commerce Report (2018). E-commerce Foundation, www.ecommercefoundation.org, 9-158
• Gökmen, A. (2012). Virtual business operations, e-commerce & its significance and the case of Turkey: current situation and its potential. Electron Commer Res 12, 31–51.
• Gümüş, R., & Kısa, A. (2016). Proceedings of SOCIOINT 2016 3rd International Conference on Education, Social Sciences and Humanities. Analysis of Usability of Web Sites in Hospitals in Diyarbakir, Turkey, (s. 246-249). Istanbul.
• Hashim, A., Ghani, E. K., & Said, J. (2009). Does consumers’ demographic profile influence online shopping?: An examination using Fishbein’s theory. Canadian Social Science, 5(6), 19-31.
• Hossein, K. M., Nishikitani, M., Kitaoka, H., Yokota, F., Islam, R., Okajima, H., . . . Ahmed, A. (2017). Factors Associated to Online Shopping at the BoP Community in Rural Bangladesh. International Journal of Advanced Computer Science and Applications, 8(10), 46-51.
• Huseynov, F., & Yıldırım, S.O. (2016). Internet users’ attitudes toward business-to-consumer online shopping: A survey. Information Development, 32(3), 452–465.
• Hwang, W., Jung, H.-S. & Salvendy. G. (2006). Internationalisation of e-commerce: a comparison of online shopping preferences among Korean, Turkish and US populations. Behaviour & Information Technology, 25(1), 3-18.
• Javadi, M.H.M., Dolatabadi, H.R., Nourbakhsh, M., Poursaeedi, A., & Asadollahi, A.R. (2012). An Analysis of Factors Affecting on Online Shopping Behavior of Consumers. International Journal of Marketing Studies, 4(5), 81-98.
• Kaya, B., Behravesh, E., Abubakar, A.M., Kaya O.S., & Orús, C. (2019). The Moderating Role of Website Familiarity in the Relationships Between eService Quality, e-Satisfaction and e-Loyalty. Journal of Internet Commerce, 18(4), 369-394.
• Lee, R. J., Sener, I. N., Mokhtarian, P. L. and Handy. S. L. (2017). Relationships between the online and in-store shopping frequency of Davis, California residents. Transportation Research Part A: Policy and Practice, 100, 40-52.
• Lightner, N. J. (2003). What users want in e-commerce design: effects of age, education and income. Ergonomics, 46(1-3), 153-168.
• Mills, B.F., & Whitacre, B.E. (2003). Understanding the non-metropolitan – metropolitan digital divide. Growth and Change, 34 (2), 219-43.
• Potosky, D. (2007). The Internet knowledge measure. Computers in HumanBehavior, 23, 2760–2777.
• Pucci, T., Casprini, E., Nosi, C., & Zanni, L. (2019). Does social media usage affect online purchasing intention for wine? The moderating role of subjective and objective knowledge. British Food Journal, 121(2), 275–288.
• Silahtaroğlu, G., & Dönertaşlı, H. (2015). Analysis and Prediction of E-Customers’ Behavior by Mining Clickstream Data. IEEE International Conference on Big Data (Big Data) 978-1-4799-9926-2/15/$31.00 1466-1472.
• Sim, L.L., & Koi, S.M. (2002). Singapore’s Internet shoppers and their impact on traditional shopping patterns. Journal of Retailing and Consumer Services, 9, 115–124.
• Shin, D.-H., & Biocca, F. (2017). Explicating user behavior toward multi-screen adoption and diffusion: User experience in the multi-screen media ecology. Internet Research, 27(2), 338–361.
• Stoklosa, M. (2020). Prices and cross-border cigarette purchases in the EU: Evidence from demand modelling. Tobacco Control, 29, 55-60.
• Stranahan, H. (2007). E‐tail spending patterns and the importance of online store familiarity. Internet Research, 17(4), 421-434.
• Tarafdar, M., & Vaidya, S. D. (2006). Challenges in the adoption of E-commerce technologies in India: The role of organizational factors. International Journal of Information Management, 26, 428–441.
• Thompson S. H. Teo & Vivien K. G. Lim (2000). Gender differences in internet usage and task preferences, Behaviour & Information Technology, 19(4), 283-295.
• TÜİK. (2019, 24.04.2021). Hanehalkı Bilişim Teknolojileri Kullanım Araştırması. from https://www.tuik.gov.tr/Kurumsal/Mikro_Veri#
• Tvrdoň, M., & Skokan, K. (2011). Regional disparities and the ways of their measurement: the case of the Visegrad Four countries. Technological and Economic Development of Economy, 17(3), 501–518
• Ünver, Ş., & Alkan, Ö. (2021). Determinants of e-Commerce Use at Different Educational Levels: Empirical Evidence from Turkey. International Journal of Advanced Computer Science and Applications;12(3), 40-49.
• Valarezo, A., Pérez-Amaral, T., Garín-Muñoz, T., García, I.H., and López, R. (2018). Drivers and barriers to cross-border e-commerce: evidence from Spanish individual behavior. Telecommunications Policy, 42, 464–473.
• Vicente, M. R. (2015). Determinants of C2C e-commerce: an empirical analysis of the use of online auction websites among Europeans. Applied Economics Letters, 22(12), 978-981.
• Yeh, J.C., Hsiao, K.L. & Yang, W.N. (2012). A study of purchasing behavior in Taiwan’s online auction websites: effects of uncertainty and gender differences. Internet Research, 22 (1), 98-115.
• Zhang, Y. (2005). Age, gender, and Internet attitudes among employees in the business world. Computers in Human Behavior, 21(1), 1–10.