INVESTIGATING CONSUMER BEHAVIOR IN ONLINE SHOPPING AMONG UNIVERSITY STUDENTS IN TWO COUNTRIES

Yıl 2020, Cilt: 7 Sayı: 1, 23 - 33, 30.03.2020

Abdulkadir Ozdemır Mohammad Naserınıa

https://doi.org/10.17261/Pressacademia.2020.1185

Öz

Purpose - The purpose of this study is to show how different countries, different cultures and technological infrastructures affect the behaviors of people via internet. In this study, the online shopping approach, which is part of e-commerce, has been studied among students from both countries. The technology aacceptance model was used to study the effective factors on student e-shopping behavior and this research is taken from the thesis defended in 2019.
Methodology – The data of thesis were collected from students at Ataturk University in Turkey and Tabriz University in Iran. Processing of collected data as well as proof of hypotheses have been made using the technology acceptance model.
Findings- Students' intention to accept online shopping technology has a significant impact on their actual behavior, which is 71.7% among students at the University of Ataturk and 67.6% among students of Tabriz University.
Conclusion- According to the results, it is seen that there is a significant proportion of both university students' intention and attitude about e-shopping. In addition, it has been observed that the technological infrastructure and usage periods have a positive effect on e-shopping.

Anahtar Kelimeler

E-commerce, online shopping, technology acceptance model

Kaynakça

• Abdullah, F., & Ward, R. (2016). Developing a General Extended Technology Acceptance Model for E-Learning (GETAMEL) by analysing commonly used external factors. Computers in Human Behavior, 56, 238-256.
• Agrebi, S., & Jallais, J. (2015). Explain the intention to use smartphones for mobile shopping. Journal of retailing and consumer services, 22, 16-23.
• Ahmad, S., Bhatti, S. H., & Hwang, Y. (2019). E-service quality and actual use of e-banking: Explanation through the Technology Acceptance Model. Information Development, 0266666919871611.
• Ahn, T., Ryu, S., & Han, I. (2004). The impact of the online and offline features on the user acceptance of Internet shopping malls. Electronic Commerce Research and Applications, 3(4), 405-420.
• Ajzen, I. (1991). The theory of planned behavior. Organizational behavior and human decision processes, 50(2), 179-211.
• Al-Ajam, A. S., & Nor, K. M. (2013). Internet banking adoption: integrating technology acceptance model and trust. European Journal of Business and Management, 5(3), 207-215.
• ALraja, M. N., & Aref, M. (2015). Customer acceptance of e-commerce: Integrating perceived risk with TAM. International Journal of Applied Business and Economic Research, 13(2), 913-921.
• Amirtha, R., & Sivakumar, V. J. (2018). Does family life cycle stage influence e-shopping acceptance by Indian women? An examination using the technology acceptance model. Behaviour & Information Technology, 37(3), 267-294.
• Barkhi, R., & Wallace, L. (2007). The impact of personality type on purchasing decisions in virtual stores. Information Technology and Management, 8(4), 313-330.
• Belanche, D., Casaló, L. V., & Flavián, C. (2012). Integrating trust and personal values into the Technology Acceptance Model: The case of e-government services adoption. Cuadernos de Economía y Dirección de la Empresa, 15(4), 192-204.
• Çelik, H. E., & Yilmaz, V. (2011). Extending the technology acceptance model for adoption of e-shopping by consumers in Turkey. Journal of Electronic Commerce Research, 12(2), 152.
• Chang, S. C., & Tung, F. C. (2008). An empirical investigation of students' behavioural intentions to use the online learning course websites. British Journal of Educational Technology, 39(1), 71-83.
• Cheema, U., Rizwan, M., Jalal, R., Durrani, F., & Sohail, N. (2013). The trend of online shopping in 21st century: Impact of enjoyment in TAM Model. Asian Journal of Empirical Research, 3(2), 131-141.
• Chen, L. D., Gillenson, M. L., & Sherrell, D. L. (2004). Consumer acceptance of virtual stores: A theoretical model and critical success factors for virtual stores. ACM Sigmis Database, 35(2), 8–31.
• Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340.
• Davis, F. D. (1989). Perceived usefulness, perceived ease of use, and user acceptance of information technology. MIS quarterly, 319-340.
• Fayad, R., & Paper, D. (2015). The technology acceptance model e-commerce extension: a conceptual framework. Procedia Economics and Finance, 26, 1000-1006.
• Granić, A., & Marangunić, N. (2019). Technology acceptance model in educational context: A systematic literature review. British Journal of Educational Technology, 50(5), 2572-2593.
• Ha, S., & Stoel, L. (2009). Consumer e-shopping acceptance: Antecedents in a technology acceptance model. Journal of business research, 62(5), 565-571.
• Hanafizadeh, P., Behboudi, M., Koshksaray, A. A., & Tabar, M. J. S. (2014). Mobile-banking adoption by Iranian bank clients. Telematics and Informatics, 31(1), 62-78.
• Harris, L. C., & Goode, M. M. (2004). The four levels of loyalty and the pivotal role of trust: a study of online service dynamics. Journal of retailing, 80(2), 139-158. Hsiao, C. H., & Yang, C. (2011). The intellectual development of the technology acceptance model: A co-citation analysis. International Journal of Information Management, 31(2), 128-136.
• Ingham, J., Cadieux, J., & Berrada, A. M. (2015). e-Shopping acceptance: A qualitative and meta-analytic review. Information & Management, 52(1), 44-60.
• 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.
• Jiang, L., Yang, Z., & Jun, M. (2013). Measuring consumer perceptions of online shopping convenience. Journal of Service Management, 24(2), 191-214.
• Johnson, D. G. (2015). Technology with no human responsibility?. Journal of Business Ethics, 127(4), 707-715.
• Kumar, V., Anand, P., & Mutha, D. (2016). A study on trust in online shopping in Pune: A comparative study between male and female shoppers. Prerna and Mutha, Devendra, A Study on Trust in Online Shopping in Pune: A Comparative Study between Male and Female Shoppers (February 12, 2016).
• Lee, Y. C. (2006). An empirical investigation into factors influencing the adoption of an e-learning system. Online information review, 30(5), 517-541.
• Li, R., Chung, T. L. D., & Fiore, A. M. (2017). Factors affecting current users’ attitude towards e-auctions in China: An extended TAM study. Journal of Retailing and Consumer Services, 34, 19-29.
• Mahadeo, J. D. (2009). Towards an Understanding of the Factors Influencing the Acceptance and Diffusion of e-Government Services. Electronic Journal of E-government, 7(4), 391-402.
• Mandhlazi, L., Dhurup, M., & Mafini, C. (2013). Generation Y consumer shopping styles: evidence from South Africa. Mediterranean Journal of Social Sciences, 4(14), 153.
• Ngai, E. W., Poon, J. K. L., & Chan, Y. H. (2007). Empirical examination of the adoption of WebCT using TAM. Computers & education, 48(2), 250-267.
• Oliveira, T., Alhinho, M., Rita, P., & Dhillon, G. (2017). Modelling and testing consumer trust dimensions in e-commerce. Computers in Human Behavior, 71, 153-164.
• Oliveira, T., Faria, M., Thomas, M. A., & Popovič, A. (2014). Extending the understanding of mobile banking adoption: When UTAUT meets TTF and ITM. International journal of information management, 34(5), 689-703.
• Oni, A. A., & Ayo, C. K. (2010). An empirical investigation of the level of users’ acceptance of e-banking in Nigeria. Journal of Internet Banking and Commerce, 15(1), 1-13.
• Pando-Garcia, J., Periañez-Cañadillas, I., & Charterina, J. (2016). “Business simulation games with and without supervision: An analysis based on the TAM model”. Journal of Business Research, 69(5), 1731-1736.
• Park, S. Y., Nam, M. W., & Cha, S. B. (2012). University students' behavioral intention to use mobile learning: Evaluating the technology acceptance model. British Journal of Educational Technology, 43(4), 592-605.
• Patel, K. J., & Patel, H. J. (2018). Adoption of internet banking services in Gujarat. International Journal of Bank Marketing.
• Praveena, K., & Thomas, S. (2014). Continuance intention to use Facebook: A study of perceived enjoyment and TAM. Bonfring International Journal of Industrial Engineering and Management Science, 4(1), 24-29.
• Richa, D. (2012). Impact of demographic factors of consumers on online shopping behaviour: A study of consumers in India. International Journal of Engineering and Management Sciences, 3(1), 43-52.
• Sánchez-Prieto, J. C., Olmos-Migueláñez, S., & García-Peñalvo, F. J. (2017). MLearning and pre-service teachers: An assessment of the behavioral intention using an expanded TAM model. Computers in Human Behavior, 72, 644-654.
• Singh, N., Yadav, M., & Sahu, O. (2016). Consumer acceptance of apparel e-commerce–Ethiopia. Intellectual Economics, 10(1), 55-62.
• Singh, S., & Srivastava, P. (2019). Social media for outbound leisure travel: a framework based on technology acceptance model (TAM). Journal of Tourism Futures.
• Suh, B., & Han, I. (2002). Effect of trust on customer acceptance of Internet banking. Electronic Commerce and Applications, 1(3), 247-263.
• Suki, N. M., & Ramayah, T. (2010). User acceptance of the e-government services in Malaysia: structural equation modelling approach. Interdisciplinary Journal of Information, Knowledge and Management, 5, 395-414.
• Suleman, D., Zuniarti, I., Setyaningsih, E. D., Yanti, V. A., Susilowati, I. H., Sari, I., ... & Lestiningsih, A. S. (2019). Decision Model Based on Technology Acceptance Model (Tam) for Online Shop Consumers in Indonesia. Academy of Marketing Studies Journal.
• Teo, T. (2010). A path analysis of pre-service teachers' attitudes to computer use: applying and extending the technology acceptance model in an educational context. Interactive Learning Environments, 18(1), 65-79.
• Teo, T. (2012). Examining the intention to use technology among pre-service teachers: An integration of the technology acceptance model and theory of planned behavior. Interactive Learning Environments, 20(1), 3-18.
• Thong, J. Y., Hong, S. J., & Tam, K. Y. (2006). The effects of post-adoption beliefs on the expectation-confirmation model for information technology continuance. International Journal of Human-Computer Studies, 64(9), 799-810.
• Venkatesh, V., & Bala, H. (2008). Technology acceptance model 3 and a research agenda on interventions. Decision sciences, 39(2), 273-315.
• Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS quarterly, 425-478.
• Venkatesh, V., Thong, J. Y., & Xu, X. (2012). Consumer acceptance and use of information technology: extending the unified theory of acceptance and use of technology. MIS quarterly, 36(1), 157-178.
• Wang, F., & Head, M. (2007). How can the web help build customer relationships?: an empirical study on e-tailing. Information & Management, 44(2), 115-129.
• Wang, T. L., & Tseng, Y. F. (2011). A study of the effect on trust and attitude with online shopping. International Journal of Digital Society, 2(2), 433-440.
• Wang, X., & Goh, D. H. L. (2017). Video game acceptance: a meta-analysis of the extended technology acceptance model. Cyberpsychology, Behavior, and Social Networking, 20(11), 662-671.
• Weng, F., Yang, R. J., Ho, H. J., & Su, H. M. (2018). A TAM-based study of the attitude towards use intention of multimedia among school teachers. Applied System Innovation, 1(3), 36.
• Yoon, C. (2009). The effects of national culture values on consumer acceptance of e-commerce: Online shoppers in China. Information & Management, 46(5), 294-301.