Nội dung được dịch bởi AI, chỉ mang tính chất tham khảo
Đánh giá tác động của rủi ro liên quan đến COVID-19 đối với hành vi mua sắm trực tuyến
Tóm tắt
Trong thời gian đại dịch và giãn cách xã hội, những rủi ro liên quan đến việc ra ngoài để mua sắm có thể khiến người tiêu dùng tìm kiếm các phương tiện trực tuyến để thực hiện các hoạt động đó. Trong bối cảnh này, nghiên cứu nhằm phân tích ảnh hưởng của COVID-19 đến hành vi mua sắm trực tuyến. Để thực hiện điều này, chúng tôi đã tiến hành một cuộc khảo sát với 1052 người tiêu dùng trực tuyến tại Brazil, với dữ liệu được phân tích thông qua PLS-SEM. Kết quả chính cho thấy rằng rủi ro bị nhiễm COVID-19 khi mua sắm trực tiếp có tác động tích cực đến tính hữu ích và sự dễ dàng khi mua hàng. Tuy nhiên, nó không có ảnh hưởng thống kê đến ý định mua sắm trực tuyến; tính hữu ích được cảm nhận có mối quan hệ tích cực với ý định mua hàng trực tuyến; và sự dễ dàng của việc đầu tư có mối liên hệ tích cực đáng kể với tính hữu ích và ý định mua hàng trực tuyến. Ý định mua sắm trực tuyến có ảnh hưởng tích cực đến việc mua sắm trực tuyến. Nghiên cứu đóng góp vào tài liệu bằng cách cung cấp các kết quả thực nghiệm sử dụng TAM và COVID-19 như một biến mô hình bên ngoài.
Từ khóa
#COVID-19 #hành vi mua sắm trực tuyến #rủi ro #tính hữu ích #tính dễ dàng khi mua hàng #TAMTài liệu tham khảo
Addo, P.C., F. Jiaming, N.B. Kulbo, and L. Liangqiang. 2020. COVID-19: Fear appeal favoring purchase behavior towards personal protective equipment. The Service Industries Journal 40 (7–8): 471–490.
Arora, S., and S. Sahney. 2018. Antecedents to consumers' showrooming behavior: An integrated TAM-TPB framework. Journal of Consumer Marketing.
Brewer, P., and A.G. Sebby. 2021. The effect of online restaurant menus on consumers’ purchase intentions during the COVID-19 pandemic. International Journal of Hospitality Management 94: 102777.
Çelik, H.E., and V. Yilmaz. 2011. Extending the technology acceptance model for adoption of e-shopping by consumers in Turkey. Journal of Electronic Commerce Research 12 (2): 152.
Chi, T. 2018. Understanding Chinese consumer adoption of apparel mobile commerce: An extended TAM approach. Journal of Retailing and Consumer Services 44: 274–284.
Chin, W.W. 1998. The partial least squares approach to structural equation modeling. Modern Methods for Business Research 295 (2): 295–336.
Chiu, Y.B., C.P. Lin, and L.L. Tang. 2005. Gender differs: assessing a model of online purchase intentions in e‐tail service. International Journal of Service Industry Management 16(5).
Chiu, C.M., E.T. Wang, Y.H. Fang, and H.Y. Huang. 2014. Understanding customers’ repeat purchase intentions in B2C e-commerce: The roles of utilitarian value, hedonic value, and perceived risk. Information Systems Journal 24 (1): 85–114.
Chu, K.M. 2018. Mediating influences of attitude on internal and external factors influencing consumers’ intention to purchase organic foods in China. Sustainability 10 (12): 4690.
Cohen, J. 2013. Statistical power analysis for the behavioral sciences. Cambridge: Academic Press.
Davis, F.D., and V. Venkatesh. 1996. A critical assessment of potential measurement biases in the technology acceptance model: Three experiments. International Journal of Human-Computer Studies 45 (1): 19–45.
Fayad, R., and D. Paper. 2015. The technology acceptance model e-commerce extension: A conceptual framework. Procedia Economics and Finance 26: 1000–1006.
Fedorko, I., R. Bacik, and B. Gavurova. 2018. Technology acceptance model in e-commerce segment. Management & Marketing 13 (4): 1242–1256.
Fornell, C., and D.F. Larcker. 1981. Evaluating structural equation models with unobservable variables and measurement error. Journal of Marketing Research 18 (1): 39–50.
Fortes, N., and P. Rita. 2016. Privacy concerns and online purchasing behaviour: Towards an integrated model. European Research on Management and Business Economics 22 (3): 167–176.
Hair, J.F., Jr., G.T.M. Hult, C. Ringle, and M. Sarstedt. 2017. A primer on partial least squares structural equation modeling (PLS-SEM). Los Angeles: Sage Publications.
Hair, J.F., Jr., C.M. Ringle, and M. Sarstedt. 2011. PLS-SEM: Indeed a silver bullet. Journal of Marketing Theory and Practice 19 (2): 139–152.
Hair, J.F., Jr., J.J. Risher, M. Sarstedt, and C.M. Ringle. 2019. When to use and how to report the results of PLS-SEM. European Business Review 31 (1): 2–24.
Hair, J.F., Jr., M. Sarstedt, C.M. Ringle, and J.A. Mena. 2012. An assessment of the use of partial least squares structural equation modeling in marketing research. Journal of the Academy of Marketing Science 40 (3): 414–433.
Henseler, J., et al. 2014. Common beliefs and reality about partial least squares: Comments on Rönkkö and Evermann. Organizational Research Methods 17 (2): 182–209.
Isaac, O., Z. Abdullah, T. Ramayah, and A.M. Mutahar. 2017. Internet usage within government institutions in Yemen: An extended technology acceptance model (TAM) with Internet self-efficacy and performance impact. Science International 29 (4): 737–747.
Ishfaq, N., and H. Mengxing. 2021. Consumer usage behavior of internet-based services (IBS) in Pakistan during COVID-19 crisis from the perspective of technology acceptance model. Environmental Science and Pollution Research 1–16.
Kantar. 2020. COVID-19: Impacts on consumption and brands. Webinar Kantar Brazil. https://br.kantar.com/covid-19
Kemp, S. 2020. Digital around the world in April 2020. We are Social. https://wearesocial.com/blog/2020/04/digital-around-the-world-in-april-2020
Kietzmann, J.H., K. Hermkens, I.P. McCarthy, and B.S. Silvestre. 2011. Social media? Get serious! Understanding the functional building blocks of social media. Business Horizons 54 (3): 241–251.
Kim, J.B. 2012. An empirical study on consumer first purchase intention in online shopping: Integrating initial trust and TAM. Electronic Commerce Research 12 (2): 125–150.
Kim, R.Y. 2020. The Impact of COVID-19 on consumers: Preparing for digital sales. IEEE Engineering Management Review.
Kim, S.S., J. Kim, F. Badu-Baiden, M. Giroux, and Y. Choi. 2021. Preference for robot service or human service in hotels? Impacts of the COVID-19 pandemic. International Journal of Hospitality Management, 93.
Koufaris, M. 2002. Applying the technology acceptance model and flow theory to online consumer behavior. Information Systems Research 13 (2): 205–223.
Law, M., R.C.W. Kwok, and M. Ng. 2016. An extended online purchase intention model for middle-aged online users. Electronic Commerce Research and Applications 20: 132–146.
Lee, D.Y., and M.R. Lehto. 2013. User acceptance of YouTube for procedural learning: An extension of the Technology Acceptance Model. Computers and Education 61: 193–208.
Manis, K.T., and D. Choi. 2019. The virtual reality hardware acceptance model (VR-HAM): Extending and individuating the technology acceptance model (TAM) for virtual reality hardware. Journal of Business Research 100: 503–513.
Moslehpour, M., V.K. Pham, W.K. Wong, and İ Bilgiçli. 2018. E-purchase intention of Taiwanese consumers: Sustainable mediation of perceived usefulness and perceived ease of use. Sustainability 10 (1): 234.
Nguyen, H.V., H.X. Tran, L. Van Huy, X.N. Nguyen, M.T. Do, and N. Nguyen. 2020. Online book shopping in Vietnam: The impact of the COVID-19 pandemic situation. Publishing Research Quarterly 100: 1–9.
Ringle, C.M., D. Da Silva, and D.D.S. Bido. 2014. Modeling of structural equations using SmartPLS. Revista Brasileira De Marketing 13 (2): 56–73.
Sheth, J. 2020. Impact of Covid-19 on consumer behavior: will the old habits return or die? Journal of Business Research 117: 280–283.
Shim, J., J. Moon, M. Song, and W.S. Lee. 2021. Antecedents of purchase intention at Starbucks in the context of covid-19 pandemic. Sustainability 13 (4): 1758.
Silva, L.E.N., M.B. Gomes Neto, R. da Grangeiro, and R., and Nadae, J. de. 2021. COVID-19 pandemic: Why does it matter for consumer research? Brazilian Journal of Marketing 20 (2): 253–278.
Sukno, R., and I. Pascual del Riquelme. 2019. E-Commerce C2C en Chile: Incorporación de la Reputación y de la Confianza en el TAM. Journal of Technology Management and Innovation 14 (3): 72–81.
Sun, L. 2017. The timing game of new disruptive technology adoption under competition. In Academy of Management Proceedings, no. 1, p. 13270. New York: Academy of Management.
Tong, X. 2010. A cross-national investigation of an extended technology acceptance model in the online shopping context. International Journal of Retail and Distribution Management 38 (10): 742–759. https://doi.org/10.1108/09590551011076524.
Troise, C., A. O’Driscoll, M. Tani, and A. Prisco. 2021. Online food delivery services and behavioural intention—A test of an integrated TAM and TPB framework. British Food Journal 123 (2): 664–683.
Ventre, I., and Kolbe, D. (2020). The Impact of Perceived Usefulness of Online Reviews, Trust and Perceived Risk on Online Purchase Intention in Emerging Markets: A Mexican Perspective. Journal of International Consumer Marketing, 1–13.
Wang, Y., and C. Herrando. 2019. Does privacy assurance on social commerce sites matter to millennials? International Journal of Information Management 44: 164–177.
Wang, Y., A. Hong, X. Li, and J. Gao. 2020. Marketing innovations during a global crisis: A study of China firms’ response to COVID-19. Journal of Business Research 116: 214–220.
Wee, C.S., M.S.B.M. Ariff, N. Zakuan, M.N.M. Tajudin, K. Ismail, and N. Ishak. 2014. Consumers perception, purchase intention and actual purchase behavior of organic food products. Review of Integrative Business and Economics Research 3 (2): 378.
Who. 2020. Coronavirus disease (COVID-19) Situation Report—176. https://www.who.int/docs/default-source/coronaviruse/situation-reports/20200714-covid-19-sitrep-176.pdf?sfvrsn=d01ce263_2
Yan, Y., S. Zhong, J. Tian, and N. Jia. 2020. An empirical study on consumer automobile purchase intentions influenced by COVID-19. SSRN 3593963.
Zhang, X., S. Liu, X. Chen, L. Wang, B. Gao, and Q. Zhu. 2018. Health information privacy concerns, antecedents, and information disclosure intention in online health communities. Information & Management 55 (4): 482–493.
