Understanding online shopping adoption: The unified theory of acceptance and the use of technology with perceived risk in millennial consumers context

Authors

  • Rian Piarna Department of Information Management, Subang State Polytechnic, Subang
  • Ferdi Fathurohman Department of Agroindustry, Subang State Polytechnic, Subang
  • Nunu Nugraha Purnawan Department of Information Management, Subang State Polytechnic, Subang

DOI:

https://doi.org/10.31106/jema.v17i1.5050

Keywords:

UTAUT 2, Perceived Risk, Online Shopping, Adoption, Behavioral Intention, Use Behavior

Abstract

Online shopping is growing so rapidly and has attracted millennials in various way. Unfortunately, the discussion regarding the adoption of online shopping in millennial consumers’ context with perceived risk application was still limited. Therefore, the purpose of this study was to investigate the effect of performance expectancy, expectation efforts, social influence, facilitation conditions, hedonic motivation, price value, habits, and perceived risks on behavioral intentions and use behavior. This study also discusses the effect of perceived risks on financial risk, performance risk, and privacy risk. This study can be classified as explanatory research with purposive sampling and partial least square as sampling techniques and data analysis. This study was designed to focus on individuals who can be classified as an online shopper with a range of age of 18-35 years old. The results show that the millennial generation is influenced by the social environment and habits in shaping their behavioral intention. Millennial consumers are also proving very concerned about their perceived risk of financial, performance, and privacy issues when doing online shopping. Interestingly, six of the factors studied (performance expectancy, effort expectancy, facilitating conditions, hedonic motivation, price value, and perceived risk) do not have any influence on the intention to use online commerce technology.

References

Alalwan, A. A., Dwivedi, Y. K., & Rana, N. P. (2017). Factors influencing adoption of mobile banking by Jordanian bank customers: Extending UTAUT2 with trust. International Journal of Information Management, 37(3), 99-110. https://doi.org/10.1016/j.ijinfomgt.2017.01.002

Alalwan, A. A., Dwivedi, Y. K., Rana, N. P., & Algharabat, R. (2018). Examining factors influencing Jordanian customers’ intentions and adoption of internet banking: Extending UTAUT2 with risk. Journal of Retailing and Consumer Services, 40, 125–138. https://doi.org/10.1016/j.jretconser.2017.08.026

Amrullah, A., & Priyono, A. (2018). Integrasi aspek risiko dalam model unified theory of acceptance and usage of technology untuk menganalisis penerimaan teknologi go-ride. MIX: Jurnal Ilmiah Manajemen, 8(1), 33. https://doi.org/10.22441/mix.2018.v8i1.003

Buehler, P., & Maas, P. (2018). Consumer empowerment in insurance. International Journal of Bank Marketing, 36(6), 1073–1097. https://doi.org/10.1108/IJBM-12-2016-0182

Celik, H. (2016). Customer online shopping anxiety within the unified theory of acceptance and use technology (UTAUT) framework. Asia Pacific Journal of Marketing and Logistics, 28(2). https://doi.org/10.1108/APJML-05-2015-0077

Choi, J., Lee, A., & Ok, C. (2013). The effects of consumers’ perceived risk and benefit on attitude and behavioral intention: A study of street food. Journal of Travel and Tourism Marketing, 30(3), 222-237. https://doi.org/10.1080/10548408.2013.774916

Cimperman, M., Makovec BrenÄiÄ, M., & Trkman, P. (2016). Analyzing older users’ home telehealth services acceptance behavior—applying an extended UTAUT model. International Journal of Medical Informatics, 90, 22–31. https://doi.org/10.1016/j.ijmedinf.2016.03.002

Diño, M. J. S., & de Guzman, A. B. (2015). Using partial least squares (PLS) in predicting behavioral intention for telehealth use among filipino elderly. Educational Gerontology, 41(1), 53–68. https://doi.org/10.1080/03601277.2014.917236

Dwivedi, Y. K., Rana, N. P., Jeyaraj, A., Clement, M., & Williams, M. D. (2019). Re-examining the unified theory of acceptance and use of technology (UTAUT): towards a revised theoretical model. Information Systems Frontiers, 21(3), 719–734. https://doi.org/10.1007/s10796-017-9774-y

Farivar, S., Turel, O., & Yuan, Y. (2018). Skewing users’ rational risk considerations in social commerce: An empirical examination of the role of social identification. Information and Management, 55(8), 1038-1048. https://doi.org/10.1016/j.im.2018.05.008

Featherman, M. S., & Pavlou, P. A. (2003). Predicting e-services adoption: A perceived risk facets perspective. International Journal of Human Computer Studies, 59(4), 451–474. https://doi.org/10.1016/S1071-5819(03)00111-3

Gupta, A., & Dogra, N. (2017). Tourist adoption of mapping apps: a UTAUT 2 perspective of smart travellers. Tourism and Hospitality Management, 23(2), 145–161. https://doi.org/10.20867/thm.23.2.6

Han, M. C., & Kim, Y. (2017). Why consumers hesitate to shop online: perceived risk and product involvement on Taobao.com. Journal of Promotion Management, 23(1), 24–44. https://doi.org/10.1080/10496491.2016.1251530

Helm, S. (2005). Designing a formative measure for corporate reputation. Corporate Reputation Review, 8(2), 95–109. https://doi.org/10.1057/palgrave.crr.1540242

Herrero, Ã., San Martín, H., & Garcia-De los Salmones, M. del M. (2017). Explaining the adoption of social networks sites for sharing user-generated content: A revision of the UTAUT2. Computers in Human Behavior, 71, 209-217. https://doi.org/10.1016/j.chb.2017.02.007

Hurtado, P. A., Dorneles, C., & Frazzon, E. (2019). Big data application for e-commerce’s logistics: A research assessment and conceptual model. IFAC-PapersOnLine, 52(13), 838–843. https://doi.org/10.1016/j.ifacol.2019.11.234

Indiani, N. L. P., Rahyuda, I. K., Kerti Yasa, N. N., & Sukaatmadja, I. P. G. (2015). Perceived risk and trust as major determinants of actual purchase, transcending the influence of intention. ASEAN Marketing Journal, 7(1), 1-13. https://doi.org/10.21002/amj.v7i1.4601

Jambulingam, M. (2013). Behavioural intention to adopt mobile technology among tertiary students. World Applied Sciences Journal, 22(9), 1262-1271. https://doi.org/10.5829/idosi.wasj.2013.22.09.2748

Jewer, J. (2018). Patients’ intention to use online postings of ED wait times: A modified UTAUT model. International Journal of Medical Informatics, 112, 34–39. https://doi.org/10.1016/j.ijmedinf.2018.01.008

Kabra, G., Ramesh, A., Akhtar, P., & Dash, M. K. (2017). Understanding behavioural intention to use information technology: Insights from humanitarian practitioners. Telematics and Informatics, 34(7), 1250–1261. https://doi.org/10.1016/j.tele.2017.05.010

Kapferer, J. N., Klippert, C., & Leproux, L. (2014). Does luxury have a minimum price? An exploratory study into consumers’ psychology of luxury prices. Journal of Revenue and Pricing Management, 13(1), 2–11. https://doi.org/10.1057/rpm.2013.34

Khechine, H., Raymond, B., & Augier, M. (2020). The adoption of a social learning system: Intrinsic value in the UTAUT model. British Journal of Educational Technology. https://doi.org/https://doi.org/10.1111/bjet.12905

Kim, C. F., & Zhang, L. (2016). Corporate political connections and tax aggressiveness. Contemporary Accounting Research, 33(1), 78–114. https://doi.org/10.1111/1911-3846.12150

Lestari, D. (2019). Measuring e-commerce adoption behaviour among gen-Z in Jakarta, Indonesia. Economic Analysis and Policy, 64, 103–115. https://doi.org/10.1016/j.eap.2019.08.004

Liu, L., Miguel Cruz, A., & Juzwishin, D. (2018). Caregivers as a proxy for responses of dementia clients in a GPS technology acceptance study. Behaviour & Information Technology, 37(6), 634–645. https://doi.org/10.1080/0144929X.2018.1470672

Madigan, R., Louw, T., Dziennus, M., Graindorge, T., Ortega, E., Graindorge, M., & Merat, N. (2016). Acceptance of automated road transport systems (ARTS): An adaptation of the utaut model. Transportation Research Procedia, 14, 2217–2226. https://doi.org/10.1016/j.trpro.2016.05.237

Narasuman, S., Yunus, M., Md, R., & Kamal, A. A. (2011). Net generation student teachers: how tech-savvy are they?. Journal of Educators & Education/Jurnal Pendidik Dan Pendidikan, 26(1), 71–89. Retrieved from https://core.ac.uk/reader/83543475

Octarina, E., Hartoyo, H., & Beik, I. S. (2019). Customer purchase intention on sharia mutual fund products: A TPB approach. Journal of Consumer Sciences, 4(1), 37. https://doi.org/10.29244/jcs.4.1.37-47

Octaviani, E. S., & Gunawan, H. (2018). Perceived risk on consumer online shopping behaviour, 3(2), 203–209. https://doi.org/https://doi.org/10.30871/jaat.v3i2.876

Ouellette, J. A., & Wood, W. (1998). Habit and intention in everyday life: The multiple processes by which past behavior predicts future behavior. Psychological Bulletin, 124(1), 54–74. https://doi.org/10.1037/0033-2909.124.1.54

Pham, M., Valette-Florence, P., & Vigneron, F. (2018). Luxury brand desirability and fashion equity: The joint moderating effect on consumers’ commitment toward luxury brands. Psychology and Marketing, 35(12), 902–912. https://doi.org/10.1002/mar.21143

Primanto, A. B., ABS, M. K., & Slamet, A. R. (2018). A study of the best selling smartphone in the two biggest marketplace in Indonesia. Jurnal Terapan Manajemen Dan Bisnis, 4(1), 17-24. https://doi.org/10.26737/jtmb.v4i1.487

Primanto, A. B., & Dharmmesta, B. S. (2019). What happens after they laugh: How humorous advertisements have an effect on consumers’ attitudes, word of mouth intentions, and purchase intentions, with the need for humor playing a moderating role. Journal of Indonesian Economy and Business, 34(2), 117. https://doi.org/10.22146/jieb.23036

Sahu, G. P., & Singh, M. (2017). Factors influencing consumer’s behavioral intention to adopt irctc connect mobile application. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) (pp. 3–15). https://doi.org/10.1007/978-3-319-68557-1_1

Salam, A. F., Rao, H. R., & Pegels, C. C. (2003). Consumer-perceived risk in e-commerce transactions. Communications of the ACM, 46(12), 325. https://doi.org/10.1145/953460.953517

Siddique, M. A. M. (2012). Explaining the role of perceived risk, knowledge, price, and cost in dry fish consumption within the theory of planned behavior. Journal of Global Marketing, 25(4), 181-201. https://doi.org/10.1080/08911762.2012.743203

Somba, W. E., Sunaryo, S., & Mugiono, M. (2018). Pengaruh nilai hedonis dan nilai utilitarian terhadap behavioral intention, dengan word of mouth (WOM) sebagai variabel mediasi. Jurnal Manajemen Dan Kewirausahaan, 6(1), 82. https://doi.org/10.26905/jmdk.v6i1.2071

Tak, P., & Panwar, S. (2017). Using UTAUT 2 model to predict mobile app based shopping: evidences from India. Journal of Indian Business Research, 9(3), 248–264. https://doi.org/10.1108/JIBR-11-2016-0132

Tarhini, A., El-Masri, M., Ali, M., & Serrano, A. (2016). Extending the UTAUT model to understand the customers’ acceptance and use of internet banking in Lebanon. Information Technology & People, 29(4), 830–849. https://doi.org/10.1108/ITP-02-2014-0034

Tingchi Liu, M., Brock, J. L., Cheng Shi, G., Chu, R., & Tseng, T. (2013). Perceived benefits, perceived risk, and trust. Asia Pacific Journal of Marketing and Logistics, 25(2), 225–248. https://doi.org/10.1108/13555851311314031

Venkatesh, V., Morris, M. G., Davis, G. B., & Davis, F. D. (2003). User acceptance of information technology: Toward a unified view. MIS Quarterly: Management Information Systems, 27(3), 425-478. https://doi.org/10.2307/30036540

Venkatesh, V., Thong, J. Y. L., & Xu, X. (2012). Consumer acceptance and use of information technology: Extending the unified theory of acceptance and use of technology. MIS Quarterly: Management Information Systems, 36(1), 157–178. https://doi.org/10.2307/41410412

Wang, Y., & Herrando, C. (2019). Does privacy assurance on social commerce sites matter to millennials? International Journal of Information Management, 44, 164–177. https://doi.org/10.1016/j.ijinfomgt.2018.10.016

Warkentin, M., Gefen, D., Pavlou, P. A., & Rose, G. M. (2002). Encouraging citizen adoption of e-government by building trust. Electronic Markets, 12(3), 157–162. https://doi.org/10.1080/101967802320245929

Wong, S.-M., Leong, C.-M., & Puah, C.-H. (2020). Mobile internet adoption in malaysian suburbs: The moderating effect of gender. Asian Journal of Business Research, 9(3). https://doi.org/10.14707/ajbr.190069

Yu, T. W., & Chen, T. J. (2018). Online travel insurance purchase intention : A transaction cost perspective. Journal of Travel and Tourism Marketing, 35(9), 1175-1186. https://doi.org/10.1080/10548408.2018.1486781

Zuiderwijk, A., Janssen, M., & Dwivedi, Y. K. (2015). Acceptance and use predictors of open data technologies: Drawing upon the unified theory of acceptance and use of technology. Government Information Quarterly, 32(4), 429-440. https://doi.org/10.1016/j.giq.2015.09.005

Zulfikar, R., & Mayvita, P. A. (2018). The relationship of perceived value, perceived risk, and level of trust towards green products of fast moving consumer goods purchase intention. JEMA: Jurnal Ilmiah Bidang Akuntansi Dan Manajemen, 15(2), 85-97. https://doi.org/10.31106/jema.v15i2.838

Downloads

Additional Files

Published

2020-03-17

How to Cite

Piarna, R., Fathurohman, F., & Purnawan, N. N. (2020). Understanding online shopping adoption: The unified theory of acceptance and the use of technology with perceived risk in millennial consumers context. JEMA: Jurnal Ilmiah Bidang Akuntansi Dan Manajemen, 17(1), 51–66. https://doi.org/10.31106/jema.v17i1.5050