Mobile commerce and device specific perceived risk
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This study examines the role of perceived risk and access device type on consumers’ on-line purchase decisions. We use a two-step hurdle approach to estimate consumer behavior. In the first step, the decision of whether to engage in eCommerce is estimated and in the second step, how many orders to place is estimated. We use a large multi-year survey sample of households from Canada’s national statistical agency—Statistics Canada. The sample size is such that we are able to conduct sub-sample analysis of PC-only users, mobile-only users, and other-users. We show that online security and price significantly influence mobile eCommerce. We also show that there is a statistically significant difference in the intensity of eCommerce engagement across device type and consumer risk type (high/low). One of our main findings is that perceived risk affects purchase decisions for mobile users more than PC users, however additional comparisons are carried out with our analysis.
Keywordse-Commerce m-Commerce Device type Perceived risk Transactions cost
- 1.AmeriCommerce. (2014). Data driven ecommerce—Infographic. In AmeriCommerce blogger, May 28, 2014. Retrieved March 26, 2015, from http://www.americommerce.com/blog/Data-Driven-Ecommerce-Infographic.
- 2.EMarketer. (2014). Retail sales worldwide will top $22 trillion this year. Retrieved December 23, 2014, from www.emarketer.com.
- 3.Canada S. (2010). Canadian internet use survey.Google Scholar
- 4.Baumhof, A. (2015). 6 Cybercrime predictions for the year ahead. Kansas City Business Journal, Jan 8, 2015.Google Scholar
- 5.Ekekwe, N. (2015). The challenges facing e-commerce start-ups in Africa. Harvard Business Review, March 12, 2015.Google Scholar
- 6.Bosomworth, D. (2015). Mobile Marketing Statistics. January 15, 2015. http://www.smartinsights.com.
- 7.DeNinno, N. (2014). Men shop online just as much as women but shop on mobile devices, tablets more—Study. International Business Times. Google Scholar
- 8.Dai, B., Forsythe, S., & Kwon, W.-S. (2014). Impact of online shopping experience on risk perceptions and online purchase intentions: Does product category matter? Journal of Electronic Commerce Research, 15, 13–24.Google Scholar
- 13.Gefen, D., Karahanna, E., & Straub, D. W. (2003). Trust and TAM in online shopping: An integrated model. MIS Quarterly, 27, 51–90.Google Scholar
- 17.Pavlou, P. A. (2003). Consumer acceptance of electronic commerce: Integrating trust and risk with the technology acceptance model. International Journal of Electronic Commerce, 7, 101–134.Google Scholar
- 19.Yan, R., Cozzarin, B., & Dimitrov, S. (2015). Mobile device access: Effect on online purchases. In Wireless telecommunication symposium (pp. 1–6). New York, NY.Google Scholar
- 23.Chin, E., Felt, A. P., Sekar, V., & Wagner, D. (2012). Measuring user confidence in smartphone security and privacy. In Proceedings of the eighth symposium. Usable privacy and security—SOUPS’12 (pp. 1–16). New York: ACM Press.Google Scholar
- 30.Williamson, O. E. (1985). The economic institutions of capitalism. New York: Free Press.Google Scholar
- 31.Williamson, O. E. (1983). Markets and hierarchies: Analysis and antitrust implications. New York: Free Press.Google Scholar