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Mobile commerce and device specific perceived risk

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Abstract

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.

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Notes

  1. The first wave of the CIUS was conducted in 2005. Afterwards CIUS 2007, 2009, 2010 and 2012 followed. Because the 2005 and 2007 CIUS surveys have low mobile device usage we omit those survey years. Furthermore, in the 2010 CIUS the section entitled ‘Group Privacy and Security’ does not include the question ‘How concerned (are you/would you be) about using your credit card over the Internet?’. In fact, there is no question related to financial security or online banking in the 2010 survey. The 2009 survey in contrast contains the online credit card use question. Thus we could only use the 2009 and 2012 surveys for this research. The response data files were merged to construct a cross-sectional database. A panel file could not be constructed as Statistics Canada does not provide a linkage variable due to their policy of maintaining respondent confidentiality.

References

  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.

  4. Baumhof, A. (2015). 6 Cybercrime predictions for the year ahead. Kansas City Business Journal, Jan 8, 2015.

  5. Ekekwe, N. (2015). The challenges facing e-commerce start-ups in Africa. Harvard Business Review, March 12, 2015.

  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.

  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 

  9. Lim, W. M. (2015). Antecedents and consequences of e-shopping: An integrated model. Internet Research,. doi:10.1108/IntR-11-2013-0247.

    Google Scholar 

  10. Miyazaki, A. D., & Fernandez, A. (2001). Consumer perceptions of privacy and security risks for online shopping. Journal of Consumer Affairs, 35, 27–44. doi:10.1111/j.1745-6606.2001.tb00101.x.

    Article  Google Scholar 

  11. Cases, A.-S. (2002). Perceived risk and risk-reduction strategies in Internet shopping. The International Review of Retail Distribution and Consumer Research, 12, 375–394. doi:10.1080/09593960210151162.

    Article  Google Scholar 

  12. Glover, S., & Benbasat, I. (2010). A comprehensive model of perceived risk of e-commerce transactions. International Journal of Electronic Commerce, 15, 47–78. doi:10.2753/JEC1086-4415150202.

    Article  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 

  14. Chen, R., & He, F. (2003). Examination of brand knowledge, perceived risk and consumers’ intention to adopt an online retailer. Total Quality Management and Business Excellence, 14, 677–693. doi:10.1080/1478336032000053825.

    Article  Google Scholar 

  15. Forsythe, S. M., & Shi, B. (2003). Consumer patronage and risk perceptions in Internet shopping. Journal of Business Research, 56, 867–875. doi:10.1016/S0148-2963(01)00273-9.

    Article  Google Scholar 

  16. Herrero Crespo, A., & Rodriguez del Bosque, I. (2010). The influence of the commercial features of the Internet on the adoption of e-commerce by consumers. Electronic Commerce Research and Applications, 9, 562–575. doi:10.1016/j.elerap.2010.04.006.

    Article  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 

  18. Narayanan, M., Koo, B., & Cozzarin, B. P. (2012). Fear of fraud and Internet purchasing. Applied Economic Letters, 19, 1615–1619. doi:10.1080/13504851.2011.648313.

    Article  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.

  20. Ono, A., Nakamura, A., Okuno, A., & Sumikawa, M. (2012). Consumer motivations in browsing online stores with mobile devices. International Journal of Electronic Commerce, 16, 153–178. doi:10.2753/JEC1086-4415160406.

    Article  Google Scholar 

  21. Lu, H.-P., & Su, P. Y.-J. (2009). Factors affecting purchase intention on mobile shopping web sites. Internet Research, 19, 442–458. doi:10.1108/10662240910981399.

    Article  Google Scholar 

  22. Wu, J.-H., & Wang, S.-C. (2005). What drives mobile commerce? Information and Management, 42, 719–729. doi:10.1016/j.im.2004.07.001.

    Article  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.

  24. Garbarino, E., & Strahilevitz, M. (2004). Gender differences in the perceived risk of buying online and the effects of receiving a site recommendation. Journal of Business Research, 57, 768–775. doi:10.1016/S0148-2963(02)00363-6.

    Article  Google Scholar 

  25. Mullahy, J. (1986). Specification and testing of some modified count data models. Journal of Econometrics, 33, 341–365. doi:10.1016/0304-4076(86)90002-3.

    Article  Google Scholar 

  26. Neyman, J., & Pearson, E. S. (1933). On the problem of the most efficient tests of statistical hypotheses. Philosophical Transactions of the Royal Society, Part A: Mathematical, Physical and Engineering Sciences, 231, 289–337. doi:10.1098/rsta.1933.0009.

    Article  Google Scholar 

  27. Katchova, A. L., & Miranda, M. J. (2004). Two-step econometric estimation of farm characteristics affecting marketing contract decisions. American Journal of Agricultural Economics, 86, 88–102. doi:10.1111/j.0092-5853.2004.00564.x.

    Article  Google Scholar 

  28. Goh, K. H., & Bockstedt, J. C. (2013). The framing effects of multipart pricing on consumer purchasing behavior of customized information good bundles. Information Systems Research, 24, 334–351. doi:10.1287/isre.1120.0428.

    Article  Google Scholar 

  29. Somaya, D., Williamson, I. O., & Lorinkova, N. (2008). Gone but not lost: The different performance impacts of employee mobility between cooperators versus competitors. Academy of Management Journal, 51, 936–953. doi:10.5465/AMJ.2008.34789660.

    Article  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 

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Authors and Affiliations

  1. Department of Management Sciences, University of Waterloo, 200 University Avenue West, Waterloo, ON, N2L 3G1, Canada

    Brian P. Cozzarin & Stanko Dimitrov

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  1. Brian P. Cozzarin
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  2. Stanko Dimitrov
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Correspondence to Stanko Dimitrov.

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Cozzarin, B.P., Dimitrov, S. Mobile commerce and device specific perceived risk. Electron Commer Res 16, 335–354 (2016). https://doi.org/10.1007/s10660-015-9204-5

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