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Mobile commerce and device specific perceived risk

Author

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  • Brian P. Cozzarin

    (University of Waterloo)

  • Stanko Dimitrov

    (University of Waterloo)

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.

Suggested Citation

  • Brian P. Cozzarin & Stanko Dimitrov, 2016. "Mobile commerce and device specific perceived risk," Electronic Commerce Research, Springer, vol. 16(3), pages 335-354, September.
    • Handle: RePEc:spr:elcore:v:16:y:2016:i:3:d:10.1007_s10660-015-9204-5
    DOI: 10.1007/s10660-015-9204-5
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    References listed on IDEAS

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    Citations

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    Cited by:

    1. Chi, Ting, 2018. "Understanding Chinese consumer adoption of apparel mobile commerce: An extended TAM approach," Journal of Retailing and Consumer Services, Elsevier, vol. 44(C), pages 274-284.
    2. Xiaoyu Li & Jiahong Yuan & Yan Shi & Tianteng Wang & Xiangpei Hu & Felix Tung Sun Chan & Junhu Ruan, 2020. "An extended Bass Model on consumer quantity of B2C commerce platforms," Electronic Commerce Research, Springer, vol. 20(3), pages 609-628, September.
    3. Sabina Lissitsa & Ofrit Kol, 2021. "Four generational cohorts and hedonic m-shopping: association between personality traits and purchase intention," Electronic Commerce Research, Springer, vol. 21(2), pages 545-570, June.
    4. Younghoon Chang & Siew Fan Wong & Christian Fernando Libaque-Saenz & Hwansoo Lee, 2019. "e-Commerce Sustainability: The Case of Pinduoduo in China," Sustainability, MDPI, vol. 11(15), pages 1-23, July.
    5. Martin P. Fritze & Andreas B. Eisingerich & Martin Benkenstein, 2019. "Digital transformation and possession attachment: examining the endowment effect for consumers’ relationships with hedonic and utilitarian digital service technologies," Electronic Commerce Research, Springer, vol. 19(2), pages 311-337, June.
    6. Sohn, Stefanie & Groß, Michael, 2020. "Understanding the inhibitors to consumer mobile purchasing intentions," Journal of Retailing and Consumer Services, Elsevier, vol. 55(C).
    7. Talwar, Shalini & Dhir, Amandeep & Khalil, Ashraf & Mohan, Geetha & Islam, A.K.M. Najmul, 2020. "Point of adoption and beyond. Initial trust and mobile-payment continuation intention," Journal of Retailing and Consumer Services, Elsevier, vol. 55(C).
    8. Shaw, Norman & Sergueeva, Ksenia, 2019. "The non-monetary benefits of mobile commerce: Extending UTAUT2 with perceived value," International Journal of Information Management, Elsevier, vol. 45(C), pages 44-55.
    9. Pham, Thi Song Hanh & Ahammad, Mohammad Faisal, 2017. "Antecedents and consequences of online customer satisfaction: A holistic process perspective," Technological Forecasting and Social Change, Elsevier, vol. 124(C), pages 332-342.
    10. Benjamin Barann & Jan H. Betzing & Marco Niemann & Benedikt Hoffmeister & Jörg Becker, 2022. "Exploring customers’ likeliness to use e-service touchpoints in brick and mortar retail," Electronic Markets, Springer;IIM University of St. Gallen, vol. 32(2), pages 523-545, June.

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