Antecedents of online purchase intention and behaviour: uncovering unobserved heterogeneity
Abstract
The paper aims at exploring the antecedents of customers’ online purchase intention and behaviour, and at uncovering sources of heterogeneity. A sample of customers was surveyed to measure perceived risk and benefits, trust, online purchase intention and behaviour. The study confirmed the causal chain of perceived risks-trust-perceived benefits-online purchase intention-actual purchase. A Finite Mixture Partial Least Squares (FIMIX-PLS) was performed to uncover sources of heterogeneity. It found that the level of security of the payment methods is relevant to understand the relationship between purchase intention and behaviour, while the level of previous experience with the online medium clarifies the relationship between perceived risk and trust. The study contributes to understanding the antecedents of online purchase intention and their relationship with actual purchase behaviour. Additionally, it offers evidence of heterogeneity in the proposed causal relations, particularly, concerning the level of trust in the payment methods and the level of Internet experience.
Keyword : online purchase intention, online purchase behaviour, perceived risk, trust, perceived benefits, unobserved heterogeneity, online payment methods, level of Internet experience, FIMIX- PLS
How to Cite
Silva, J., Pinho, J. C., Soares, A., & Sá, E. (2019). Antecedents of online purchase intention and behaviour: uncovering unobserved heterogeneity. Journal of Business Economics and Management, 20(1), 131-148. https://doi.org/10.3846/jbem.2019.7060
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Forsythe, S., Liu, C., Shannon, D., & Gardner, L. C. (2006). Development of a scale to measure the perceived benefits and risks of online shopping. Journal of Interactive Marketing, 20(2), 55-75. https://doi.org/10.1002/dir.20061
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Henseler, J., Ringle, C. M., & Sarstedt, M. (2015). A new criterion for assessing discriminant validity in variance-based structural equation modeling. Journal of the Academy of Marketing Science, 43(1), 115-135. https://doi.org/10.1007/s11747-014-0403-8
Hong, I. B. (2015). Understanding the consumer’s online merchant selection process: the roles of product involvement, perceived risk, and trust expectation. International Journal of Information Management, 35(3), 322-336. https://doi.org/10.1016/j.ijinfomgt.2015.01.003
Hong, I. B., & Cha, H. S. (2013). The mediating role of consumer trust in an online merchant in predicting purchase intention. International Journal of Information Management, 33(6), 927-939. https://doi.org/10.1016/j.ijinfomgt.2013.08.007
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Jarvenpaa, S. L., Tractinsky, N., & Vitale, M. (2000). Consumer trust in an Internet store. Information Technology and Management, 1(1/2), 45-71. https://doi.org/10.1023/A:1019104520776
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Jeske, D., & van Schaik, P. (2017). Familiarity with Internet threats: beyond awareness. Computers & Security, 66, 129-141. https://doi.org/10.1016/j.cose.2017.01.010
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Kim, D. J., Ferrin, D. L., & Rao, H. R. (2008). A trust-based consumer decision-making model in electronic commerce: the role of trust, perceived risk, and their antecedents. Decision Support Systems, 44(2), 544-564. https://doi.org/10.1016/j.dss.2007.07.001
Kim, H.-W., Xu, Y., & Gupta, S. (2012). Which is more important in Internet shopping, perceived price or trust. Electronic Commerce Research and Applications, 11(3), 241-252. https://doi.org/10.1016/j.elerap.2011.06.003
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