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Trust and risk in consumer acceptance of e-services

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An Erratum to this article was published on 21 December 2015

Abstract

Consumers’ risk perception and trust are considered among the most important psychological states that influence online behavior. Despite the number of empirical studies that have explored the effects of trust and risk perceptions on consumer acceptance of e-services, the field remains fragmented and the posited research models are contradictory. To address this problem, we examined how trust and risk influence consumer acceptance of e-services through a meta-analysis of 67 studies, followed by tests of competing causal models. The findings confirm that trust and risk are important to e-services acceptance but that trust has a stronger effect size. We found that certain effect sizes were moderated by factors such as the consumer population under study, the type of e-service, and the object of trust under consideration. The data from the meta-analysis best supports the causal logic that positions trust as antecedent to risk perceptions. Risk partially mediates the effects of trust on acceptance.

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Notes

  1. The use of two coders helps to increase coding reliability and can provide a check on the coding and effect size calculations.

  2. \( Z = \left( {Z_{y,a} - Z_{y,b} } \right)\sqrt {\frac{N - 3}{{2\left( {1 - r_{ab} } \right)h^{{\prime }} }}} \), where Zy,a and Zy,b are Fisher’s Z-transformations, N is the sample size, h is \( {{(1 - f\bar{r}^{2} )} \mathord{\left/ {\vphantom {{(1 - f\bar{r}^{2} )} {(1 - r^{2} ),\;f\;{\text{is}}\;\frac{{1 - r_{a,b} }}{{2(1 - \bar{r}^{2} )}},}}} \right. \kern-0pt} {(1 - r^{2} ),\;f\;{\text{is}}\;\frac{{1 - r_{a,b} }}{{2(1 - \bar{r}^{2} )}},}} \) and \( \bar{r}^{2} \) is \( \frac{{x_{y,a}^{2} + x_{y,b}^{2} }}{2} \) [71]. Given the number of correlations (K) between TR-ATT (K = 21) and PR-ATT (K = 20), TR-BI (K = 71) and PR-BI (K = 68) are different. To examine whether trust has a significantly stronger effect on attitude, we used K = 20 as the previous formula’s N. For the effects on behavior intention, we adopted K = 68 as the formula’s N.

  3. \( {\text{Harmonic mean = N}}/( 1/{\text{a}}_{ 1} + 1/{\text{a}}_{ 2} + 1/{\text{a}}_{ 3} + 1/{\text{a}}_{ 4} + \cdots + 1/{\text{a}}_{\text{n}} ) \).

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Acknowledgments

This paper was supported by Sungkyun Research Fund, Sungkyunkwan University.

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  1. Department of Interaction Science, Sungkyunkwan University, 25-2 Sungkyunkwan-ro, Jongro-gu, Seoul, 110-745, Korea

    Jian Mou & Dong-Hee Shin

  2. University of the Witwatersrand, Private Bag x3, WITS, Johannesburg, 2050, South Africa

    Jason F. Cohen

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An erratum to this article is available at http://dx.doi.org/10.1007/s10660-015-9210-7.

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Table 10 Studies Used in Meta-Analysis
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Mou, J., Shin, DH. & Cohen, J.F. Trust and risk in consumer acceptance of e-services. Electron Commer Res 17, 255–288 (2017). https://doi.org/10.1007/s10660-015-9205-4

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  • DOI: https://doi.org/10.1007/s10660-015-9205-4

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