The effects of e-commerce institutional mechanisms on trust and online purchase have traditionally been understood in the initial online purchase context. This study extends this literature by exploring the role of e-commerce institutional mechanisms in the online repurchase context. In doing so, it responds to the emerging call for understanding the institutional context under which customer trust operates in an e-commerce environment. Specifically, this study introduces a key moderator, perceived effectiveness of e-commerce institutional mechanisms (PEEIM), to the relationships between trust, satisfaction, and repurchase intention. Drawing on the theory of organizational trust, and based on a survey of 362 returning online customers, we find that PEEIM negatively moderates the relationship between trust in an online vendor and online customer repurchase intention, as it decreases the importance of trust to promoting repurchase behavior. We also find that PEEIM positively moderates the relationship between customer satisfaction and trust as it enhances the customer’s reliance on past transaction experience with the vendor to reevaluate trust in the vendor. Consistent with the predictions made in the literature, PEEIM does not directly affect trust or repurchase intention. Academic and practical implications and future research directions are discussed.
Multigroup or between-group analyses are common in the information systems literature. The ability to detect the presence or absence of between-group differences and accurately estimate the strength of moderating effects is important in studies that attempt to show contingent effects. In the past, IS scholars have used a variety of approaches to examine these effects, with the partial least squares (PLS) pooled significance test for multigroup becoming the most common (e.g., Ahuja and Thatcher 2005; Enns et al. 2003; Zhu et al. 2006). In other areas of social sciences (Epitropaki and Martin 2005) and management (Mayer and Gavin 2005; Song et al. 2005) research, however, there is greater emphasis on the use of covariance-based structural equation modeling multigroup analysis. This paper compares these two methods through Monte Carlo simulation. Our findings demonstrate the conditions under which covariance-based multigroup analysis is more appropriate as well as those under which there either is no difference or the component-based approach is preferable. In particular, we find that when data are normally distributed, with a small sample size and correlated exogenous variables, the component-based approach is more likely to detect differences between-group than is the covariance-based approach. Both approaches will consistently detect differences under conditions of normality with large sample sizes. With non-normally distributed data, neither technique could consistently detect differences across the groups in two of the paths, suggesting that both techniques struggle with the prediction of a highly skewed and kurtotic dependent variable. Both techniques detected the differences in the other paths consistently under conditions of non-normality, with the component-based approach preferable at moderate effect sizes, particularly for smaller samples.