The term mediation analysis encapsulates methodological approaches for determining whether a causal effect of a predictor on an outcome variable can be explained by another variable. This explanatory variable is called a mediator. Mediation analysis is therefore particularly relevant for those interested in grasping the underlying mechanism of a focal effect. This paper reviews the purpose and aims of mediation analysis, common approaches of conducting mediation analysis, and how mediation analysis can enrich research findings. We especially address the widespread uncertainty of researchers and practitioners in the marketing field in deciding which method to implement to explore a proposed mediation mechanism and how to conduct the analysis. The review contributes to extant literature by connecting theoretical and practical knowledge in order to help in applying mediation analysis. Furthermore, the second part of the paper is organized as a tutorial in order to make the state-of-the-art methodology applicable to the reader. Here, we illustrate how to specify, interpret, and report results using PROCESS, a commonly used macro developed specifically for mediation analysis with SPSS and SAS. In the first part of the paper we introduce two basic elements of mediation processes: the indirect and the direct effect. The indirect effect indicates whether a proposed mediator can explain the relationship between a predictor variable and an outcome variable. Therefore, estimating the indirect effect is essential for establishing mediation. The interpretation of the indirect effect depends on the model structure – for example the number of proposed mediators and their interconnection. Based on the indirect effect, we discuss four commonly used mediation model groups. Conversely, the direct effect reveals to what extent a proposed mediator can explain the relationship of a predictor variable and an outcome variable. Thus, the direct effect determines whether the mediator can fully explain the relationship (full mediation) or only partially explain it (partial mediation). We refer to this broad distinction as mediation types. Determining the mediation type can guide further theory building, as it may indicate a second mediator that has been omitted in the analysis thus far. In the methodology section of the paper, we review three different regression-based approaches for inferring mediation: the Baron-and-Kenny approach, the Sobel test, and bootstrapping. The Baron-and-Kenny approach relies on estimating each relationship of a mediation model separately and then inferring mediation if all relationships are significant. The Sobel test is a parametric test of the indirect effect. While common, both approaches are widely criticized because of their questionable assumptions. In contrast to these traditional approaches, bootstrapping is a non-parametric approach that provides an accurate test of the indirect effect and is based on data resampling. The significance of an indirect effect is then inferred from a confidence interval of its bootstrap distribution. As bootstrapping is the state-of-the-art methodology in mediation analysis, we limit our focus to this approach for the subsequent tutorial. After the theoretical discussion, the tutorial illustrates in detail how to specify different model groups with the easy-to-apply macro PROCESS. In this section we work with an example data set from the marketing field to illustrate the steps of the mediation analysis.
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