We used Hierarchical Linear Modeling (HLM) software to test the proposed moderators. Hox (2010) and Bijmolt and Pieters (2001) recommend this testing approach because it is unlikely that studies reporting multiple measurements are independent of one another. HLM allows us to account for the nesting not only of multiple instruments within one study but also of multiple outcomes. The random-effects model differentiates between two levels, the effect size level (level 1) and the study level (level 2) (Pastor and Lazowski 2018). The dependent variable is the reliability-adjusted correlation, which is regressed on level 1 and level 2 variables. As suggested by Hox (2010), we dummy-coded the marketing instruments and outcome variables and included them on level 1. We also calculated interactions between the dummy-coded instruments on level 1 and the moderators on level 2 (de Jong, de Ruyter, and Lemmink 2004) to estimate the following model: Level 1: and Level 2: , where Yij is the ith reliability-adjusted correlation of the jth study, Xij refers to the level 1 predictors (marketing mix, outcomes), W0j and W1j are the level 2 predictors (shopping context, retail environment, method), is the residual error on level 1, and and are ɛij μ0j μ1j the residual error terms at level 2. Results Descriptive Statistics To test the marketing-mix effects, we first synthesized existing research by calculating the averaged correlations for different instruments with outcome variables. We could relate 24 instruments to customer satisfaction (Table 1). For most instruments, we averaged at least 30 effect sizes, except for advertising, which is often examined in econometric models that do not report standardized effect sizes. Non-monetary incentives are also infrequently examined.
We used Hierarchical Linear Modeling (HLM) software to test the proposed moderators. Hox (2010) and Bijmolt and Pieters (2001) recommend this testing approach because it is unlikely that studies reporting multiple measurements are independent of one another. HLM allows us to account for the nesting not only of multiple instruments within one study but also of multiple outcomes. The random-effects model differentiates between two levels, the effect size level (level 1) and the study level (level 2) (Pastor and Lazowski 2018). The dependent variable is the reliability-adjusted correlation, which is regressed on level 1 and level 2 variables. As suggested by Hox (2010), we dummy-coded the marketing instruments and outcome variables and included them on level 1. We also calculated interactions between the dummy-coded instruments on level 1 and the moderators on level 2 (de Jong, de Ruyter, and Lemmink 2004) to estimate the following model: Level 1: and Level 2: , where Yij is the ith reliability-adjusted correlation of the jth study, Xij refers to the level 1 predictors (marketing mix, outcomes), W0j and W1j are the level 2 predictors (shopping context, retail environment, method), ɛij is the residual error on level 1, and μ0j and μ1j are the residual error terms at level 2. Results Descriptive Statistics To test the marketing-mix effects, we first synthesized existing research by calculating the averaged correlations for different instruments with outcome variables. We could relate 24 instruments to customer satisfaction (Table 1). For most instruments, we averaged at least 30 effect sizes, except for advertising, which is often examined in econometric models that do not report standardized effect sizes. Non-monetary incentives are also infrequently examined.