shopping context, including food focus of store (1=food; 0=non-food), frequency of store visits (1=frequent; 0=infrequent), agglomeration level (1=agglomeration; 0=single store), and hedonic context (1=hedonic; 0=utilitarian). We coded dummies for type of participants (1=students, 0=non-students), study design (1=cross-sectional, 0=longitudinal), and data source (1=secondary data, 0=survey data). We dummy-coded whether the studies were conducted after 2000, when Internet shopping began gaining momentum. We used the country information to merge the meta-data with secondary data, including the country’s Gross Domestic Product per capita (International Monetary Fund 2016), country innovativeness (Global Innovation Index 2017), retail sales as share of Gross Domestic Product and employment in retailing as a share of national employment (Euromonitor 2014). Integration of Effect Sizes In total, we included 24 marketing instruments and four outcome variables in the analysis. Research has usually measured the relationships between antecedents and outcome variables by means of correlations (e.g., Babic Rosario et al. 2016). Therefore, the effect size in this meta-analysis is represented by correlation coefficients (r). The r-statistic is often used because it is scale-free, easily interpretable, and robust (Grewal, Puccinelli, and Monroe 2017). If other statistical information (e.g., t-tests) was available, we used it to convert the effect sizes into correlations (Hunter and Schmidt 2004). Some studies used regressions and did not report correlation information. To incorporate the standardized beta coefficients of these primary studies, we used the conversion formula Peterson and Brown (2005) suggest. If a study provided multiple associations for the same relationship, we averaged the effect sizes and reported them as a single data point (Palmatier et al. 2006). To correct effect sizes for differences in measurement reliability, we used Hunter and Schmidt’s (2004) suggested procedure. Specifically, we divided each correlation by the square root of the product of the reliabilities of the independent and dependent variables. For
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.
study suggests that scholars should consider the different retail environments in different country markets to better understand retail patronage. In particular, we find that the effectiveness of more than half of the investigated instruments differs in influencing retail patronage. For example, branding—on a product and corporate level—plays a more important role in more developed countries, while incentives and prices are less important in developed countries. In addition, many instruments are less effective in innovative countries because customers are used to provider switching, independent of the employed instruments. The results also suggest that marketing instruments work differently in competitive markets in which firms try to retain or increase their market shares. Furthermore, the results indicate that in countries with the possibility to interact more frequently with retail employees, particularly the understanding of incentives improves. Advertising also gains importance because employees help customers understand advertised offers. Finally, we find that several instruments gained importance with the advent of the Internet (e.g., advertising), while others lost relevance (e.g., spatial/temporal distance). Fourth, our study assessed the influence of method moderators. The study clarifies that stronger effects exist in student samples, longitudinal designs, and studies using survey data. The findings thus suggest that studies should avoid student samples, use more longitudinal research designs, and combine data sources to ensure that associations are not inflated. Managerial Implications The findings of the meta-study have several practical implications for retail managers. In particular, they provide guidance on the importance of different retail marketing instruments in creating value for customers and supporting a retailer’s strategic value proposition in the market. Table 5 describes the most essential instruments retail managers can use to establish and sustain retail patronage. For example, they can focus on different customer outcomes—from satisfaction, to WOM, to patronage intention and behavior—and