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 Specifically, we divided each correlation by the Schmidt’s (2004) suggested procedure. square root of the product of the reliabilities of the independent and dependent variables. For
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