Abstract
Although creativity is often considered a key success factor in advertising, the marketing literature lacks a systematic empirical account of when and how advertising creativity works. The authors use a meta-analysis to synthesize the literature on advertising creativity and test different theoretical explanations for its effects. The analysis covers 93 data sets taken from 67 papers that provide 878 effect sizes. The results show robust positive effects but also highlight the importance of considering both originality and appropriateness when investing in advertising creativity. Moderation analyses show that the effects of advertising creativity are stronger for high- (vs. low-) involvement products, and that the effects on ad (but not brand) reactions are marginally stronger for unfamiliar brands. An empirical test of theoretical mechanisms shows that affect transfer, processing, and signaling jointly explain these effects, and that originality mainly leads to affect transfer, whereas appropriateness leads to signaling. The authors also call for further research connecting advertising creativity with sales and studying its effects in digital contexts.
Highlights
Creativity is often considered a key success factor in advertising, the marketing literature lacks a systematic empirical account of when and how advertising creativity works
We offer a comprehensive synthesis of the effects of advertising creativity on consumer responses
The results further show that advertising creativity has stronger effects in high-involvement contexts, and that effects on ad response are stronger for unfamiliar brands
Summary
For this meta-analysis, we selected papers that provide estimates of the effects of advertising creativity on various consumer responses. Each data set can provide single or multiple effect sizes that refer to the effect of advertising creativity on any consumer response variable. Because these consumer response variables appeared either in only one or two data sets or in only one paper, we eliminated them from further analysis.2 We did this to ensure a minimum degree of generalizability, because a meta-analysis should provide a high degree of generalization and requires more information than a single manuscript or a single-study manuscript followed by a replication study. We accounted for the dependencies of effect sizes and the nested structure of meta-analytic data by using a mixed-effects multilevel model as described previously (Raudenbush and Bryk 2002) We used this correlation matrix (see Web Appendix Table 2) as input in a structural equation modeling (SEM) analysis using the maximum likelihood method. We used the harmonic mean of the cumulative sample size underlying each integrated effect size (i.e., effect size cells comprising each entry in the correlation matrix) as the sample size for the analysis
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