Today, it is nearly impossible to find a public event or campaign that is not sponsored in some way. Nevertheless, research on the determinants of sponsorship effectiveness is deficient. In this study, conducted in a non-profit context, we analysed an affective outcome, Attitudes towards the Sponsorship, as the consequence of Sponsorship Fit, and we proposed two characteristics as the latter’s antecedents: Sponsorship Similarity and Sponsor Cohesiveness, respectively a characteristic of the pair and a characteristic of the sponsor. To examine the effects that these variables have on each other, we conducted an online experiment, with a 2x2 between-subjects design, on a sample of 259 individuals. We then conducted a regression analysis on the data that we gathered, and we found that both Sponsorship Similarity and Sponsor Cohesiveness, as well as their interaction, have a positive effect on Sponsorship Fit. These results are critical to understanding how to develop better-perceived sponsorship pairs. Indeed, companies should actively try to highlight the features they share with the charity that they decide to sponsor if those features are not easily noticeable. Also, they should maintain a consistent strategy over time for their brand to be perceived as more cohesive.

After collecting the data, we clean it, and we run a regression analysis. Data cleaning consists in checking for duplicates, straight liners and for bad quality data through the attention check. To run the analysis, we also need to create categorical variables for our manipulated variables. For this purpose, we use effect coding. In this way, it avoids preselecting an arbitrary reference category as it happens with dummy coding. Moreover, when there is an interaction between two categorical variables, effect coding helps in estimating both main and interaction effects reasonably. With dummy coding instead, main effects cannot be considered true, as they are the effect of one variable at the level of the other variable. For the regression analysis, the R package lavaan will be used based on the following formula:

• SF=a1SS+a2SC+a3SSSC
• ATS=b1SF+c1SS+c2SC+c3SS*SC

Here, SF is Sponsorship Fit, SS is Sponsorship similarity, SC is Sponsor Cohesiveness and ATS is Attitude towards the Sponsorship.

The results of this study illustrate the positive effects that sponsorship similarity and sponsor cohesiveness have on increasing sponsorship fit. Practical implications that stem from this research are valuable to multiple subjects, managers of the corporate sponsor, as well as managers of the charity. A way in which managers could take advantage of the findings of this research is when deciding to undertake a sponsorship agreement with a company that has a low similarity with the charity. In this case, it does not mean that the sponsorship is going to be a complete failure from the beginning. As a matter of fact, the company can achieve a high fit even in this situation, if it decides to invest in communication efforts. Indeed, with a clear communication strategy they can explicate what makes the company and the charity similar, even if those similarity are abstract or not immediately noticed. The consumers will then perceive the sponsor in a different light. Moreover, with a clear communication strategy and consistent efforts over time in the same direction, sponsors would also improve their brand image and therefore, their cohesiveness. Undeniably, by knowing that similarity and sponsor cohesiveness can be drivers of a sponsorship’s success, managers can evaluate better which company or which charity would form a logical pair and consequently lead to successful partnerships before deciding to sign an agreement together.