Brand, Identity and Reputation: Exploring, Creating New Realities ...

When investigating unique significant correlations (second-order partial correlations) between ‗FoI‘, ‗ideals‘ or ‗CT‘
with typicality, we found that only ‗FoI‘ displayed a significant unique correlation with ‗exemplar goodness‘. There
was also a marginally significant unique correlation between ‗FoI‘ and ‗output dominance‘ as well. ‗Ideals‘ did not
display unique significant correlation with ‗exemplar goodness‘ or ‗output dominance‘. ‗FoI‘ being the most
significantly correlated variable with ‗output dominance‘ is also consistent with Barsalou (1985).
A regression analysis was performed on ‗exemplar goodness‘ and ‗output dominance‘ (dependent variables) and ‗FoI‘
(independent variable). ‗FoI‘ was found to make a significant, appreciable contribution to predicting ‗exemplar
goodness‘ and a significant, although not appreciable contribution to predicting ‗output dominance‘.
Experts‘ and Novices‘ Results
Experts
‗FoI‘ is the only variable which has a significant correlation with ‗exemplar goodness‘. Although not significant, there
is even a negative association between ‗output dominance‘ and ‗ideals‘. Second-order partial correlations reveal that
‗FoI‘ is the only variable which has significant unique variance with ‗exemplar goodness‘ and a marginally significant
unique variance with ‗output dominance‘.
Regression analysis was performed on ‗exemplar goodness‘ and ‗output dominance‘ (dependent variables) and ‗FoI‘
(independent variable) for experts. ‗FoI‘ was found to make a significant, appreciable, contribution to predicting
‗exemplar goodness‘. However, it was not significant in the contribution to predicting ‗output dominance‘.
Novices
Interestingly, ‗ideals‘ is the only variable to have a significant correlation with ‗output dominance‘ for novices. The lack
of significant correlation of ‗output dominance‘ with ‗FoI‘ (frequency of instantiation) suggests novices may think of
‗ideal‘ sponsors when generating the list (although what these ‗ideals‘ are can only be speculated). ‗FoI‘ and ‗central
tendency‘ are also significantly associated with ‗exemplar goodness‘. Second-order partial correlations reveal that the
only variable to have unique significant association with ‗exemplar goodness‘ was ‗ideals‘. It is also marginally
significantly associated with ‗output dominance‘.
Regression analysis was performed on ‗exemplar goodness‘ and ‗output dominance‘ (dependent variables) and ‗ideals‘
(independent variable) for novices. ‗Ideals‘ was found to make a significant, although not appreciable contribution to
predicting ‗exemplar goodness‘ and ‗output dominance‘.
Generation of Members in the Category ‗Sponsorship‘
A one-way ANOVA, using the median split for expertise, found there to be no significant difference between either
group for the number of sponsors generated. Kendall‘s coefficient of concordance also shows that there is a significant
ranking agreement between novices, experts, and their combined scores. A one-way ANOVA was even computed on
the original list, where experts and novices generated brands, which never made it to the final category of ‗sponsorship‘.
This too found there to be no significant difference between experts and novices.
Conclusions
This study suggests that a goal-derived category of ‗sponsorship‘ exists and that sport expertise can be an important
factor for the criteria chosen for sponsor typicality. ‗Ideals‘ was found to have a significant unique correlation with
typicality for novices but not experts. Assuming that novice subjects base their ‗ideal‘ judgments of sponsors on some
form of (relevancy) congruity basis (cf. Fleck and Quester, 2007), this would support previous sponsorship literature,
which has found a positive relationship between constructs such as ‗match-ups‘ or ‗fit‘ and brand evaluations. This has
previously led to academics and practitioners articulating ‗fit‘ communication messages between the brand and the
sponsee/event, which can often be a difficult task for many products. However, experts were found to judge typicality
by ‗frequency of instantiation‘ (FoI), which suggests that repeated (and long-term) exposure to the sponsoring brand
will indeed produce more favourable typicality judgments for those consumers who follow sport more often, which is
usually the target market a brand aims for when undertaking a sponsorship arrangement. Subjects‘ responses to
sponsorship usually involve long-term memory even though short-term responses are usually measured in a laboratory
setting (Cornwell et al., 2000). The importance of ‗FoI‘ for ‗experts‘ in this study not only indicates that the measures
used were appropriate but also suggests that long-term memory was accessed by some subjects, further contributing to
our understanding of sponsorship research. The results should give both sponsoring brands and sports properties some
confidence that, otherwise-incongruent brands can improve their sponsorship typicality ratings by taking a long-term
view of the sponsorship deal. It also suggests that articulating a message about the length of time a brand has been
associated with sponsorship could enhance the connection between the sponsor and sponsee/event, subsequently
increasing sponsor typicality ratings. This is because consumers may start to believe a brand has been sponsoring a
specific event for longer than it has because of specific communication about other previous sponsorships. Articulating
this message is often a lot easier than a ‗fit‘ or ‗match-up‘ communication for many brands. Sponsorship residual effects
should also be increase using this type of communication message as well.
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