Beyond a simple yes or no: using signal detection theory to measure sponsorship identification accuracy

Purpose: Sponsorship identification accuracy is typically assessed as the percentage of consumers answering “yes” when asked if a brand is a sponsor (hits). However, this fails to consider misattribution (answering “yes” for a non-sponsor brand; false alarms). Misattribution reflects consumer confusion and dilutes the benefits of an official sponsorship, offers an advantage to a non-sponsoring rival and reduces a brand’s return on sponsorship investment. Informed by signal-detection theory (SDT), we show how hits may be disentangled from false alarms using a measure of sensitivity called d-prime (d’). A related measure of response bias (c) is also discussed. Design/methodology/approach: In Study 1, we report the results of an experiment. In Study 2, we rely on a field study involving actual sponsors and fans. Findings: The use of d’ and c is superior to tallying “yes” responses because they account for accurate sponsor attribution and misattribution to non-sponsor competitors. Originality/value: In the context of sponsorship, we demonstrate how d’ and c can be easily calculated using Excel. Our research also includes an experimental study that establishes the hypothesized effects and then replicate results in a field setting.