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

Robert Madrigal, Jesse King
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引用次数: 0

Abstract

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.

超越简单的 "是 "或 "否":利用信号检测理论衡量赞助商识别的准确性
目的赞助商识别准确度通常是根据消费者在被问及某品牌是否为赞助商时回答 "是 "的百分比(命中率)来评估的。然而,这没有考虑到错误归属(对非赞助商品牌回答 "是";误报)。错误归属反映了消费者的混淆,削弱了官方赞助的好处,为非赞助对手提供了优势,降低了品牌的赞助投资回报。在信号检测理论(SDT)的启发下,我们展示了如何使用一种称为 d-prime(d')的敏感度测量方法将命中与误报区分开来。设计/方法/途径在研究 1 中,我们报告了一项实验的结果。研究结果d'和 c 的使用优于统计 "是 "的回答,因为它们能准确反映赞助商的归属以及对非赞助商竞争对手的错误归属。原创性/价值在赞助商方面,我们展示了如何使用 Excel 轻松计算 d' 和 c。我们的研究还包括一项实验研究,以确定假设的效果,然后在实地环境中复制结果。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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