Should Facebook advertisements promoting a physical activity smartphone app be image or video-based, and should they promote benefits of being active or the app attributes?

Translational behavioral medicine(2021)

引用 1|浏览3
暂无评分
摘要
Social media provides a convenient platform for health campaigns. However, practitioners designing such campaigns are faced with a number of decisions regarding advertising formats and appeals. This study set out to compare the effectiveness of two advertising formats (image vs. video) and two advertising appeals (benefits of being active vs. app attributes and features) for promoting a physical-activity smartphone app. The advertising experiment was conducted on Facebook and employed a 2 × 2 full-factorial experimental design, examining two advertising formats: image versus video and two advertising appeals: benefit versus attribute. Outcome measures were advertisement cost (number of viewers reached according to the amount spent) and consumer engagement (rates of advertisement click-through and app downloads). Chi-Square analysis revealed that advertisement cost was found to differ according to the type of advertising format used, with image advertisements achieving a greater audience reach than video advertisements (χ 2(1) = 905.292, p < .001). Consumer engagement also differed according to advertising format and appeal: images achieved high rates of advertisement click-through (2.7% vs. 1.9%; χ 2(1) = 196.9, p < .001) and app downloads (0.6% vs. 0.5%; χ 2(1) = 4.0, p = .044) compared with videos. Furthermore, benefit appeal advertisements were more effective than attribute appeals, yielding a greater rate of advertisement click-through (2.8% vs. 1.8%; χ 2(1) = 282.2, p < .001) and app downloads (0.7% vs. 0.4%; χ 2(1) =106.0, p < .001). Overall, image advertisements were seen to be the most cost-effective and engaging. Advertisements employing a benefit appeal achieved greater consumer engagement than and attribute appeal advertisements.
更多
查看译文
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要