AI Aversion Alleviated by Ethical Leadership: A Prescription for Tech Innovation
A new study suggesting that artificial intelligence (AI)-generated ads leave consumers feeling 'annoyed', 'boring', and 'confused' may not necessarily result in negative brand perceptions, if the corporation displays 'leadership for the greater good'. The authors of the study define this type of leadership as prioritizing societal well-being in conjunction with an organization's success, for instance, by adopting environmentally sustainable practices.
To determine if brands demonstrating this form of leadership could lessen consumer skepticism and improve brand image, four studies were conducted. In the first study, participants were divided into two groups, one exposed to non-AI generated ads and the other to AI-generated ads. The participants found the AI ads less credible and expressed less favorable attitudes towards the brand.
The second study, similar to the first, included the added element of leadership. Participants were grouped into four categories: AI ads paired with a stated leadership approach and AI ads without a stated leadership approach, as well as non-AI ads under the same conditions. Surveys were conducted to measure credibility and brand attitudes, with the results showing that a brand perceived to demonstrate leadership for the greater good could reduce negative consumer reactions to AI-generated ads.
The third study closely mimicked the second but only displayed AI-generated ads, and the brand's leadership approach was presented more subtly. Nonetheless, the findings stayed consistent.
In the final study, purchase intentions were evaluated, exploring the impact of leadership for the greater good on consumer willingness to buy. The researchers concluded that for brands utilizing AI for ad generation, perceptions of such leadership lead to higher brand credibility, more positive brand attitudes, and, consequently, higher purchase intentions.
The study's findings suggest that AI-generated ads can harm brand perceptions, but this negative impact is not uninterrruptable and can be significantly influenced by the perceived intentions of the brand in relation to leadership. Brands should link their AI advertising strategies with leadership for the greater good and consider when and how to disclose the use of AI. However, government regulations may soon mandate AI disclosure, so brands should also weigh the benefits of remaining undisclosed.
The study, titled "Easing AI-advertising aversion: how leadership for the greater good buffers negative response to AI-generated ads," was written by Sean Sands, Vlad Demsar, Carla Ferraro, Sam Wilson, Melissa Wheeler, and Colin Campbell.
Implications for Brands:
Brands can reduce negative consumer reactions to AI-generated ads and increase purchase intentions by addressing key concerns and fostering trust through strategies such as transparency, ethical use of AI, education, social responsibility, sustainability, community involvement, personalization with integrity, innovative and positive messaging, and feedback mechanisms. By adopting these strategies, a brand can show leadership in using AI responsibly and for the greater good, potentially reducing negative reactions and increasing consumer trust and purchase intentions.
- To minimize negative consumer reactions towards AI-generated ads and boost purchase intentions, brands could consider strategizing transparency, ethical AI usage, educational initiatives, social responsibility, sustainability, community engagement, responsible personalization, positive and innovative messaging, and feedback mechanisms.
- The implementation of such strategies would not only exhibit a brand's responsible use of AI but also portray their commitment to social welfare, potentially improving consumer trust and purchase intentions.
- Given that government regulations regarding AI disclosure may soon be implemented, brands must carefully evaluate the benefits of remaining undisclosed while considering the potential advantages of displaying their AI-leadership approach.
