Do you create your content yourself? Using generative artificial intelligence for social media content creation diminishes perceived brand authenticity
Abstract
Recent studies have demonstrated the potential of generative artificial intelligence (GenAI) in enhancing marketing content. However, its impact on consumer behavior has remained empirically untested. In response to social media platforms mandating the disclosure of GenAI content, we investigate how followers perceive brands that use GenAI for content creation. Drawing from literature on algorithm aversion and brand authenticity, the results of three experimental studies indicate that brands' GenAI adoption induces negative attitudinal and behavioral follower reactions. These effects are mediated by followers' perceptions of brand authenticity and can be triggered by GenAI disclosure. Negative reactions are attenuated if GenAI is used to assist humans in content creation rather than to replace them through automation. Our findings underscore the need for nuance in brands' GenAI adoption to unlock economic benefits without compromising on relationships with consumers.
Study specs
Three experimental studies investigating consumer perceptions and reactions toward brand disclosure of GenAI usage in content creation.
- Discipline
- Marketing
- Study Type
- Experimental Study
- Year
- 2024
- Human Data Platform
- Prolific
- Source
- View Source DOI Google Scholar
Measured Outcomes
Followers' attitudinal and behavioral reactions, mediated by perceptions of brand authenticity.
Peer Review & Critical Discussion
Potential Selection Bias in 2023 Cohort
The participant pool shows a concerning overrepresentation of users from high-income demographics. Looking at Table 3, we can see that 78% of respondents had annual incomes above $75k, which significantly limits the generalizability of these findings to broader populations.
Non-naive Participants Issue
I've noticed a methodological concern regarding participant naivety. Given that Prolific users often complete multiple studies, there's a real risk that participants had prior exposure to similar experimental paradigms, which could confound the results.
RLHF Applicability to This Study Design
The implications for RLHF training pipelines are understated. If we accept the authors' conclusions about preference stability, this has direct consequences for how we should structure reward model training. The temporal decay effect described in Section 4.2 is particularly relevant.
Verify your expertise to join discussion
Create an account and verify your credentials to participate in peer discussions.