Engaging with (vs. avoiding) personalized advertising on social media
Abstract
This study investigates binomial consumer brand engagement (vs. advertising avoidance) in the social media context. Grounded in social exchange theory, the relationships between personalized advertising, information control, privacy concerns, advertising avoidance, and consumer brand engagement are analyzed by drawing on a survey comprising n = 429 participants. The findings reveal that personalized advertising boosts brand engagement while also reducing privacy concerns. Additionally, it has been discovered that privacy concerns do not have a significant impact on consumers’ engagement with a brand. Overall, this study demonstrates that consumers can recognize personalized advertising and are open to relying on it.
Study specs
Grounded in social exchange theory, the study utilized a quantitative survey to assess relationships between personalized advertising, information control, privacy concerns, advertising avoidance, and brand engagement.
- Authors
- SMC Loureiro,L Hollebeek,RA Rather
- Institution
- Universitário de Lisboa
- Discipline
- Marketing,Behavioral Science
- Sample Size
- N=429
- Study Type
- Survey Research
- Year
- 2025
- Human Data Platform
- Prolific
- Source
- View Source Google Scholar
Measured Outcomes
The interplay between personalized advertising, consumer brand engagement, privacy concerns, information control, and advertising avoidance.
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.
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