How and when personalized advertising leads to brand attitude, click, and WOM intention
Abstract
We study the effect of perceived personalization in advertising on social networking sites (SNSs) on consumer brand responses. In study 1 (*N* = 202), we test a parallel mediation via perceived personal relevance and intrusiveness on brand attitude (Ab) and click intention (CI). Perceived personalization improves Ab and CI by increasing the perceived personal relevance and, unexpectedly, by decreasing the perceived intrusiveness of the ad. Study 2 (*N* = 264) extends the processing mechanism of personalized advertising by additionally including the mediating effects of self-brand connection and reactance toward the ad. Perceived personalization has a positive indirect effect on self-brand connection via perceived personal relevance, but not via perceived intrusiveness. Self-brand connection, in turn, has a positive effect on consumers' responses. Contrary to expectations, reactance does not significantly affect brand responses. Study 2 also examines the moderating role of perceived privacy protection by the SNS. Higher levels of perceived privacy protection by the SNS do not strengthen the indirect effects of perceived personalization.
Study specs
- Authors
- F De Keyzer,N Dens,P De Pelsmacker
- Institution
- Erasmus University Rotterdam
- Discipline
- Marketing
- Year
- 2022
- Human Data Platform
- Prolific
- Source
- View Source DOI Google Scholar
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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