I Am, Therefore I Buy: Low Self-Esteem and the Pursuit of Self-Verifying Consumption
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract The idea that consumers use products to feel good about themselves is a basic tenet of marketing. Yet, in addition to the motive to self-enhance, consumers also strive to confirm their self-views (i.e., self-verification). Although self-verification provides self-related benefits, its role in consumer behavior is poorly understood. To redress that gap, we examine a dispositional variable—trait self-esteem—that predicts whether consumers self-verify in the marketplace. We propose that low (vs. high) self-esteem consumers gravitate toward inferior products because those products confirm their pessimistic self-views. Five studies supported our theorizing: low (vs. high) self-esteem participants gravitated toward inferior products (study 1) because of the motivation to self-verify (study 2). Low self-esteem consumers preferred inferior products only when those products signaled pessimistic (vs. positive) self-views and could therefore be self-verifying (study 3). Even more telling, low self-esteem consumers’ propensity to choose inferior products disappeared after they were induced to view themselves as consumers of superior products (study 4), but remained in the wake of negative feedback (study 5). Our investigation thus highlights self-esteem as a boundary condition for compensatory consumption. By pinpointing factors that predict when self-verification guides consumer behavior, this work enriches the field’s understanding of how products serve self-motives.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it