Identity‐Based Autonomy and Its Impact on Connections With Identity‐Linked Products and Brands
Bibliographic record
Abstract
ABSTRACT We introduce a new characteristic of identity into the identity‐based consumer behavior literature called identity‐based autonomy : the autonomy (i.e., freedom from others' influence) versus heteronomy (i.e., real or perceived external influence) that people feel when enacting an identity. This new dimension thus captures how consumers enact a salient identity in an identity‐relevant context. We create a new measure of identity‐based autonomy to capture how this dimension varies across identities. We show that autonomous identities foster a greater sense of control over when, where, and how one uses identity‐linked products. As a result, we demonstrate that people feel stronger connections to products and brands linked to autonomous (vs. heteronomous) identities. We reveal this effect across both common consumer identities (e.g., gender, family) and unique, participant‐generated identities (e.g., work identity, sports fan identity). Importantly, we identify theoretically motivated moderators that underscore the roles of identity (i.e., identity‐relevance) and autonomy (i.e., self‐directed vs. externally imposed) in driving the IBA effect on brand and product connections. This research contributes to the identity literature by highlighting autonomy as a meaningful and measurable feature of identity that shapes consumer‐brand relationships.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".