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Record W7082249906 · doi:10.1002/mar.70033

Identity‐Based Autonomy and Its Impact on Connections With Identity‐Linked Products and Brands

2025· article· en· W7082249906 on OpenAlexaff

Bibliographic record

VenuePsychology and Marketing · 2025
Typearticle
Languageen
FieldComputer Science
TopicGeochemistry and Geologic Mapping
Canadian institutionsUniversity of Ottawa
Fundersnot available
KeywordsAutonomyIdentity (music)HeteronomySalientProduct (mathematics)Dimension (graph theory)Perspective (graphical)Self

Abstract

fetched live from OpenAlex

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.

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.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.386
Threshold uncertainty score0.423

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.013
GPT teacher head0.305
Teacher spread0.292 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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