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Record W4388486715 · doi:10.1002/mar.21932

The bittersweet of consumer–human brand relationships in the social media context

2023· article· en· W4388486715 on OpenAlexaff
Andreawan Honora, Maryam Memar Zadeh, Nicole Haggerty

Bibliographic record

VenuePsychology and Marketing · 2023
Typearticle
Languageen
FieldSocial Sciences
TopicImpact of Technology on Adolescents
Canadian institutionsWestern UniversityUniversity of Winnipeg
Fundersnot available
KeywordsPsychologyContext (archaeology)Consumption (sociology)Social mediaSocial psychologyAdvertisingAttachment theoryBusinessSociology

Abstract

fetched live from OpenAlex

Abstract The current research proposes an integrated model to investigate both the bright and the dark sides of consumer–human brand relationships facilitated by social media on consumers' lives. Grounded in the duality of social media and self‐regulation theory, the findings show that human brand attachment improves consumers' daily performance through stress relief, which in turn increases life satisfaction (Study 1). However, the findings also indicate that human brand attachment can cause consumers' daily performance to deteriorate as a result of compulsive human brand consumption on social media and human brand‐personal conflict, which diminishes life satisfaction (Study 2). Collectively, the findings may suggest that strong consumer–human brand relationships tend to be detrimental to consumers' well‐being as the indirect negative impact of human brand attachment on daily performance and life satisfaction overpowers its indirect positive impact (Study 3). Such detrimental effects are moderated by self‐regulatory focus (Study 4). Moreover, the findings indicate that the indirect negative effect of human brand attachment is attenuated when consumers have a higher level of work/study–life balance. Accordingly, the current research advances the theoretical understanding of the consumer–human brand relationship facilitated by social media, by highlighting its dual effects associated with the nature of technology and consumers' self‐regulatory focuses.

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)0.008
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesScience and technology studies
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.152
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0020.001
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 head0.070
GPT teacher head0.386
Teacher spread0.316 · 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.

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

Citations13
Published2023
Admission routes1
Has abstractyes

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