The bittersweet of consumer–human brand relationships in the social media context
Bibliographic record
Abstract
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.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.003 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".