When social media facilitates the dark side of consumer–human brand relationships: an investigation into social media-induced sleep problems
Bibliographic record
Abstract
Purpose This research investigates the unintended adverse outcomes of consumer–human brand relationships facilitated by social media, particularly the impact of human brand attachment on social media-induced sleep problems. The moderating roles of self-regulation and need-fulfillment focus are examined. Design/methodology/approach A total of 497 valid responses from Indonesian consumers (Study 1) and 273 from US consumers (Study 2) were analyzed using structural equation modeling to empirically evaluate the proposed research model. Findings The results showed that stronger human brand attachment contributed to problematic human brand engagement on social media, which subsequently led to social media-induced sleep problems (i.e. poor social media sleep hygiene, problematic sleep and exhaustion). These effects were intensified by higher self-regulation, especially when consumer need-fulfillment was more promotion-focused (vs prevention-focused). Originality/value This research is among the few studies to highlight the dark side of consumer–human brand relationships in the digital realm. It advances research on social media-induced sleep problems from the perspective of consumer–human brand relationships, offering insights to consumers, parents and governments to inform preventive measures.
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 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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".