Charting sustainable routes: Navigating uncertainty in the supply chain for lasting loyalty
Bibliographic record
Abstract
This study investigates the dynamics of literacy, trust, awareness, loyalty, and corporate sustainability among Social Security Management Agency (BPJS) of Employment. Social Security Management Agency (BPJS) of Employment in West Java, Banten, and Lampung Province, Indonesia. Employing structural equation modeling, the study reveals significant relationships and mediating effects. Literacy emerges as a key factor, positively impacting loyalty and corporate sustainability. Trust is found to directly influence corporate sustainability but not loyalty. Awareness significantly affects both loyalty and corporate sustainability. Notably, loyalty plays a mediating role in the relationships between literacy, awareness, and corporate sustainability. These findings contribute to literacy and engagement models, sustainability frameworks, and mediating models in organizational literature. Practical implications include the recommendation for literacy enhancement programs, multifaceted trust-building strategies, proactive communication campaigns, and loyalty-building initiatives. Future research recommendations encompass longitudinal studies, diverse organizational settings, mixed-methods approaches, exploration of moderating variables, and intervention studies. The research contributes to a nuanced understanding of participant engagement and sustainability perceptions, offering actionable insights for organizations, particularly those in the public sector like Social Security Management Agency (BPJS) of Employment.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.005 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".