The role of content marketing and influencer marketing strategies and banking guarantees in SMEs bankruptcy addressing
Bibliographic record
Abstract
Micro, Small and Medium Enterprises (MSMEs) face challenges in business growth and often face bankruptcy due to various factors, such as declining consumer demand for their products and the rapid advancement of digital marketing technologies. This shift in consumer behavior towards digital platforms has particularly affected MSMEs in Medan City, leading to prolonged closures. To address this issue, this study aims to investigate the effectiveness of content marketing and influencer marketing strategies, along with the role of banking guarantees, in mitigating bankruptcy risks for MSMEs in the region. Using data analysis via SmartPLS software, the findings reveal that while content marketing alone doesn't show a direct positive impact, it significantly contributes to banking guarantees. Additionally, influencer marketing plays a significant role in enhancing banking guarantees and mitigating bankruptcy risks. Moreover, banking guarantees serve as a crucial intervening variable, amplifying the impact of both content and influencer marketing strategies in overcoming bankruptcy challenges for MSMEs.
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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 | 0.009 |
| 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 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".