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 distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".