Enhancing SME Performance through Strategic Social Media Utilization
Bibliographic record
Abstract
With the rise of online retailers and social media users, the knowledge and proficiency of SMEs in using social media to advertise products and increase sales have become essential. Everyday life is significantly impacted by social media; as of January 2023, there were 167 million active users, representing 60.4% of Indonesia’s total population. This study aims to encourage SMEs to utilize social media strategically to address various challenges they face. Data were collected using Google Forms from 130 active social media users in Indonesia between January 2024, concentrating on the use of social media by SMEs. Utilizing Partial Least Squares Structural Equation Modeling (PLS-SEM) and the SMART-PLS 4 software, the study analyzed the impact of social media adoption on SME performance. The results show that social media adoption greatly improves SME performance, with four out of five variables showing significant results. The research methodology revealed that four of the five variables in the study were significant due to the exploration of Technological Impact, Perceived Utility, Perceived Usability, Compatibility, Adoption of Social Media, and Performance of SMEs. Twenty-four of the 28 indicators were found to be reliable. Comparing these results with SMEs in other regions highlights the unique aspects of the Indonesian context, such as the rapid increase in internet penetration and the specific challenges faced by SMEs in this region.
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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 | 0.004 |
| 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 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".