The role of relational and informational capabilities in mediating the effect of social media adoption on business performance in fashion industry
Bibliographic record
Abstract
This study aims to explain the role of relational capability and informational capability in mediating the effect of social media adoption on business performance. The population of this study is the owners of the fashion sector SMEs in Bali. The sample size used was 114 businesses with a purposive sampling approach. The analytical technique used is Path Analysis using the SEM-PLS approach. The results show that the adoption of social media has a positive and significant effect on business performance. Social media adoption has a positive and significant effect on relational capability and social media adoption also has a positive and significant effect on informational capability. Furthermore, relational capability has a positive and significant effect on business performance and informational capability has a positive and significant effect on business performance. Relational capability and informational capability can significantly mediate the effect of social media adoption on business performance. Therefore, it is important for SME owners in the fashion sector in Bali to intensify the adoption of social media to build relational and informational capabilities in order to increase business performance.
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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 | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.000 | 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".