Market entry agility in the process of enhancing firm performance: A dynamic capability perspective
Bibliographic record
Abstract
Social media empowers small and medium enterprises (SMEs) in engaging with their stakeholders economically and effectively. Social media use affects SMEs operation and knowledge sharing which creates innovation opportunities, speeds time to market, satisfies firm's customers, and improves business performance. Current research aims to explore the impact of social media use on the market entry agility, product innovativeness, reducing the market entry time for the SME and improving the firm performance. Cross-sectional survey-based data was collected from the jewelry crafting SMEs in Bali, Indonesia. The data was analyzed with the covariance-based statistical analysis technique with the SPSS based AMOS 23. The study results identify that social media use and market entry agility significantly impact firm performance. However, product innovativeness insignificantly influences firm performance. Furthermore, the market entry agility mediates for the firm performance so, SMEs need to leverage social media use, and market entry agility enables the dynamic capacity to enhance firm performance. Firms’ level innovativeness capability should be considered as a mediating role but should support another variable to leverage firm performance. The study limitation and future research options are reported at the end.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| 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".