The effect of market information on export performance: The mediating role of notional export support and encouragement
Bibliographic record
Abstract
Global competition and economic globalization are forcing countries' governments to devise encouragement support programs and formulate exporting policies to help SMEs export, compete, and survive in foreign markets. This study examines the mediating role of national export support and encouragement programs (NESEPs) in clarifying the impact of determinants of market environment information on the SMEs' export financial performance (EXFP) and SMEs' export strategic performance (EXSP) based on a resource-based view, which helps understand novel holistic relationships between them. Data from 106 exporting SME firms working in the Kingdom of Saudi Arabia were collected using an online questionnaire survey. Regression path analysis from structural equation modeling was used to test the study's model relationships. The most important positive findings that emerged from this study are: Determinants of market information acquisition (DMIA) will positively influence SMEs' EXFP and NESEP. Determinants of market information dissemination (DMID) will positively influence SMEs' EXSP. Determinants of market information responsiveness (DMIR) will positively influence NESEP. NESEP will positively mediate the effect of DMIA and DMIR on SMEs' EXSP. The study’s theoretical and social implications and limitations are discussed in the concluding sections. Furthermore, directions for future research are provided.
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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 | 0.016 |
| 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 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".