Modeling the relationship between business intelligence, supply chain integration, and firm performance: Empirical study
Bibliographic record
Abstract
This study aims to model the relationship between business intelligence (BI), supply chain integration (SCI), and firm performance in Jordanian small and medium-sized enterprises (SMEs) using structural equation modeling (SEM). The study utilizes a sample of 400 SMEs from different sectors in Jordan to investigate the hypothesized relationships between the constructs. The results show that BI positively influences SCI and firm performance in Jordanian SMEs. Moreover, SCI was found to mediate the relationship between BI and firm performance. The study also found that the impact of BI on firm performance is fully mediated by SCI, suggesting that SCI plays a crucial role in enhancing firm performance in the context of Jordanian SMEs. The study makes several significant contributions to the literature on supply chain management and business intelligence in the context of SMEs. First, it provides empirical evidence of the positive impact of BI on SCI and firm performance. Second, it sheds light on the mediating role of SCI in the relationship between BI and firm performance. Third, the study contributes to the limited literature on supply chain management and business intelligence in the context of Jordanian SMEs. The findings of this study have practical implications for managers of SMEs in Jordan. They highlight the importance of investing in BI tools and strategies to enhance SCI and firm performance. Additionally, the study suggests that managers should focus on improving their firms' SCI practices to fully exploit the benefits of BI and improve their overall performance. Overall, this study provides new insights into the relationship between BI, SCI, and firm performance in the context of Jordanian SMEs. The study's findings can guide policymakers, researchers, and practitioners in developing and implementing effective strategies to improve supply chain management and business intelligence practices in SMEs, particularly in emerging markets such as Jordan.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".