The effect of ERP on firm performance through information quality and supply chain integration in Covid-19 era
Bibliographic record
Abstract
The disruption caused by the COVID-19 pandemic has an imbalance between demand and supply in Indonesia's manufacturing industry. The products needed to handle the spread of the virus are in high order, and there is even a product shortage. Products that are not required to prevent a pandemic have stagnated so that the manufacturing industry suddenly reduces capacity. Manufacturing companies need to coordinate quickly to be able to adjust the disruption. Manufacturing companies already have an integrated information technology system that has been the primary process in Enterprise Resources Planning (ERP). Manufacturing companies make ERP their primary system, so they need to be updated and adjusted as needed. This study obtains a questionnaire from Indonesia's manufacturing industry using the google form link and distributed through social media WhatsApp, Facebook and Instagram. Data processing was carried out by using the partial least square of 285 manufacturing company respondents. The results showed that ERP sustainability was able to influence supply chain integration (internal and external). External integration has an impact on information quality, while internal integration does not affect. Supply chain integration and information quality affect increasing firm performance. Research makes a practical contribution to the industry in optimizing ERP systems in Pandemic conditions and a theoretical contribution to ERP sustainability as a mainstay in supply chain integration.
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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 | 0.005 | 0.020 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".