The central role of IT capability to improve firm performance through lean production and supply chain practices in the COVID-19 era
Bibliographic record
Abstract
Today, global competition entails companies having an advantage in supply chain networks to pursue superior performance. This work examines the link between information technology (IT) capability with the firm performance by adopting a lean production approach, vendor-managed inventory, and supply chain practices. The study has surveyed the population of the manufacturing company in East Java, Indonesia, using a questionnaire with a five-point Likert scale. A total of 111 manufacturing companies (medium and large) were selected from 5420 manufacturing companies listed in the Industrial Department of East Java. The partial least square (PLS) technique was used to analyze the data, using the SmartPLS software version 3.3. Thirteen hypotheses in this study were developed to investigate. The result revealed that all hypotheses of direct relationship were supported. IT capability directly affects lean production, vendor managed inventory, and supply chain practices. Moreover, lean production, vendor-managed inventory, and supply chain practices improve firm performance. Further analysis also indicated that all hypotheses of indirect hypotheses were supported except hypothesis one hypothesis (H9). IT capability indirectly improves firm performance through lean production, vendor-managed inventory, and supply chain practices. The result provides insight for managers and policymakers on enhancing firm performance by improving its IT capability, adopting lean production, vendor-managed inventory, and supply chain practices. This research contributes to reinforcing the supply chain management theory.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| 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 | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".