The effect of integrated information technology on competitive advantage through supply chain integration and supply chain flexibility
Bibliographic record
Abstract
Unlimited global competition for manufacturing companies means that companies must be efficient and effective, so implementing information technology is needed to produce fast and precise decisions. Manufacturing companies in East Java were obtained with a sample size of 89 companies as research respondents and processed using smart PLS. The research results show that the implementation of continuously adjusted information technology can provide improvements in supply chain integration, supply chain flexibility, and competitive advantage. Connections with external partners and transactions using information technology make it easier to coordinate internally and externally in decision making. Supply chain integration, which is described as collaborating with partners and involving them in decision making, can increase supply chain flexibility and competitive advantage. Manufacturing companies can increase flexibility in processes and develop new products, increasing market share and customer satisfaction compared to competitors. The company's supply chain flexibility can increase its competitive advantage. This research provides a practical contribution to company management in appropriately managing business strategy and in line with external changes. For operational practitioners, it includes enlightenment in maintaining the role and function of up-to-date information technology to make decision making easier. The theoretical contribution of the research is to enrich the theory's resources-based view on competitive advantage and operational system integration.
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 | 0.004 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.004 |
| Open science | 0.001 | 0.004 |
| 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".