Key user ERP capability maintaining ERP sustainability through effective design of business process and integration data management
Bibliographic record
Abstract
Business competition is increasingly complex, and there are no clear boundaries between products, so company operational processes are needed efficiently and effectively. The performance achieved is obtained through the implementation of an integrated information technology known as ERP. ERP implementation requires a person in charge of a business function called a key user who understands business processes and collaborates with ERP system vendors. The study obtained data that can further process from 77 manufacturing companies in East Java by purchasing an ERP or self-development package. Data processing uses PLS to answer all research hypotheses. The results show that key user capability was able to have a direct effect on the effective design of business processes of 0.643 and integration data management of 0.373. In contrast, it had no direct impact on ERP suitability. Effective Design of Business Processes has an immediate effect on Integration Data Management of 0.338 and ERP suitability of 0.395. The results also show that Integration Data Management has a direct effect on ERP suitability of 0.462. The data processing results for the indirect effect showed that key user capability influenced ERP suitability through the effective design of business processes of 0.507. Key user capability affects ERP suitability through Integration Data Management, and it is obtained as much as 0.254. The last hypothesis, key user capability, influences ERP suitability through effective Design of Business Processes and Integration Data Management of 0.182. The study results provide theoretical contributions to ERP implementation success factors, while practical gifts give key users a good understanding of the company's business processes and ERP systems.
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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 | 0.026 |
| 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 | 0.003 | 0.003 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 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".