Compensation‐based incentives, ERP and delivery performance
Bibliographic record
Abstract
Purpose The purpose of this paper is to investigate the role of compensation‐based incentives in relationships between enterprise resource planning (ERP) usage and delivery performance in manufacturing. Design/methodology/approach The authors carry out two studies exploring links between ERP, incentives, and performance from alternative perspectives: first, of incentives tied to regular production activities, and their relationship with delivery performance advantage over competitors; second, of incentives tied to improvement activities and their relationship with delivery performance improvements. Statistical analysis is carried out on data from 698 metal‐working manufacturers from 22 countries, giving a broad cross‐sectional view of a global industry. Findings The studies indicate that ERP usage relates positively with both delivery advantage and delivery improvements. Furthermore, incentives tied to improvement initiatives may explain delivery improvements, both directly and as moderators in the relationship between ERP and performance. Research limitations/implications The results suggest that ERP adoption can be framed as a principal‐agency phenomenon where performance outcomes are partially influenced by incentives. Practical implications The results imply that incentives tied to improvement initiatives may foster employee engagement with the new ERP, leading to stronger delivery performance benefits. Originality/value To the best of the authors' knowledge, this is the first research to explore ERP usage as a principal‐agency problem, and to analyse its relationships with incentives under alternative performance perspectives. The results may significantly contribute to the knowledge of ERP‐performance relationships and the role of incentives.
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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 | 0.007 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".