The impact of procurement card usage on cost reduction, management control, and the managerial audit function
Bibliographic record
Abstract
Purpose Provide a better understanding of the functionalities and benefits of the procurement card technology (P‐Card), and examines the card's impact on management control and the audit function. Design/methodology/approach Describes the recent published works on P‐Card's benefits in costs reduction and data integration with information systems, aiming to provide comprehensive research and practical advices. Findings Provides information about the impact of P‐Card on business processes, along with opportunities to set managerial reports. The future of P‐Card technology is elaborated in order to broadening P‐Card usage. Research limitations/implications To explain the determinants of and outcomes from the adoption and usage of P‐Card, contingency variables such as size, business environment, and structure may be examined. Also, studies on P‐Card have only used the survey method as the way to gather information, while interviews, observation, and system documentation examination should be performed to corroborate the survey results obtained. Intangible benefits such as improved decision‐making, better management control, or improved job satisfaction should be considered to provide more robust assessment of P‐Card usage and benefits. Practical implications A useful source of information to help management auditors to take proactive approaches to improve business efficiency, design effective control systems, and streamline accounting processes. Originality/value The paper describes ways to integrate P‐Card data directly to computer‐based accounting information systems via electronic posting to the ledger offered by software capabilities.
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 | 0.008 | 0.048 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".