Firm Performance of Saudi Manufacturers: Does the Management of Cash Conversion Cycle Components Matter?
Bibliographic record
Abstract
The purpose of this study is to examine the liquidity management of a corporation. It aims to examine how managing cash conversion cycle components affects corporate performance. A dataset of 88 firms listed on the Saudi Stock Exchange between 2018 and 2022 was analyzed using both pooled OLS and fixed effects regression models. A sample of 84 firms listed on the Saudi Stock Exchange for the period from 2018 to 2022 was used. Both the pooled OLS and the fixed effects regression models were used. This study’s key findings are: (1) there is a strong negative correlation between the time it takes to convert inventory into sales (inventory conversion period) and firm performance. If inventory does not sell quickly, profit tends to be lower. (2) Firm performance demonstrates a strong inverse relationship with the duration it takes for companies to collect cash from customers, commonly known as the accounts receivable collection period. A short accounts receivable collection period may become collectible and increase a business’s profitability and performance. (3) There is a highly significant negative link between the time taken to pay creditors (days payable outstanding) and firm performance. A short average payment period, indicated by a low payment period, suggests that the firm is promptly settling its bills and obligations without any delays.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".