Impact of the Application of IFRS 15 on the Profitability of Jordanian Telecom Companies (Case Study: Jordan Orange Telecom)
Bibliographic record
Abstract
This study investigates the impact of applying IFRS 15 standard on the profitability of Jordanian telecommunications companies (case study: Jordan Orange Telecom). The study addresses three independent variables, namely, contract revenue, customer contract assets and customer contract liabilities and their impact on the ROA, ROE and PM in Orange Jordan. The researcher has investigated the relationship and impact of the independent variables on dependent variables by analysing financial statements after applying IFRS15 for 2017, 2018, 2019. The study concludes that there is a relationship and impact between contract revenue and ROA and ROE, where the correlation coefficient amounted to (99.8, 99.0) respectively, with a level of significance (0.02, 0.04). The coefficient of determination (R2) amounted to (99.6%), indicating that contract revenue interprets (99.6%) of the return on assets in Orange Telecom. Also, the coefficient of determination (R2) amounted to (99.9%), indicating that contract revenues interpret (99.9%) of the ROE in Orange Telecom. The correlation coefficient between contract revenue and PM amounted to (99.2) with a level of significance (0.08), where a correlation relationship is noticed but with a level of significance exceeding (0.05). As for customer contract assets and customer contract liabilities, no relationship or impact is found betweem these and ROA, ROE and PM in Jordan Orange Telecom.Accordingly, the study recommends that companies in Jordan should abide by IFRS15 in order to increase corporate profitability.
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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 | 0.004 | 0.013 |
| 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 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".