Impact of profitability on investment opportunities and its effect on profit sustainability
Bibliographic record
Abstract
This study aimed to clarify the relationship between profitability, investment opportunities, and sustainable profits for industrial companies listed on the Amman Stock Exchange. The population consisted of all 53 industrial companies on the Exchange in 2022. Financial data was obtained for the period 2018-2022, including metrics on investment opportunities and profitability. Using statistical analysis like regression testing, we assessed the data to test hypotheses and reveal insights. Existing research presents mixed findings; some studies suggest a positive link between investment chances and financial performance and profit durability, while others make the opposite claim. Our results contribute uniquely to this ongoing debate by providing statistical evidence that investment opportunities have a robust, positive impact on profit sustainability. Additionally, profitability strongly influences both investment opportunity indicators and profit sustainability for these industrial companies. These conclusions assist stakeholders in justifying financial acquisitions and allocating capital to potential investments. The findings also aid in shaping short and long-term investment approaches aligned with company goals and policies. This can increase value creation for shareholders by enhancing profitability and sustaining profits over time. In summary, by quantitatively demonstrating the interconnected nature of profitability, investment opportunities and durable profits, this study equips decision makers to make informed strategic choices that promote growth and sustainability. The analysis methods and results add valuable insights to academic and practical understanding of these relationships within Jordan's industrial sector.
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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 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".