The Relationship between Accounting Information Systems and Making Investment Decisions in the Industrial Companies Listed in the Saudi Stock Market
Bibliographic record
Abstract
The study aimed to measure the relationship between the investment decision-making in the industrial companies listed in the Saudi Stock Market with the (IVs) characteristics of the accounting information systems (appropriateness and reliability, comparability and understanding), and renovation and maintenance of the hardware and software. The problem in the Kingdom of Saudi Arabia is that the government depends on oil revenues more than on attracting investments, therefore, the importance of this study is constituted by the provision of critical recommendations to policy makers in the Kingdom of Saudi Arabia in order to overcome this issue and improve the investments. In order to achieve the objectives of this study, questionnaires were administered to 194 people representing the study population; a multiple regression (standard regression) was also used to test the study hypotheses. In general, all variables were positively significantly related with the investment decision-making.The findings of this study also showed that the independent variables explained more than 65% of the variance in investment decision-making. The Saudi government and policy makers should issue new regulations to increase the interest in accounting information systems in order to attract the investment. In relation to the practical and theoretical contribution, this study used new variables in the new model, such as renovation and maintenance of the hardware and software. Furthermore, practical contribution will help policy makers and the Saudi government to advance in this area and implement new policies for investors in order to protect the economy and the society stability due to the war in Yemen and Syria.
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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 | 0.002 | 0.008 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".