Linking Artificial Intelligence Use to Improved Decision-Making, Individual and Organizational Outcomes
Bibliographic record
Abstract
Artificial Intelligence (AI) plays a significant role at the organizational and operational levels. Managers of companies started to employ AI in decision-making to achieve their operational goals. However, not all managers have the same adaptability to this new technology. Therefore, the present study investigates the impact of utilizing AI on the decision-making process. It also aimed to examine the impact of improved decision-making on three dependent variables: Organizational Performance (OP), Individual Productivity (IP), and Organizational Culture (OC). Statistical Package for Social Sciences (SPSS) software was used to analyze the obtained data and test the hypotheses. The sample of this study included 133 participants working in Saudi organizations, selected from different levels of management (i.e., top managers, middle managers, first line managers, and non-managerial employees). The results of the study showed that AI plays a significant role in the process of decision-making. The results also revealed a positive direct relationship between improved decision-making and organizational performance, individual productivity, and organizational culture. Based on the results of this study, some implications and recommendations were provided concerning the relationship under investigation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".