Understanding Managerial Perceptions of Artificial Intelligence Adoption in Decision-Making
Bibliographic record
Abstract
This study investigates managerial perceptions of artificial intelligence (AI) adoption in decision-making, focusing on understanding how managers interpret, evaluate, and integrate AI systems into their organizational processes. As organizations increasingly rely on AI-driven tools for data analysis, forecasting, and decision support, managers play a critical role in determining the effectiveness and ethical deployment of these technologies. The research employed a qualitative approach, utilizing in-depth semi-structured interviews with managers from diverse industries to capture their experiences, insights, and concerns regarding AI adoption. Thematic analysis was conducted to identify patterns and themes that illustrate how managerial perceptions shape both the adoption process and the outcomes of AI-assisted decision-making. The findings reveal that managers perceive AI as a valuable tool for enhancing efficiency, analytical accuracy, and strategic focus, allowing them to shift attention from routine operational tasks to higher-order decision-making activities. At the same time, managers reported challenges associated with AI complexity, resistance to change, data quality, trust, transparency, and accountability, highlighting the socio-technical nature of AI adoption. Ethical considerations, including fairness, bias, and data privacy, were emphasized as critical factors influencing managerial confidence and willingness to rely on AI outputs. Organizational support, leadership endorsement, and continuous skill development were identified as essential enablers for successful integration. The study underscores the importance of balancing human judgment with machine-generated insights, reflecting the concept of collaborative intelligence, where AI augments rather than replaces managerial decision-making. This research provides a nuanced understanding of the factors shaping managerial engagement with AI, offering practical and strategic insights for organizations seeking to implement AI responsibly and effectively in decision-making processes.
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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 | 0.024 | 0.047 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.002 | 0.006 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 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".