Unveiling the impact of CEO characteristics and technological factors on management accounting information system use
Bibliographic record
Abstract
The objective of the current study is to explore how certain attributes of chief executive officers (CEOs), such as their innovativeness, knowledge of information systems (IS), and trust in technology and technological factors (Compatibility, relative advantage, and complexity) on the utilization of accounting information systems (AIS) in companies across various industries in Jordan. The research gathered data through a structured questionnaire, which included a 7-point scale. The respondents were CEO/owner of small, medium, and large enterprises (SMEs) in Jordan. A total of 315 valid responses were analyzed using the Partial Least Squares Structural Equation Modeling (PLS-SEM) technique. The findings indicated a significant and positive correlation between complexity, compatibility, CEOs trust in technologies, CEOs information system (IS) knowledge, and the utilization of AIS. However, CEO innovativeness and relative advantage have an insignificant impact on AIS use. The present study is the first to examine CEO characteristics in the AIS context. The practical and theoretical implications derived from the empirical findings of this study offer valuable insights for managers and practitioners. These insights aim to enhance their understanding of the fundamental factors crucial for the successful implementation of AIS in companies, ultimately contributing to improved firm performance.
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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 | 0.020 |
| 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 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".