A Survey of Human Factors’ Impacts on the Effectiveness of Accounting Information Systems
Bibliographic record
Abstract
One of the significant factors of management success in achieving organization goals is effectiveness of accounting information systems, and the users of the accounting information systems have a great role in the effectiveness of the systems. The purpose of this study is to investigate the impact of human factors including individual and personal characteristics of the users of accounting information systems computer-based on effectiveness of these systems. For this purpose, a sample includes 62 offices, organizations and public sector and private companies that use accounting information system computer-based, has been randomly selected and the required data has been gathered using questionnaires. In order to discover the personal characteristics of the users, NEO questionnaires which are designed based on Five Factor Model of Personality, has been used. In order to study the relation between personality and effectiveness of the system, five hypotheses based on five main features of personality have been discussed. Moreover, in order to investigate the relationship between expertise (educational field, educational level and amount of training courses of computer skills), experience and job satisfaction of the users, and the effectiveness of the accountancy information systems computer-based, some hypotheses have also been written and studied. The information about the effectiveness of the system has been gathered by a self-made questionnaire and the accuracy of the research hypotheses are examined by using Spearman correlation and Chi-square test. The research results indicates that the personal characteristics including openness, Agreeableness, Conscientiousness and also job satisfaction and experience of working with financial software of the Users, is efficient on the effectiveness of the accounting information systems computer-based.
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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 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".