HRIS Mediating Role the Relationship between TOE and Decision Making
Bibliographic record
Abstract
Decision making plays an important role in organizations. It is the most important activity that managers do. Studies have extensively tackled the importance of decision-making process Yet, determining the main factors that affect decision making and the role of Human Resources Information Systems (HRIS) as a mediator has received negligible attention, especially in the Egyptian context. Accordingly, the main subject of this paper is to examine the effect of the implementation of the TOE model based on the three contexts; technology (competitive advantage, complexity, compatibility, security and trust), organization (senior management, readiness, technology, maturity and performance), environment (competition, telecommunications infrastructure, internet service provider, business partner support and business partner pressure) on the process of informed decision-making mediated by HRIS in the higher education institutions sector namely Arab Academy for Science and Technology and Maritime Transport (AASTMT). The study reviews literature, identifies key constructs, develops hypotheses and proposes a research framework. A structured questionnaire was also adopted and adapted to understand the employees’ perspectives. Questionnaires were distributed to over 500 employees, 400 of which were returned and considered valid. Descriptive Statistical analysis was conducted using SPSS. The research framework has the potential to contribute to the body of knowledge, and therefore improve the decision-making process to attain better quality of job life.
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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 | 0.003 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.014 | 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".