Examining the mediating role of organizational trust in the relationship between organizational learning and innovation performance: A study of information systems and computer science service firms
Bibliographic record
Abstract
Innovation is a crucial driver of competitive advantage and long-term success, particularly in the dynamic Information Systems and Computer Science Service industry. This study investigates the relationships between organizational learning constructs (organizational memory, knowledge acquisition, knowledge interpretation, and knowledge distribution), organizational trust, and innovation performance within Information Systems and Computer Science Service Firms in Jordan. Recognizing the potential mediating role of organizational trust, the study aims to provide insights into the mechanisms that enable effective translation of organizational learning into innovation outcomes. The study employs a quantitative research design using a cross-sectional survey approach. In this study, the data was gathered from 468 participants from Information Systems and Computer Science Service companies in Jordan, consisting of both senior managers and department heads, and the people involved in organizational learning and innovation processes. The analysis shows that organizational memory, knowledge collection and knowledge interpretation have direct impacts on innovations’ performance, but knowledge distribution does not contribute to this performance. Primarily, organizational trust works as the mediating factor that allows the innovation constructs of organizational learning to improve throughput sizes. The evidence also shows that competent learning is a prerequisite for organizational trust.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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".