Identifying the success factors of knowledge management tools in research projects (Case study: A corporate university)
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
The factors affecting the success of knowledge management (KM) tools play an important role in knowledge processes. This research investigates the factors affecting the success of KM tools in the research projects of a corporate university. The research method is descriptive and the statistical population of the study consisted of all professors and knowledge workers of a university. 147 of them were selected through a targeted sampling method. Data collection was conducted through a questionnaire. To determine the validity of the questionnaire, content and formal validity were used and its reliability was calculated by using Cronbach's alpha with the value calculated of 0.83. Data were analyzed by using descriptive statistics, t-test and Friedman test. In this study, the factors of culture, information technology, strategy and goal, organizational infrastructure, employee motivation, leadership and management support, human resources management, education, financial resources, measurement, processes and activities, structure and communications in the knowledge management cycle of research projects of the university studied were identified as the effective factors in the KM cycle.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.011 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it