The impact of knowledge management infrastructure on the innovation process and products: The mediating role of knowledge management technologies and mechanisms
Bibliographic record
Abstract
The study aims at measuring the availability of knowledge management (KM) infrastructure and its impact on the innovation process and products of Munir Sukhtian Trading Group Company (MSTGC), through the intermediary role of KM mechanisms and technologies.The study tool, which took the form of a questionnaire, was designed to collect the required data from the company under research.The validity and stability of the research tool were both tested.The study community is made up of the MSTGC, and the study sample consisted of the senior and middle management.A group of 101 managers were randomly selected from the sampling unit which consisted of (140) managers, heads and deputy heads of departments, sales supervisors and team leaders at the Head Office of the company in Amman.The study used the descriptive-analytical research method, and found the following most notable findings: high level of information technology (IT) infrastructure and intermediate levels of the rest of the components of KM infrastructure (physical environment, common knowledge, organizational culture, and organizational structure).The innovation process is of a medium level and the same is for KM mechanisms and technologies.As for MSTGC's commercial products, the results show a high level represented by two things: first, value-added products and knowledge-based products.Furthermore, a statistically significant effect was found (α≤0.05) for the KM infrastructure with its components (IT infrastructure, physical environment, common knowledge and organizational structure) on the MSTGC's products.On the other hand, the effect was outwardly in relation to the component of organizational culture.The findings also show that the KM infrastructure had a statistically significant impact on the innovation process and the products through KM mechanisms and technologies.The study presented a set of recommendations, most notably: the need for enhancing and elevating the intermediate levels of KM (physical environment, common knowledge, organizational structure, organizational culture), while maintaining the high levels of IT infrastructure.Also, among the recommendations is the need for maintaining the high levels of value-added and knowledge-based products of MSTGC's in order to remain in competition with the market and enhance the reputation of the company's products among customers.Also recommended is paying more attention to KM infrastructure because of its impact on the innovation process and the company's products.
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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 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".