A Knowledge Management IT Tool: An Investigation within a Marketing Introductory Course
Bibliographic record
Abstract
Aim/Purpose: The purpose of this study is to shed light on how students learn within an environment tailored to knowledge creation. Background: We build on Nonaka, Toyama, and Konno’s three key elements: SECI model, Ba, Leadership as well as current knowledge management researchers critiques and improvements. Methodology: Based on an introductory marketing course, we used an in-house web based learning tool (peer-to peer) to capture score performances and perception surveys. The analysis was conducted through an exploratory factor analysis (EFA). Contribution: This study shed light on current knowledge management critiques by providing measures at the micro-level and community level. Findings: Perceptions of adaptability and usefulness change positively over time, while students’ repeated practice prepares them for different styles of questions as their performances increases over time. Recommendations for Practitioners: Organizations can understand how employees create knowledge through exchange of ideas, feedback, and common goals. Supervisor can understand their employees better and employees can gain a sense of control on their work. Recommendation for Researchers: The ability to capture information over time on the human and community level within a system allows further research to shed light on different variables of knowledge creation in the field. Impact on Society : An appreciation of the mechanism of knowledge creation can encourage organization to become more innovative and focus on people rather than material. Future Research: Measures such as the engagement level, the personality level, and compatibility level within a community to create knowledge are to be explored.
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.
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 | 0.007 | 0.025 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| 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".