The Role of Basic Human Values in Knowledge Sharing: How Values Shape the Postadoptive Use of Electronic Knowledge Repositories
Bibliographic record
Abstract
A growing body of literature examines how to elicit knowledge contributions to electronic knowledge repositories (EKRs) with the goal of helping organizations increase implementation benefits. While this literature has explained in detail the initial EKR adoption by knowledge contributors, it has not yet examined the drivers of postadoptive EKR usage for contributing knowledge. Postadoptive EKR usage, such as innovative feature use, can potentially result in richer contributions to EKRs. To aid understanding of how to unlock the benefits of EKRs for organizations, this study examines the impact of basic human values on one type of postadoptive behavior that goes well beyond basic usage: trying to innovate with EKR features. We develop a research model that integrates human values and trying to innovate with EKRs, suggesting that human values indicate modes of independent thought and action and can lead to attempts to innovate in EKR use by increasing the frequency of EKR usage. Data collected from 233 knowledge workers support the model. Our findings shed light on how to encourage innovative EKR usage and underscore the importance of human values for the success of knowledge management initiatives.
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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 | 0.015 | 0.057 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.009 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".