Enterprise social media, meta-knowledge and knowledge management capability: an affordance perspective
Bibliographic record
Abstract
Purpose Enterprise social media (ESM) application in knowledge management is critical for individuals and organizations. However, there is limited empirical evidence regarding how ESM enhances individual knowledge management capability or whether prior experience affects the value derived from ESM. This study aims to understand the intricate relationships among ESM affordance, employees’ meta-knowledge, prior usage experience and knowledge management capability. Design/methodology/approach This study constructs an integrated framework based on the technology affordance perspective and meta-knowledge theory. It was validated through a two-stage survey, yielding 322 usable responses. Findings The affordance of ESM has a positive influence on individual meta-knowledge, which, in turn, enhances knowledge management capabilities. Additionally, visibility has a direct impact on meta-knowledge. Furthermore, prior experience significantly moderates the relationship between ESM affordance and meta-knowledge. Research limitations/implications This study contributes to the literature by offering a strategy for conducting theory-driven research on how ESM enhances individuals’ knowledge management capability from an affordance perspective. This study develops a framework by integrating ESM affordance and meta-knowledge, and offers a nuanced perspective that considers who knows what and who knows whom as individual meta-knowledge concepts. Practical implications The impact of ESM affordance on knowledge management capability is expected to enhance individual knowledge management skills. Originality/value This study investigates the role of technology affordance, meta-knowledge and knowledge management capability in the context of ESM implementation.
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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 | 0.003 | 0.013 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.008 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".