Strategic knowledge management and enterprise social media
Bibliographic record
Abstract
Purpose This paper aims to examine if (and how), enterprise social media (ESM) can be understood as a strategic knowledge management phenomenon to improve organizational performance. Design/methodology/approach This paper uses intellectual capital theory and its functional building blocks to organize different types of the ESM platforms, based on secondary data. It then connects these findings to the underling intellectual capital tenets to introduce a conceptual model that explicates how ESM impacts strategic knowledge management, and vice versa. Findings This paper concludes that ESM provides a unique complement to traditional strategic knowledge management. The authors argue that ESM differs substantially from other contexts in which intellectual capital has been applied, and extend intellectual capital with three appropriate dimensions (human, social and structural capital). Given the potentially disruptive nature of ESM, this framework helps firms understand the nature of the changes that are needed. Originality/value The paper provides the first review of the business needs that are served by the software functions and management processes under the ESM banner. This original contribution takes the intellectual capital and strategic knowledge management discussions from their usual high levels of abstraction and relates them to the real world of ESM, focusing on outcomes. Its unique “Intellectual Capital Framework for the Socially Oriented Enterprise” includes distinct, testable propositions that provide a practical approach to strategically planning, implementing and optimizing ESM.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.008 | 0.007 |
| Open science | 0.001 | 0.004 |
| 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".