A Multilevel Perspective of Tensions Between Knowledge Management and Social Media
Bibliographic record
Abstract
In this article, we discuss the tensions that are perceived in organizations as the use of social media platforms such as Facebook and Twitter challenge past approaches to knowledge management initiatives in organizations. We address these perceived tensions using a three-level conceptual framework: the macro- (organizational) level, the meso- (group) level, and the micro- (individual) level. In our discussion, we posit that perceived tensions arise when managers seek to maintain their traditional roles at the macro- (organization) level, yet social media affordances enable these roles to be performed at the micro- (individual level) and mesolevels. Shifts in the extent of the meso-level connections beyond the immediate organizational boundaries enable a wider community of practice than before. As a consequence, traditional management roles may give way to more flexible roles, with greater individual responsibilities for control and more sense-making and knowledge access taking place at the mesolevel. Our contribution is three-fold. In our article, we examine four key organizational factors (roles, ownership, control, and value) using a three-level conceptual model; associate the perceived tensions that arise in organizations with implicit shifts in these variables that accompany the use of social media; and suggest that shifts in emphasis in roles and control at each level can be instrumental in resolving perceived tensions as knowledge management efforts encompass social media.
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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 | 0.005 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.006 | 0.021 |
| Scholarly communication | 0.018 | 0.020 |
| Open science | 0.002 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.005 | 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".