Bridging the gap between work‐ and nonwork‐related knowledge contributions on enterprise social media: The role of the employee–employer relationship
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Abstract Knowledge is an invaluable resource and a key to organisational success. To leverage this resource adequately, organisations must encourage their employees to share what they know with their peers. Enterprise social media (ESM) has emerged as an ideal venue for achieving this goal, and numerous studies have examined the drivers of work‐related knowledge contributions on these platforms. The present study contributes to this body of research by examining a prevalent yet underexplored form of knowledge sharing that often occurs on ESM: nonwork‐related knowledge contributions. We argue that contrary to a commonly held belief, this presumably hedonic employee behaviour can benefit organisations through its spillover effect on the work domain. In other words, we argue that nonwork‐related knowledge contributions on ESM can foster work‐related ones. Building on social exchange theory and on the associative–propositional evaluation model in social psychology, we also show that the employee–employer (EE) relationship—conceptualised in terms of perceived organisational support and perceived employee psychological safety—moderates the relationship between the two forms of knowledge contributions. The analysis of field data collected from 269 employees of a French e‐commerce company confirmed that nonwork‐related knowledge contributions are positively associated with work‐related ones and that this positive association is moderated by the EE relationship. We discuss the theoretical contributions of our results and explain key managerial implications for organisations hoping to reap the benefits of ESM in a sustainable way.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it