Should HR managers allow employees to use social media at work? Behavioral and motivational outcomes of employee blogging
Bibliographic record
Abstract
There is a dilemma for HR executives concerning social media policies: Should HR managers allow employees to use social media while at work? The question has no easy answer because there are conflicting views on the matter. However, the conflicting views can be resolved if we focus on the individuals with whom an employee interacts through social media. Building on data on the blogging activity of 269 employees working for a Canadian health-care provider, the paper reveals a new problem: The extent to which employees engage in personal blogging with outsiders – individuals who do not work for the organization – is negatively related to intrinsic work motivation and to proactive behavior. After having introduced the problem, the paper shows a solution. If employees engage in blogging with coworkers, the negative effects turn positive: Blogging with coworkers positively affects intrinsic work motivation and proactive behavior. Finally, the paper offers a recommendation for HR managers to leverage the solution. Through social job design and increasing formal interaction requirements, HR executives can reinforce the association between social media use and blogging with coworkers. Overall, the paper helps HR executives to clarify the outcomes of social media, find a problem, suggest a solution, and recommend how to achieve it.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".