Workplace bullying: consequences, causes and controls (part two)
Bibliographic record
Abstract
Purpose This paper's aim is to examine workplace bullying – what it is and its causes, consequences – and to offer managers control systems on how to counter, reduce or eliminate it. The scale of bullying in the workplace is quite alarming – it is estimated that 1.7 million Americans and 11 percent of British workers have experienced bullying at work in the last six months. Until now the topic has many problems identified but limited solutions. This article attempts to close that gap. Design/methodology/approach The two‐part article begins with a review of definitions/descriptions of workplace bullying. Next, an exploratory look at the consequences of workplace bullying is presented and demonstrates its impact on victims and organizations. Moreover, a summary of potential sources will be exposed ranging from personality traits to organizational constructs. Finally, the article will approach three organizational strategies that have been proven to act as control systems towards workplace bullying. Findings It is found that transformational and ethical leadership are both very effective tools for managers to counter workplace bullying and that the instauration of an ethical climate in the workplace appears to be the most effective in avoiding workplace bullying from forming. Research limitations/implications The paper does not compare control systems against one another and does not explore the effectiveness of bullying predictors. Originality/value The paper offers a comprehensive approach in understanding workplace bullying, its causes and its consequences. As well, it offers tools to managers on control systems designed to counter it. The topic is quite new in the literature and very relevant in terms of incidents that are repeated in the popular press but limited in terms of research articles.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 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".