Toxins in the workplace: affect on organizations and employees
Bibliographic record
Abstract
Purpose The purpose of this article is to define toxins such as toxic leader, toxic manager, toxic culture, and toxic organization and explore how they affect the organization's performance and its employees. Design/methodology/approach This article is basically twofold. It is a literature review utilizing a selective bibliography providing advice on information sources and it is comprehensive in that the objective is to cover the mainstream and unique contributors of toxicity. It is also a general review providing an overview of this contemporary issue facing organizations with descriptions of what the problem is and how to address it. Findings Organizations as well as their employees suffer from the affects of toxins that are present within the organization. They also suffer from psychological effects, such as; impaired judgment, irritability, anxiety, anger, an inability to concentrate and memory loss. On the other hand, it has also been found that companies in North America alone lose an excess of $200 billion each year due to employee deviance. Employee deviance has also been found to be the cause of approximately 30 percent of all business failures. Practical implications There are possible solutions to reduce, and sometimes even eliminate, toxicity in an organization. Possible solutions, such as recognition, and the use of toxin handlers to eliminate, reduce, or avoid the infiltration and spreading of these toxins is very important to organizations that suffer from or would like to prevent toxicity in the workplace. Originality/value The article is unique and highly practical for all individuals who are in management and for those who are called on to assume leadership roles mandated to deal with deviance, organizational citizen behavior and toxicity in the organization.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".