Knowledge self-enhancement and knowledge self-presentation in the workplace: conceptual foundations, measures, and impacts
Bibliographic record
Abstract
Purpose This study aims to introduce two novel constructs – knowledge self-enhancement and knowledge self-presentation – into the knowledge management field to address a theoretical gap concerning the cognitive mechanisms driving knowledge behavior. Design/methodology/approach This article comprises two studies. Study 1 developed and validated a survey instrument, and Study 2 tested the new constructs within a nomological network of productive and counterproductive knowledge behavior. Data were collected from two samples of 202 and 171 experienced employees via the CloudResearch Connect platform. Findings On average, employees overstate their professional knowledge by 20% compared to that of their coworkers. Knowledge self-enhancement is a neutral or positive construct because it leads to knowledge hoarding and knowledge sharing, whereas knowledge self-presentation is a negative construct because it triggers knowledge hiding and knowledge sabotage. The effect of knowledge self-enhancement on knowledge self-presentation is amplified by the narcissistic personality trait. Practical implications Instead of preselecting new workers who do not possess knowledge self-enhancement or attempting to suppress the knowledge self-enhancement of existing employees, managers should prescreen job applicants for possession of the narcissistic personality trait. Managers should identify and remove distractor cues that activate the narcissistic personality trait in their workers. They should also educate their employees about the concept of knowledge self-presentation and its pernicious effects. Originality/value Knowledge self-enhancement and knowledge self-presentation are distinct constructs that differ from the other well-established measures tapping into similar cognitive and behavioral domains, namely, socially desirable responding, lying, and overclaiming. This study empirically shows that people’s natural tendency to self-enhance their characteristics is also present in the workplace.
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 | 0.006 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.005 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".