Job Performance in Russia: The Impact of Cultural and Emotional Intelligence
Bibliographic record
Abstract
A great deal of work has been done in recent decades examining the impact of emotional intelligence (EQ) and cultural intelligence (CQ) on extra-role behavior and job performance. However, comparatively little of this research has used both EQ and CQ constructs when doing this. In addition, the available research literature is virtually silent with regards to the relationships of these constructs in Russia. Using the Thomas et al. (2015) model of CQ which separates motivational CQ from the Earley and Ang (2003) core CQ subconstructs, this study examines these relationships using a sample of 189 supervisory and non-supervisory subjects in Russia. The results suggest that motivational CQ is an antecedent of both CQ and EQ, both of which significantly impact extra-role job performance along with the control variables of supervisory work role and the degree of daily interpersonal interactions. EQ and CQ factors were also found to mediate the motivational CQ and job performance relationship. In addition, necessary condition analysis was completed adding to model insights. These results have potentially significant implications for current EQ-CQ model theories, which are discussed along with study limitations and need for future research.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".