Emotional intelligence and boundary‐spanning behavior among door‐to‐door salespeople
Bibliographic record
Abstract
Abstract This study examines the impact of emotional intelligence (EI) on boundary‐spanning behavior, relationship quality, and performance among door‐to‐door salespeople. Data is collected from salespeople and customers of South Korean door‐to‐door cosmetics businesses and analyzed using structural equation modeling. The findings show that EI positively affects boundary‐spanning behavior. Similarly, boundary‐spanning behavior improves relationship quality, which improves both sales performance and customer satisfaction. This suggests that EI plays an important role in salespeople's boundary‐spanning behavior, thereby enhancing relationship quality, sales performance, and customer satisfaction. Unlike previous studies, this study makes use of actual sales volumes, salespeople's self‐reporting responses, and salespeople's customer‐reporting, which adds to the findings' uniqueness. The present study highlights that EI improves boundary‐spanning behavior, which is vital in developing relationship quality, improving sales performance, and increasing customer satisfaction. The study uses triadic data from door‐to‐door salespeople, customers, and sales organizations extensively, which is unusual in this field.
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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 | 0.001 | 0.003 |
| 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 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".