The role of individual cultural traits and proactivity in an organizational setting
Bibliographic record
Abstract
Purpose – The purpose of this research is to assess the impact of espoused individual cultural traits on proactive behaviors within an organizational environment. While there have been many reports about the positive outcomes of proactivity, there is much less known about the antecedents, particularly those related to culture. Design/methodology/approach – Sales employees (n=147) in a multi-national organization from Australia, Canada, New Zealand, the UK, and the USA were surveyed to assess the impact of cultural trait influences on proactive behavior at the individual level. Using linear regression and partial least squares structural equation modeling, three independent variables were found to be significant antecedents to proactive behavior. Findings – Long-term orientation positively influenced proactive behaviors as did uncertainty avoidance. Uncertainty avoidance was hypothesized to have a negative impact on proactive behaviors, but the results of this study implied that individuals found it safer to adjust to a fluid environment rather than to remain inflexible. No relationship was found between power distance and proactivity. Masculinity was found to be positively related to proactive behaviors but collectivism was not. Research limitations/implications – The results of this study should be limited to its own population and not generalized to larger, more culturally diverse populations which were not represented in the sample. Practical implications – This study provides better understanding of managerial proactive behavior related to cultural traits, particularly in the domain of field sales. Originality/value – This study is unique in that it explores individual proactivity in an organizational selling environment related to cultural traits at the individual level.
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 | 0.006 |
| 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 | 0.002 | 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".