Organizational obstacles to green behaviors of university employees from different countries – what about the cultural distance?
Bibliographic record
Abstract
Purpose This study aims at identifying and explaining organizational factors that affect organizational citizenship behaviors for the environment (OCBEs) among university employees working in culturally distant countries. This research aims to address the gap in understanding how cultural dimensions influence the organizational antecedents of OCBEs, particularly in cross-cultural contexts. Design/methodology/approach Based on interviews with administrative personnel from universities located in Canada and Colombia (culturally distant countries), the research team analyzed how various types of OCBEs were affected by nine organizational obstacles to these behaviors. Thematic comparison and contrasting were used to identify and categorize recurring patterns in the analyzed data. Findings Several differences are identified in terms of how organizational obstacles to OCBEs are perceived among university employees in two culturally distant countries. For instance, authentic communication seemed to be more determinant for eco-initiatives and eco-helping in Colombia, and establishing corporate objectives were aligned with willingness to engage in eco-civic engagement in Canada and in eco-helping in Colombia. These findings suggest that cultural context significantly shapes the perception and impact of organizational obstacles on OCBEs. Originality/value To the best of the authors’ knowledge, this is the first research that attempts to merge the literature on cultural dimensions with the literature on organizational antecedents to OCBEs. This study provides empirical evidence of cultural context’s impact on organizational obstacles to OCBEs, suggesting universities and policymakers tailor environmental initiatives accordingly.
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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 | 0.002 | 0.006 |
| 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 | 0.002 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".