Power-based behaviors between supply chain partners of diverse national and organizational cultures: the crucial role of boundary spanners’ cultural intelligence
Bibliographic record
Abstract
Purpose This paper aims to explain the effects of national and organizational cultures of boundary spanners on their choices of using three archetype power-based behaviors – dominance, egalitarian and submissive – with supply chain partners. Improved outcomes for global supply chain (GSC) partners are anticipated due to the ways that cultural intelligence affects these culturally guided decisions. Design/methodology/approach Drawing on multiple streams of literature and focusing on boundary spanners in GSCs, the authors build a conceptual framework that highlights cultural antecedents of predispositions toward power-based behaviors and explains the moderating role of cultural intelligence of boundary spanners on behaviors performed. Findings The authors propose that boundary spanners’ national and organizational cultural values influence predispositions toward applying and accepting power-based behaviors. They also discuss how cultural intelligence moderates the relationship between culturally determined predispositions and power-based behaviors applied by partners. The cultural intelligence of boundary spanners is argued to have a pivotal role in making power-based decisions, resulting in healthier cross-cultural buyer–supplier relationships. Originality/value This paper is the first paper to advance an understanding of the cultural antecedents of boundary spanners’ power-based behaviors that are exercised and interpreted by partners in GSCs. Furthermore, the potential role of cultural intelligence in inter-organizational power dynamics and power-based partner behaviors in supply chains has not previously been discussed.
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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 | 0.004 | 0.012 |
| 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 | 0.004 |
| Scholarly communication | 0.005 | 0.002 |
| Open science | 0.000 | 0.004 |
| 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".