Logics of local actors and global agents: divergent values, divergent world views
Bibliographic record
Abstract
Purpose This paper seeks to develop a theoretical explanation of conflicts and incompatible interpretations of events between agents of multinational corporations (MNCs) and actors present in certain host countries. It aims to situate the argument in comparative economic systems as a part of a broader social system. The socio‐economic system can be modeled using institutional theory, particularly using Scott's three pillars and the concept of formal and informal institutions. Within different socio‐economic systems a dominant logic is developed, and this becomes internalized among actors and agents as behavioral scripts. Design/methodology/approach The paper uses a multi‐level and multi‐disciplinary conceptual analysis, developing a model of dominant logic and behavioral scripts with MNC agents and traditional emerging economy actors. Findings MNC agents and traditional emerging economy actors have difficulty comprehending the logic of the other, creating a fertile context for conflict. Research limitations/implications An ideal type template is developed that can be used for empirical investigations focusing on situations where disagreement and conflict occur when MNCs operate in traditional emerging economies. Practical implications By integrating the authors' conceptualization into training for expatriate managers, the potential for conflict can be reduced. Originality/value This multi‐level and multi‐disciplinary model allows grounded development of understanding of conflicts or potential conflicts in the MNC agent‐traditional emerging economy actor context.
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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 | 0.010 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.004 | 0.048 |
| Scholarly communication | 0.015 | 0.013 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".