The Challenge of the Multinational Corporation to Organization Theory: Contextualizing Theory
Bibliographic record
Abstract
Organization scholars have increasingly looked to the multinational corporation (MNC) as a convenient research site for testing and developing general hypotheses applicable to any organization. In this essay, we argue that theorizing about organizational processes in the MNC needs to treat the MNC itself as a research object: that is, to recognize that the complex multidimensionality of the MNC will influence the phenomena under investigation and needs to be incorporated into research design and conceptual framing. To do so requires what we term contextualized explanations: styles of theorizing that view context as constitutive of organizational phenomena. We reanalyse an existing study of identity work in the Carlsberg Group to demonstrate the theoretical insights to be gained from a contextualized approach. Our case analysis illustrates how integrating the MNC into the explanation changes our theoretical understanding of the phenomena being investigated.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.006 | 0.052 |
| Scholarly communication | 0.014 | 0.019 |
| Open science | 0.002 | 0.007 |
| Research integrity | 0.003 | 0.006 |
| 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".