Capability, environment and internationalization fit, and financial and marketing performance of MNEs’ foreign subsidiaries
Bibliographic record
Abstract
Purpose Extant work in international business (IB) involves a partial contingency-theoretic perspective: a holistic view of the impact of bundles of contingencies on an outcome variable is missing. The purpose of this paper is to adopt a contingency approach to study multinational enterprise (MNE) subsidiary performance in the appropriate context of European transition economies at the beginning of the current millennium. Design/methodology/approach Methodologically, the authors introduce abduction as a line of inquiry into IB and management to develop new theoretical insights, and apply the novel empirical general interaction method to estimate bundle effects. In so doing, the authors contribute to the further development of a theoretical and empirical toolkit to revitalize holistic, or configurational, quantitative research in IB and management. Findings The authors find that capability fit is a necessary condition for high MNE subsidiary marketing performance, whilst environment fit is particularly critical for high MNE subsidiary financial performance. Research limitations/implications A key limitation is that this is a cross-section study. Practical implications This study offers insights as to subsidiary fit into Eastern Europe, indicating fitting entry and establishment modes. Originality/value This paper offers a novel holistic approach to IB, both in terms of theoretical and empirical methodology.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".