Using the Resource-Based View in Multinational Enterprise Research
Bibliographic record
Abstract
The resource-based view (RBV) has evolved into a preeminent theory of strategic management. It is widely used by international business (IB) scholars since there is considerable synergy in core research questions pursued by IB and strategy researchers. However, in research on multinational enterprise (MNE) behavior, the use of RBV remains limited relative to other influential perspectives, such as the eclectic paradigm, the Uppsala model, and institutional theory. This is not surprising since the RBV was developed to explain performance differentials between country-centric firms with dominant product businesses rather than large MNEs with an expansive product-geographic scope. We describe how these limitations arise from the wider range of outcomes and explanatory variables, multiple levels of analysis, and the spatial, economic, and institutional barriers that are relevant to MNEs. We discuss the application of RBV to MNE research by the first author and other IB scholars. We then provide directions on how future research could use RBV more fruitfully to examine MNE performance and sources of competitive advantage in several areas. These include diversified corporations, subsidiary agglomeration, emerging market MNE internationalization, subsidiary autonomy, international joint ventures and alliances, and corporate social responsibility. Drawing upon teaching case examples from the first author’s work, we also point to the effectiveness of RBV in teaching with business cases, given its focus on firm performance (strategy).
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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 | 0.022 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.007 | 0.008 |
| Science and technology studies | 0.003 | 0.020 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".