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Record W3122397273 · doi:10.1177/0149206321995575

Using the Resource-Based View in Multinational Enterprise Research

2021· article· en· W3122397273 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

affAt least one author lists a Canadian institution in the pinned OpenAlex snapshot.

Bibliographic record

VenueJournal of Management · 2021
Typearticle
Languageen
FieldBusiness, Management and Accounting
TopicInternational Business and FDI
Canadian institutionsWestern University
Fundersnot available
KeywordsMultinational corporationInternationalizationIndustrial organizationResource-based viewInternational businessBusinessCompetitive advantageResource (disambiguation)Scope (computer science)Emerging marketsEconomies of scopeProduct (mathematics)Core competencyAutonomyMarketingEconomic geographyEconomicsManagementEconomies of scaleInternational tradePolitical scienceComputer science

Abstract

fetched live from OpenAlex

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).

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.800
Threshold uncertainty score0.354

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.086
GPT teacher head0.343
Teacher spread0.257 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it