How does home government influence the internationalization of emerging market firms? The mediating role of strategic intents to internationalize
Bibliographic record
Abstract
Purpose The purpose of this paper is to focus on the differential impact of government promotional measures and government ownership on two internationalization variables: location and speed of internationalization of emerging market multinationals (EMNEs). Central to the authors’ study is the mediating role of strategic intents to internationalize. In particular, we study how government impacts the resource-seeking, market-seeking and technology-seeking motives to internationalize. Design/methodology/approach The empirical setting for the paper is Chinese companies that have internationalized via an equity based entry mode. The authors employ 672 firm responses collected by the Asia Pacific Foundation of Canada and the China Council for the Promotion of International Trade. Findings The empirical results demonstrate that different home government measures have differential impact on internationalization outcomes. Government promotional measures (such as direct incentives and bilateral agreements to support internationalization) have only an indirect effect on international location and speed through the effect they have on the strategic motives to internationalize; while government ownership in the company has a direct impact on international location. Research limitations/implications The study highlights that home governments are shaping EMNEs strategic intent. Home government can influence EMNEs internationalization choices by providing resource flows through financial resources and state ownership or through asset-accumulation mechanisms via promotional measures. Practical implications Policy makers in emerging markets need to develop policies focused on the specific motivations that firms have when internationalizing. EMNEs are suggested to take advantage of government policies more intentionally. Originality/value The theoretical contribution centers on identifying important mediating mechanisms pointing to the interplay between government policies and international location and speed of firms. The authors contribute to the growing stream of research on internationalization of emerging market firms by building a sound theoretical model and examining empirically the role of home government in the internationalization of EMNEs.
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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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 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".