Determinants of EMNEs’ Entry Mode Decision with Environmental Volatility Issues: A Review and Research Agenda
Bibliographic record
Abstract
Emerging market multinational enterprises (EMNEs) play a vital role in global economic development and usually adopt aggressive internationalization strategies. However, the volatile global environment has caused EMNEs to face various risks in their overseas expansion. To maximize the competitive advantages and achieve successful expansion, EMNEs should choose the most suitable foreign entry mode. Therefore, EMNEs need to understand what environmental factors affect their decision-making and how they influence the choice of entry modes, especially in a volatile environment. This review examines 44 selected journal articles from 1996 to June 2021 on the environmental volatility determinants of EMNEs’ entry mode choice. The entry mode choice we examined is mainly wholly-owned subsidiary versus international joint venture. We categorized the environmental volatility determinants investigated in the literature we reviewed into country-level factors (such as cross-national distance) and industry-level factors (such as industry condition). The main contributions are: (1) the review reveals three research gaps in extant studies, which are lack of research on external environmental factors, lack of research on multinationals from less concerning emerging economies, and lack of research on small-to-medium (SMEs) enterprises. (2) Practically, the study highlights the importance of understanding external environmental factors for EMNEs to make the most suitable entry mode decisions.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".