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.
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.
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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".