Investigating firm heterogeneity in country-of-origin cluster location choice decisions
Bibliographic record
Abstract
Purpose This paper aims to analyse which firm-level characteristics drive their location decisions when investing in a foreign country. Focusing on origin clusters, the authors will study the potential influence of the home country context and, in particular, the impact of firm-level factors, both investor- and investment-related, underlying heterogeneity in their location choice decisions. Design/methodology/approach The empirical analysis draws on data gathered from mainland Chinese MNEs that have invested in Germany between 2005 and 2013 (269 firms). The authors chose a single host (Germany) and a single home (China) country for their representativeness and for methodological reasons to control for country effects. The authors used a multinomial logit model to assess the effects of the independent variables on the probability that each of the three location possibilities would be selected. Findings The results suggest that investors preferring co-location in origin clusters have distinct structural and strategic characteristics. From a more structural point of view, Chinese foreign direct investment (FDI) undertaken by smaller firms and those without prior experience in the EU prefer an area where there are other Chinese investors. From a more strategic perspective, these FDI flows are more likely to tap into industry agglomerations when the investors’ objective is strategic asset seeking, and they have less knowledge-intensive investments. Practical implications The findings may be of great practical value to practitioners and policymakers. Knowledge of the advantages and disadvantages of the types of agglomeration networks can help managers to balance the rewards and risks in their decision-making and to select a suitable development path for their FDIs. For policymakers, an understanding of the structure and formation of different groups of firms in one location and the characteristics of investors who may enter the location can help them to improve their regulatory work and to develop policies to attract investments, thereby enhancing local economic development and community stability. Originality/value The research shifts the emphasis of the location choice decision beyond just where to locate toward with whom to collocate. It also contributes to the growing research on emerging market multinationals by providing further insight into understanding of FDI location behavior by firms from emerging economies.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".