The Impact of Foreign Capital on the Level of ERM Implementation in Czech SMEs
Bibliographic record
Abstract
The COVID-19 pandemic has had a devastating impact on many small and medium-sized businesses around the world. Enterprise risk management (ERM) is a conceptual framework that encompasses the systematic and comprehensive identification, analysis, and management of risks in an enterprise. In the post-communist countries of Central Europe, the level of ERM is still relatively low, especially in small and medium-sized enterprises (SMEs). This study fills a gap in the existing knowledge on ERM by shedding light on the influence of foreign capital on the level of ERM implementation in Czech SMEs. The aim of the study is to assess the influence of the share of foreign capital in Czech SMEs on the level of ERM implementation. A validated self-report of 296 non-financial SMEs in the Czech Republic was analyzed using latent class analysis (LCA) and multiple linear regression. The results of the study contribute to the literature by enriching the empirical analysis of ERM in emerging markets. The originality of the results lies in the identification of three distinct groups of firms according to the combination of implemented ERM elements—“no ERM”, “best practice ERM”, and “pretended ERM”—and the finding that the share of foreign capital, age, and firm size influence the level of ERM implementation. In particular, the positive influence of foreign capital in younger companies makes it possible to overcome the barrier of traditionalist thinking of old-school Czech managers influenced by the period of economic transition in post-communist countries. The paper builds on the existing evidence with new empirical conclusions and argues for a greater inflow of foreign direct investment into emerging markets.
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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 | 0.002 | 0.000 |
| 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 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| 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".