Towards De-internationalisation of Entrepreneurial SMEs: Exploring Internal and External Factors
Bibliographic record
Abstract
In recent years, the global business environment has witnessed a wave of de-internationalisation among not only multinationals but also small and medium-sized enterprises (SMEs). This disengagement of cross-border activities is deemed to be driven by various firm-specific determinants as well as external factors. Building on the premise of dynamic capabilities view and institutional theory, this paper is set to disentangle the extent to which internal and external factors drive SMEs towards de-internationalisation. To address our research objectives, we take advantage of a hybrid multi-layer decision-making-mathematical modelling approach. Our key findings reveal two distinct frameworks reflecting the general interrelationship amongst internals and externals. Also, the subordinate level explores the unique compositions leading to different de-internationalisation modes. In this vein, our findings highlight two categories of factors namely reducing and terminating factors, which drive SMEs into respectively partial and full de-internationalisation.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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".