Technology oriented, service intensive, transnational entrepreneurs' international target market strategies
Bibliographic record
Abstract
This instrumental case study's objective is to understand the target market strategies of technology oriented, service intensive, transnational entrepreneurs (TEs). Existing studies featuring the merits of ‘concentration versus spreading’ target market strategies provide mixed findings, typically involving firms with product oriented as opposed to service intensive business models. Furthermore, prior research focuses on export-oriented owner-managers as opposed to TEs who have capabilities associated with being socially embedded across different countries. Interview data features the practices of 15 TEs, whose businesses involved information and knowledge-based solutions. Specifically, first generation UK-based South Asian immigrant entrepreneurs who are socially embedded in both their country of origin and country of settlement. Unique insights contribute to a microfoundational cultural perspective of business-to-business (B2B) practices, regarding a new generation of TEs, being relatively highly educated, experienced, and technologically oriented. New evidence builds on dated prior studies often featuring lower skilled and less experienced immigrant entrepreneurs in low-tech, service-oriented sectors. The choice of TEs' target market strategy is not necessarily binary in nature, whereby the importance of decision-makers possessing the ability to pivot strategies is evidenced. To varying degrees, certain TEs employ an ambidextrous approach, concentrating on key markets and entering/exiting others perceived as peripheral.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".