Going global: how smaller enterprises benefit from strategic alliances
Bibliographic record
Abstract
Purpose This paper aims to focus on understanding three dimensions of international alliance formation by small to medium‐sized enterprises (SMEs): the role of internal actors, planning/opportunity management, and organizational learning. Design/methodology/approach The three dimensions form a proposed model of international alliance formation which is examined using semi‐structured interviews with 16 biotechnology SMEs from Montreal (Canada) and 12 from Boston (USA). Findings Findings deepen the understanding of the firm's internal development of international alliance strategy. Results generally support different roles of organizational actors in international alliance formation, often a combination of planning and opportunity management, and signal rather weak administrative routines to ensure organizational learning from the alliance experience. Interestingly, alliance formation strategies vary across the two cities (countries). Age of the firm, development phase, human and financial resources, and competencies may explain these differences. Research limitations/implications Limitations include a single respondent in each firm, sample size, and single sector (biotechnology). Future longitudinal research could combine information from and about the implication of all actors and their networks during alliance formation and examine the process by alliance functions (R&D, production, marketing) and governance modes (equity, non‐equity). Practical implications Results suggest weaknesses and potential avenues to be explored by managers. Originality/value To the authors' knowledge, this is a first attempt to model the internal dimensions of alliance strategy formation for SMEs, integrating the role of actors, planning and opportunity, as well as learning. Multiple quotations provide a rich environment for understanding practice.
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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 | 0.003 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.004 |
| Scholarly communication | 0.008 | 0.010 |
| Open science | 0.001 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.020 | 0.002 |
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".