SMEs’ strategic orientation through Miles and Snow typology: a synthesis of literature and future directions
Bibliographic record
Abstract
Purpose The purpose of this research is to present a systematic analysis of consequents and antecedents of strategy and performance. To acheive this, this systematic review article analyzes and synthesizes mainstream research on small and medium-sized enterprises (SMEs) where Miles and Snow typology was used for strategic orientation of the SMEs. The specific focus of the research is to develop a conceptual framework showing consequents and antecedents of the strategic orientation. Design/methodology/approach This study uses systematic literature review (SLR) method to identify, summarize and synthesize literature on Miles and Snow typology. Preferred reporting method for systematic reviews and meta-analyses to ensure adherence to systematic approach. The key words search consists of the words: “Miles and Snow”, “Miles and Snow” and “miles-snow” from Web of Science and Scopus databases for sample articles. Findings The trend of research on SMEs using Miles and Snow typology is on the rise with a shift from developed countries to the developing ones. Support for strategy-performance relationship hypotheses is overwhelming but the traditional view is in decline while new antecedent and consequent variables are being added. Mediator and moderating variables are also identified. Originality/value The SLR where a synthesis approach was applied for finding antecedents and consequent variables of strategy-performance relationship along with a presentation of conceptual framework makes this research unique. Additionally, the article presents the trends of research over the time based on timeframe, regions, methodological approaches and hypotheses support.
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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 | 0.020 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.019 | 0.014 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.007 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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 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".