Enabling the business strategy of SMEs through e‐business capabilities
Bibliographic record
Abstract
Purpose The present study aims at a deeper understanding of the performance outcomes of the alignment between the e‐business capabilities of manufacturing small‐ and medium‐sized enterprises (SMEs) and their business strategy in terms of Miles and Snow's recognised strategic typology that includes prospectors, analyzers, and defenders. Design/methodology/approach From a contingency theory perspective, a survey of 107 Canadian manufacturers was used to collect data that were analyzed through correlation analysis. Findings Results indicate that the ideal e‐business profiles vary in the relation to the firms' strategic orientation, whether it is of the defender, analyzer or prospector type. E‐business alignment has positive performance outcomes for manufacturing SMEs in terms of growth, productivity and financial performance. Research limitations/implications The nature of the sample impose care in generalizing the results of the study. These results also allow us to emphasise the nature rather than the investment value of the SMEs' information technology investment, given that certain forms of e‐business would be more appropriate for certain firms, depending upon their strategic orientation. Practical implications For SME owner‐managers that require greater manufacturing flexibility, increased systems integration, products and services of better quality, and higher levels of product and process innovation, the results of this study allow us to prone an examination of their firm's level of e‐business assimilation, this being done in conjunction with their strategic intent. Originality/value This is one of the first studies to have used a rigorous conceptualisation and measure of alignment to confirm the theoretical validity and empirical usefulness of this notion and of the strategic contingency approach for research on e‐business, and to compare this approach with the universalistic approach founded upon “best practices”.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".