An empirical study of the relationships among strategy, flexibility, and performance in the supply chain context
Bibliographic record
Abstract
Purpose The purpose of this research is to examine the relationships among strategy, flexibility, and performance in the supply chain context. Design/methodology/approach The research is based on a quantitative approach using a questionnaire survey and personal interviews from a total of 175 small and medium‐sized Canadian manufacturing companies. The identified constructs have been utilized to test a theoretical model using the path analysis technique. Findings First, the findings provide evidence of direct effects of strategy on flexibility and flexibility on performance. Second, innovative strategy firms must invest time and resources in developing new product and delivery flexibility; while customer‐oriented strategy firms are required to invest heavily in developing sourcing, product, and delivery flexibility and follower strategy firms need no investment in any specific type of flexibility. Third, results demonstrated that Canadian manufacturers must reconsider how they use information technology to enhance information systems flexibility and improve overall performance. Research limitations/implications The measures of flexibility and strategy dimensions used to rate the supply chain organizations are a possible limitation of the research study. Practical implications Managers need to think seriously about which type of flexibility they implement and that they should not increase all dimensions of flexibility in their power; some dimensions of flexibility may not significantly contribute to the overall performance. Considering that small and medium‐sized enterprises have limited resources, it is important for managers to carefully assess their strategic needs before getting involved in any flexibility program; otherwise the result can be competitively negative. Originality/value No empirical study was found in the supply chain literature that specifically investigates the relationships among strategy, flexibility and performance in the supply chain context; the paper fills an important gap in the supply chain literature.
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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 | 0.002 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".