The influence of product life cycle on the efficacy of purchasing practices
Bibliographic record
Abstract
Purpose The purpose of this study is twofold. First, to examine the contingent role of the product life cycle on the efficacy of purchasing practices. Second, to use the results of the first investigation to explore the adequacy of the profit‐maximization framework for explaining purchasing decision making. This second investigation is motivated by growing evidence on the role of institutional factors in explaining supply chain management practices. Design/methodology/approach Survey data from a sample of North American manufacturing firms, across four standard industry sectors, are analysed using ANOVA and linear regression, to examine the hypotheses. Findings The results indicate that product life cycle has a contingent effect on the efficacy of some purchasing practices but not on others. Interestingly, the results suggest that the profit‐maximization framework is capable of explaining only some purchasing decisions but not others; firms adopt certain purchasing practices in certain product life cycle stages, even when these practices have no apparent effect on purchasing performance. This raises a need for an alternative framework to profit‐maximization, to better understand purchasing decision making. Originality/value The paper pioneers an empirical examination of how product life cycle moderates the relationship between purchasing practices and purchasing performance. The paper presents novel insights on the inadequacy of the rational profit‐maximization framework to explain purchasing decision making. Furthermore, the paper presents testable propositions on the role of institutional factors that are potentially driving purchasing decision making in managing the product life cycle contingency.
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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 | 0.004 | 0.037 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| 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".