Our own translation box: exploring proximity antecedents and performance implications of customer co-design in manufacturing
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Bibliographic record
Abstract
Abstract Customer involvement with design activity is one of the principal components of mass customisation. Whereas many studies proposed methods to enable customer co-design, more research needs to determine co-design predictors and its associations with operations improvements. This study tests relationships between proximity, co-design, and performance, and whether co-design mediates proximity-performance relationships. Following on recent technology and collaborative trends, the study uses a three-dimensional operationalisation of customer proximity that includes physical, virtual, and affinity proximity measures. Regression analyses of data from 698 manufacturers from metal-mechanic industries suggest that virtual and affinity proximity related positively with customer co-design, that co-design explained quality and delivery improvements, and that co-design mediated the relationship between virtual proximity and quality improvements. Keywords: collaborationproduct designsupply chain managementmass customisation Acknowledgements This research is supported by the Natural Sciences and Engineering Research Council of Canada (Discovery Grant #283134-04) and by the Warren & Marline Dyer Faculty Fellowship in Operations Management at the Haskayne School of Business. The author thanks Flávio Fogliatto and Laurie Milton for valuable comments on earlier manuscript drafts, and an anonymous reviewer whose comments helped to improve the discussion section. Notes Notes 1. See, for example, Hemetsberger and Godula (Citation2007) who emphasised the need for sales input in design, and Swink and Song (Citation2007) who found a strong relationship between 'manufacturing-marketing integration' in design and 'product competitive advantage'. 2. To exclude the effects of variances other than common method bias on predictor and outcome variables (see Podsakoff et al. Citation2003), this test used three partial models, namely one with proximity and co-design variables, another with proximity and performance variables, and a third with co-design and performance variables.
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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.003 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.003 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it