Effects of technology adoption on mass customization ability of broad and narrow market firms
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Despite the increasing number of mass customization (MC) studies in the literature and practice, two research gaps still remain. First, there is a lack of empirical studies to test predicted theoretical relationships between MC strategic, technical, and performance aspects (Tu et al., 2001). Second, there is limited understanding about the role of technology, including information technology in MC systems (Åhlström and Westbrook, 1999). This paper investigates the effects of technology adoption on the MC ability of 315 manufacturers of metal products, machinery, and equipment with either narrow or broad strategic market scope. The results suggest that different technologies have different effects on the MC dimensions of product capability and productivity, and that this effect is determined by the market scope of the firm
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| 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