The relationship between servitization and product customization strategies
Bibliographic record
Abstract
Purpose The purpose of this paper is to examine the relationship between product customization and servitization strategies, specifically the relationship between product customization strategy intensity and degree of servitization (offering of basic and/or advanced services) and the moderating role of product customization strategy alignment on that relationship. Design/methodology/approach The authors develop and test hypotheses through partial least squares path modeling to analyze data from the Sixth International Manufacturing Strategy Survey, involving 931 manufacturers in 22 countries. Findings The results indicate that customization strategy intensity is positively associated with the offering of basic and advanced services; these relationships are not moderated by customization strategy alignment. Practical implications Manufacturers pursuing product customization strategies may be especially well positioned to servitize, even those with misalignment in strategic choices. Paradoxically, while manufacturers of standard products might look at servitization as an attractive strategy to differentiate their value proposition, they appear to be less servitized than manufacturers pursuing product customization. Originality/value This is one of the first studies to examine how manufacturing strategy choices (intensity and alignment) influence the adoption of servitization strategies. The study introduces manufacturing strategy as a contingency factor that influences the adoption of servitization, answering calls for the study of servitization contingencies.
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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 | 0.017 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| 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) | 0.005 | 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".