Capability antecedents and performance outcomes of servitization
Bibliographic record
Abstract
Purpose The purpose of this paper is to theoretically articulate and empirically test an integrated model of capability antecedents and performance outcomes of servitization strategies. The authors characterize servitization strategies based on the offering of two types of services: basic services (BAS) and advanced services (ADS). Design/methodology/approach Hypotheses are tested based on statistical analyses of a large survey of manufacturers from different countries and sectors. Findings The authors find that manufacturing capabilities associate with the provision of BAS, while service capabilities associate with both BAS and ADS; BAS do not impact financial performance, but support the offering of ADS; there seem to be naturally occurring servitization trajectories involving the gradual development of balanced levels of BAS and ADS and adequate levels of manufacturing and service capabilities. Research limitations/implications The findings on servitization trajectories are based on the observation of manufacturing business units at different stages of servitization (cross-sectional data). Practical implications Manufacturers wishing to servitize should distinguish between BAS and ADS and deploy a balanced adoption of BAS and ADS, using BAS as a platform. This should be accompanied with the building of appropriate capabilities. Originality/value This is one of the first studies to show an explicit link between different servitization strategies, capabilities, and servitization maturity. It provides new insights into the servitization paradox and servitization trajectories.
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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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.000 | 0.003 |
| Research integrity | 0.001 | 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".