An Empirical Examination of The Relationship between Capability Maturity and Firm Performance across Manufacturing and IT Industries
Bibliographic record
Abstract
We investigate the effect on firm performance of the motivation for applying maturity models in manufacturing and information technology organizations. We expect the association between profitability and maturity models to be less if motivated by external contract requirements (e.g., for certain government contracts), than if motivated internally to improve processes. Using a sample of firm-year observations for 1,105 SEC registrants in the manufacturing (Standard Industry Classification (SIC): 3600-3812) and IT industries (SIC: 7370-7374) for 2017 and 2018, and CMMI information from the CMMI institute published appraisal results system, it is observed that 28 public firms (17 IT firm-years and 23 manufacturing firm-years) in the sample had CMMI appraisals between 2017 and 2018. We use logistic regression to test if the likelihood of CMMI appraisal is positively associated with government sales. The results support for the manufacturing industry, but not for the IT industry, prior research’s assertion that maturity is a source for competitive advantage.
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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 | 0.021 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".