The Influence of Firm Characteristics on the Relationship Between Operational Innovation and Performance of Manufacturing Firms in Kenya
Bibliographic record
Abstract
With a marketplace characterized by increased competition globally and constant changes in customer needs and wants, there is a need to adopt operational innovations while complying with the business environment (internal capabilities) and the firm characteristics, influencing factors in the innovation adoption and implementation. For this reason, this study aimed to investigate the influence of firm characteristics on the relationship between operational innovation and the performance of manufacturing firms in Kenya. The positivism approach was used to increase the reliability of investigation findings for generalization. Further, a descriptive research design was adopted, equally to increase the reliability of the survey. Sample of 182 firms with strong affiliations to Kenya Association of Manufacturers (KAM) was used. The firms had 14 subcategories based on the products they manufactured. Statistical Package for the Social Sciences (SPSS) and smart PLS4 were used for data analysis, and regression analysis was used for conclusive results. The findings reveal that firm characteristics have a sizable impact on the association between innovation and firm performance.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".