The Intensity of Knowledge Capital Investment in Kenya: Evidence from Manufacturing and Service Sectors
Bibliographic record
Abstract
The study investigates the extent to which firms in Kenya manufacturing and service sectors invest in knowledge capital leading to innovations. 534 firms were included in the analysis. This was the combined data from the first Kenya innovation survey data of 2012, which covered 158 firms, (2008-2011) and the second Kenya innovation survey of 2015 which covered 376 firms (2012-2014). The Crépon, Duguet, and Mairessec (CDM) (1998) model, which considers a system of four equations: innovation propensity, innovation investment, innovation output and performance equations, was used as the estimation technique. The results revealed that, a firm’s decision to spend on R&D was significantly influenced by firm ownership, financial turnover and product innovativeness. A firm’s R&D intensity was significantly determined by its financial turnover and ownership. A firm’s activity and financial turnover were also significant in determining whether it introduced a new product in the market or not. The results of this paper suggest that a firm’s financial turnover was significant in R&D decisions but R&D intensity did not significantly matter to a firm’s product innovativeness. Further, a firm’s level of innovativeness was a significant determinant of its productivity. In addition, the results suggest that, innovations among the Kenyan firms in the manufacturing and service sectors were heavily reliant on financial capital and were struggling to convert knowledge inputs into product output. This study thus recommends a policy that incorporates the academia and firm level innovation with national innovation systems to enhance knowledge and skill intensive innovations that are new to the world.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| 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) | 0.003 | 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".