Adoption of Technological and Non-technological Innovation in Manufacturing and Service Firms
Bibliographic record
Abstract
Abstract The motivation for this study builds on the rapid changes in the business environment that have led companies to include innovation as one of the strategies for productivity growth, competitiveness, and sustainability. The objective of this work is to analyze the interaction and complementarity between technological innovation practices and non-technological innovation used by companies. The methodology used by the paper is a Bivariate Probit model applied to micro-data from 1897 firms in Cameroon, Côte d’Ivoire, and Senegal. The effect of the adoption of a technological innovation on a non-technological innovation practice is estimated by conditional probabilities using two-dimensional normal distributions. The results show strong significant correlations between the various innovation practices within the firm with important complementarity effects between them. This complementarity is the proof that the adoption of a technological innovation practice leads to the initiation of another non-technological innovative activity and vice versa for a better performance of the firm’s activities. These complementarity effects between the different types of innovations are heterogeneous according to the sector of activity, which suggests that innovation policies should be specific to each sector. The originality of the study is premised on the use of both technological and non-technological dimensions of innovation in the assessment of how innovations affect manufacturing and service firms in Africa.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".