The interplay between innovation adoption and pricing competitiveness in Sub-Saharan Africa
Bibliographic record
Abstract
The objective of this work is to evaluate the effects of adopting technological innovation, non-technological innovation, and their complementarity on price competitiveness. This work employs a recursive bivariate probit model applied to microdata from 1897 firms in three Sub-Saharan countries: Cameroon, Côte d'Ivoire, and Senegal. This model allows us to solve the endogeneity problem by assessing the complementarity relationship between technological and non-technological innovation practices and their effects on firm competitiveness. The results confirm that technological and non-technological innovations are complementary and have significant effects on firms' competitive advantage in terms of price. This complementarity constitutes evidence that their simultaneous adoption contributes more to firms' competitiveness than the individual adoption of each type of innovation. Non-technological innovations facilitate the effectiveness of technological innovations, which leads to a competitive advantage of about 26% when both types of innovations are adopted together. However, firms can also suffer significant losses in market share as a result of the non-adoption of innovations. Indeed, firms that do not adopt any innovations deteriorate their competitive advantage in terms of price by 4% on average.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| 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".