Institutions, resources and innovation in East Africa: A firm level approach
By L. Barasa, Joris Knoben, P. Vermeulen, P. Kimuyu, B. Kinyanjui. 2017
253 citations recorded by the source corpus.
This study examines how firm-level resources interact with regional institutional quality to explain innovation in East Africa. We hypothesize that the institutional environment within which the firm operates moderates the effect of firm-level resources on innovative output. We examine the moderating role of institutions with regards to the transformation of firm-level resources including internal research and development, human capital and managerial experience into innovative output using firm-level data from the World Bank Enterprise Survey and the Innovation Follow-up Survey for three countries in East Africa including Kenya, Tanzania and Uganda. We test our hypotheses using a clustered robust standard errors logistic model. We find that the effects of firm-level resources vary depending on the institutional environment and that regional institutional quality positively moderates the effects of the firm-level resources.
Subfields: Economics and Econometrics, Information Systems
View Paper: https://www.semanticscholar.org/paper/db47289089628e299cce19fc8bd00ae8f241e46f
How researchers see this paper
The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.
In Economics and Econometrics
In Information Systems
Citations in the corpus
- Do political connections matter for firm innovation? Evidence from emerging markets in Central Asia and Eastern Europe (2020, background): “Existing institutional rationales (North, 1981; North, 1990) emphasize how the institutional context surrounding the firm critically determines its behavior in general and innovation in particular (Barasa et al. 2017; Krammer, 2018).”
- Did the global financial crisis impact firms' innovation performance? The role of internal and external knowledge capabilities in high and low tech industries (2018, unknown): “This type of graph, usually used to examine interaction effects (e.g., Barasa et al., 2017; Kingsley et al., 2017), allows us to correctly interpret the interaction effect in more detail (Corten et al., 2017).”