Understanding Imbalanced Transmission from R&D Inputs into Innovation Outputs and Impacts: Evidence from Kazakhstan Article Swipe
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3390/economies13020025
Innovation ecosystems use R&D inputs to generate innovation outputs first and innovation impacts later. But some countries show a relatively low transmission, such as in the case of Kazakhstan, the largest economy in Central Asia. This article analyzes the transmission from R&D into innovation outputs and impacts through a framework for which different factors matter, such as the company size, education and skills, competition, exports, and foreign ownership. Transmission is conceptually understood in two steps: from R&D into innovation outputs, and from innovation output into innovation impacts. The main hypothesis is that the high endowments of these company factors should lead to the better transmission of results and improved performance in terms of outputs and impacts. We test this using new evidence from Kazakhstan and the ECA region (Europe and Central as defined by the World Bank) as benchmarking, and data are from the Global Innovation Index (descriptive section) and the World Bank Enterprise Surveys (analytical section). The econometrics are a Crépon–Duguet–Mairesse (CDM) model in three steps: factors for propensity to invest in R&D, then to innovate, and, finally, innovation impacts on productivity. Results confirm the positive roles of factors, such as exports and education, in positive transmissions and uneven or insignificant results on productivity impacts from characteristics, such as age, size, and foreign ownership. The specifics for Kazakhstan suggest a potential for business innovation growth in the country. The paper concludes by suggesting key policy measures to unlock the potential for business innovation at a country level.
Related Topics
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/economies13020025
- https://www.mdpi.com/2227-7099/13/2/25/pdf?version=1737542332
- OA Status
- gold
- Cited By
- 1
- References
- 72
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4406702214
Raw OpenAlex JSON
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https://openalex.org/W4406702214Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3390/economies13020025Digital Object Identifier
- Title
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Understanding Imbalanced Transmission from R&D Inputs into Innovation Outputs and Impacts: Evidence from KazakhstanWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
-
2025Year of publication
- Publication date
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2025-01-22Full publication date if available
- Authors
-
Stefka Slavova, Luis Rubalcaba, José N. Franco-RiquelmeList of authors in order
- Landing page
-
https://doi.org/10.3390/economies13020025Publisher landing page
- PDF URL
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https://www.mdpi.com/2227-7099/13/2/25/pdf?version=1737542332Direct link to full text PDF
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YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
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https://www.mdpi.com/2227-7099/13/2/25/pdf?version=1737542332Direct OA link when available
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Transmission (telecommunications), Economics, Business, Computer science, TelecommunicationsTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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72Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.which | 51 |
| abstract_inverted_index.Global | 144 |
| abstract_inverted_index.better | 103 |
| abstract_inverted_index.growth | 225 |
| abstract_inverted_index.inputs | 4 |
| abstract_inverted_index.invest | 171 |
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| abstract_inverted_index.level. | 247 |
| abstract_inverted_index.output | 83 |
| abstract_inverted_index.policy | 235 |
| abstract_inverted_index.region | 127 |
| abstract_inverted_index.should | 99 |
| abstract_inverted_index.steps: | 74, 166 |
| abstract_inverted_index.uneven | 199 |
| abstract_inverted_index.unlock | 238 |
| abstract_inverted_index.(Europe | 128 |
| abstract_inverted_index.Central | 33, 130 |
| abstract_inverted_index.R&D | 3, 41, 76 |
| abstract_inverted_index.Results | 183 |
| abstract_inverted_index.Surveys | 154 |
| abstract_inverted_index.article | 36 |
| abstract_inverted_index.company | 58, 97 |
| abstract_inverted_index.confirm | 184 |
| abstract_inverted_index.country | 246 |
| abstract_inverted_index.defined | 132 |
| abstract_inverted_index.economy | 31 |
| abstract_inverted_index.exports | 192 |
| abstract_inverted_index.factors | 53, 98, 167 |
| abstract_inverted_index.foreign | 66, 213 |
| abstract_inverted_index.impacts | 12, 46, 180, 205 |
| abstract_inverted_index.largest | 30 |
| abstract_inverted_index.matter, | 54 |
| abstract_inverted_index.outputs | 8, 44, 113 |
| abstract_inverted_index.results | 106, 202 |
| abstract_inverted_index.skills, | 62 |
| abstract_inverted_index.suggest | 219 |
| abstract_inverted_index.through | 47 |
| abstract_inverted_index.R&D, | 173 |
| abstract_inverted_index.analyzes | 37 |
| abstract_inverted_index.business | 223, 242 |
| abstract_inverted_index.country. | 228 |
| abstract_inverted_index.evidence | 121 |
| abstract_inverted_index.exports, | 64 |
| abstract_inverted_index.factors, | 189 |
| abstract_inverted_index.finally, | 178 |
| abstract_inverted_index.generate | 6 |
| abstract_inverted_index.impacts. | 86, 115 |
| abstract_inverted_index.improved | 108 |
| abstract_inverted_index.measures | 236 |
| abstract_inverted_index.outputs, | 79 |
| abstract_inverted_index.positive | 186, 196 |
| abstract_inverted_index.section) | 148 |
| abstract_inverted_index.concludes | 231 |
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| abstract_inverted_index.different | 52 |
| abstract_inverted_index.education | 60 |
| abstract_inverted_index.framework | 49 |
| abstract_inverted_index.innovate, | 176 |
| abstract_inverted_index.potential | 221, 240 |
| abstract_inverted_index.section). | 156 |
| abstract_inverted_index.specifics | 216 |
| abstract_inverted_index.Enterprise | 153 |
| abstract_inverted_index.Innovation | 0, 145 |
| abstract_inverted_index.Kazakhstan | 123, 218 |
| abstract_inverted_index.ecosystems | 1 |
| abstract_inverted_index.education, | 194 |
| abstract_inverted_index.endowments | 94 |
| abstract_inverted_index.hypothesis | 89 |
| abstract_inverted_index.innovation | 7, 11, 43, 78, 82, 85, 179, 224, 243 |
| abstract_inverted_index.ownership. | 67, 214 |
| abstract_inverted_index.propensity | 169 |
| abstract_inverted_index.relatively | 19 |
| abstract_inverted_index.suggesting | 233 |
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| abstract_inverted_index.Kazakhstan, | 28 |
| abstract_inverted_index.performance | 109 |
| abstract_inverted_index.(descriptive | 147 |
| abstract_inverted_index.Transmission | 68 |
| abstract_inverted_index.competition, | 63 |
| abstract_inverted_index.conceptually | 70 |
| abstract_inverted_index.econometrics | 158 |
| abstract_inverted_index.productivity | 204 |
| abstract_inverted_index.transmission | 39, 104 |
| abstract_inverted_index.benchmarking, | 138 |
| abstract_inverted_index.insignificant | 201 |
| abstract_inverted_index.productivity. | 182 |
| abstract_inverted_index.transmission, | 21 |
| abstract_inverted_index.transmissions | 197 |
| abstract_inverted_index.characteristics, | 207 |
| abstract_inverted_index.Crépon–Duguet–Mairesse | 161 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 2 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.90236553 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |