Policy priorities for improving Global Innovation Index score and innovative performance in upper-middle-income countries: Implications for Armenia

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As an upper-middle-income country, Armenia should develop and implement targeted policies, such as increased R&D investments, education reforms, and industry-academia collaboration, to enhance its innovation performance. Strengthening these areas is expected to contribute to higher Global Innovation Index (GII) rankings, reflecting improved national innovation capacity.
This study aims to estimate the impact of various GII components (including pillars, sub-indices, and sub-pillars) on the overall GII and pillar scores for upper-middle-income countries. Based on these findings, the study seeks to identify Armenia’s key policy priorities and provide targeted recommendations for enhancing its innovation performance. This study employs a cross-sectional regression to analyze the factors influencing GII scores in upper-middle-income countries, assessing the impact of sub-indices, pillars, and sub-pillars. The analysis reveals that market sophistication and creative outputs strongly influence GII scores among upper-middle-income countries, contributing significantly to national innovation performance. Additionally, knowledge and technology outputs, human capital and research, and infrastructure pillars show a statistically significant impact at the 5% level. Notably, even minor improvements in innovation output sub-index scores account for substantial variations in GII rankings. These findings suggest that Armenia should prioritize targeted education reforms, increase R&D investment, and strengthen university-industry linkages to enhance its innovation ecosystem and improve its global competitiveness.

Acknowledgment
The research was supported by the Science Committee of the Republic of Armenia within the framework of the project No. 21T-5B234.

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    • Table 1. Description of variables included in various models
    • Table 2. Descriptive statistics of the pillars, innovation input and output sub-indices, and the GII
    • Table 3. Cross-correlation among pillars
    • Table 4. Identified outliers per sub-pillar
    • Table 5. Estimation results 1 (Method: Ordinary least squares)
    • Table 6. Estimation results 2 (Method: Ordinary least squares)
    • Table 7. Estimation results 3 (Method: Ordinary least squares)
    • Conceptualization
      Svetlana Dallakyan, Anna Makaryan, Verej Isanians, Hamlet Mkrtchyan, Harutyun Sargsyan, Gayane Tovmasyan
    • Data curation
      Svetlana Dallakyan, Anna Makaryan
    • Formal Analysis
      Svetlana Dallakyan, Anna Makaryan, Verej Isanians, Hamlet Mkrtchyan
    • Funding acquisition
      Svetlana Dallakyan
    • Investigation
      Svetlana Dallakyan, Anna Makaryan, Verej Isanians, Hamlet Mkrtchyan
    • Methodology
      Svetlana Dallakyan, Anna Makaryan, Verej Isanians, Hamlet Mkrtchyan, Harutyun Sargsyan, Gayane Tovmasyan
    • Project administration
      Svetlana Dallakyan, Anna Makaryan, Gayane Tovmasyan
    • Resources
      Svetlana Dallakyan, Anna Makaryan, Verej Isanians, Svitlana Bilan
    • Software
      Svetlana Dallakyan, Anna Makaryan, Harutyun Sargsyan
    • Supervision
      Svetlana Dallakyan, Anna Makaryan
    • Validation
      Svetlana Dallakyan, Anna Makaryan, Verej Isanians, Hamlet Mkrtchyan, Harutyun Sargsyan, Gayane Tovmasyan
    • Visualization
      Svetlana Dallakyan, Anna Makaryan, Svitlana Bilan
    • Writing – original draft
      Svetlana Dallakyan, Anna Makaryan, Verej Isanians, Hamlet Mkrtchyan, Harutyun Sargsyan, Gayane Tovmasyan
    • Writing – review & editing
      Svetlana Dallakyan, Anna Makaryan, Verej Isanians, Hamlet Mkrtchyan, Harutyun Sargsyan, Gayane Tovmasyan, Svitlana Bilan