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Causal complexity analysis of the Global Innovation Index

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  • Yu, Tiffany Hui-Kuang
  • Huarng, Kun-Huang
  • Huang, Duen-Huang

Abstract

This research aims to identify the common causal complexity for the Global Innovation Index (GII), which measures various dimensions of the innovation ecosystem by country. We take all these variables as antecedents and the GII score representing the innovation competence of each country as the outcome and employ GII dataset from 2016 to 2020 for analysis. Because fuzzy set/Qualitative Comparative Analysis (fsQCA) has advantages over conventional statistical analysis and is good at expressing different causal complexities for a problem, this study utilizes it as the research method for analysis. The findings identify a common causal combination with the highest consistency and coverage among all the causal combinations in each year. This causal combination can be used as a representative to interpret GII.

Suggested Citation

  • Yu, Tiffany Hui-Kuang & Huarng, Kun-Huang & Huang, Duen-Huang, 2021. "Causal complexity analysis of the Global Innovation Index," Journal of Business Research, Elsevier, vol. 137(C), pages 39-45.
    • Handle: RePEc:eee:jbrese:v:137:y:2021:i:c:p:39-45
    DOI: 10.1016/j.jbusres.2021.08.013
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    References listed on IDEAS

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    Cited by:

    1. Yu, Tiffany Hui-Kuang & Huarng, Kun-Huang, 2023. "Configural analysis of GII’s internal structure," Journal of Business Research, Elsevier, vol. 154(C).
    2. Huarng, Kun-Huang & Yu, Tiffany Hui-Kuang, 2022. "Analysis of Global Innovation Index by structural qualitative association," Technological Forecasting and Social Change, Elsevier, vol. 182(C).
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    5. Huarng, Kun-Huang & Yu, Tiffany Hui-Kuang, 2022. "Causal complexity analysis for fintech adoption at the country level," Journal of Business Research, Elsevier, vol. 153(C), pages 228-234.

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