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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. Abroon Qazi, 2025. "Innovation forecasting: mapping pathways with global indicators," Journal of Innovation and Entrepreneurship, Springer, vol. 14(1), pages 1-16, December.
    2. Yu, Tiffany Hui-Kuang & Huarng, Kun-Huang, 2023. "Configural analysis of GII’s internal structure," Journal of Business Research, Elsevier, vol. 154(C).
    3. 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).
    4. Vasist, Pramukh Nanjundaswamy & Krishnan, Satish, 2024. "Powered by innovation, derailed by disinformation: A multi-country analysis of the influence of online political disinformation on nations' innovation performance," Technological Forecasting and Social Change, Elsevier, vol. 199(C).
    5. Ahangama, Supunmali & Krishnan, Satish & Singh, Nidhi & Vishnoi, Sushant Kumar, 2025. "How do state internet regulations impact innovation? A cross-country configural narrative," Technological Forecasting and Social Change, Elsevier, vol. 219(C).
    6. Shen Zhong & Zhicheng Zhou & Hongjun Jing & Daizhi Jin, 2025. "What Affects Durable National Innovation Performance? An Analysis in the Context of the COVID-19 Pandemic," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 16(3), pages 11856-11895, September.
    7. Pureheart Ogheneogaga Irikefe & Mohammad Falahat & Ahmad Danial Zainudin & Ihtisham Ullah & Nohman Khan & Bernard Ojonugwa Anthony, 2026. "Disaggregating innovation for sustainable development in ASEAN: Panel evidence on the moderating role of government effectiveness," PLOS ONE, Public Library of Science, vol. 21(3), pages 1-23, March.
    8. Yu, Tiffany Hui-Kuang & Huarng, Kun-Huang, 2024. "Causal analysis of SDG achievements," Technological Forecasting and Social Change, Elsevier, vol. 198(C).
    9. 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.
    10. Mehmet Ali Koseoglu & Hasan Evrim Arici & Omer Faruk Aladag, 2026. "Machine Learning Analysis of Global Innovation Index Enablers: Regional Variations and Covid-19 Effects," Journal of the Knowledge Economy, Springer;Portland International Center for Management of Engineering and Technology (PICMET), vol. 17(2), pages 3337-3369, April.

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