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Public Perception of Science and Technology, Government Support for the AI Sector, and AI Startups

Author

Listed:
  • Hassan F. Gholipour

    (School of Business, Western Sydney University, Sydney, Australia)

  • Reza Tajaddini

    (School of Business, Law, and Entrepreneurship, Swinburne University of Technology, Melbourne, Australia)

  • Mohammad Reza Farzanegan

    (Economics of the Middle East Research Group, CNMS & School of Business and Economics, Philipps-Universität Marburg, Germany)

  • Fredrick Chege

    (School of Business, Western Sydney University, Sydney, Australia)

Abstract

The purpose of this study is to examine the relationship between public opinion of science and technology (S&T), government support to the Artificial intelligence (AI) sector, and AI startups across countries. By controlling for other macro determinants of business formation and applying OLS, Robust and 2SLS regressions, our results show that the number of AI startups is significantly higher in countries where the public has a less negative perception of S&T and where governments provide stronger support to the AI sector. In addition, we show that spiritual perception of S&T is more important than materialistic perception of S&T in explaining the cross-country differences in the number of AI startups. Finally, our results suggest that the availability of AI talent and financial development are positively correlated with a higher number of AI startups.

Suggested Citation

  • Hassan F. Gholipour & Reza Tajaddini & Mohammad Reza Farzanegan & Fredrick Chege, 2025. "Public Perception of Science and Technology, Government Support for the AI Sector, and AI Startups," MAGKS Papers on Economics 202511, Philipps-Universität Marburg, Faculty of Business Administration and Economics, Department of Economics (Volkswirtschaftliche Abteilung).
  • Handle: RePEc:mar:magkse:202511
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    File URL: https://www.uni-marburg.de/en/fb02/research-groups/economics/macroeconomics/research/magks-joint-discussion-papers-in-economics/papers/2025-papers/11-2025.pdf
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