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Development and validation of the generative artificial intelligence appropriation (GAIA) Scale: A comprehensive measurement tool for assessing user engagement and utilisation

Author

Listed:
  • Khatri, Puja
  • Duggal, Harshleen Kaur
  • Thomas, Asha
  • Corvello, Vincenzo
  • Prałat, Ewa
  • Shiva, Atul

Abstract

Generative Artificial Intelligence Appropriation (GAIA) encapsulates how users adopt Generative Artificial Intelligence tools, adapt them according to their needs, and integrate them into their work. The rapid adoption of generative AI tools has demonstrated their transformative potential to effect significant improvements in the field of business management and change the work habits of their users. Considering the multitude of applicative possibilities offered by the technology, in addition to its nascence, there are significant concerns regarding how the technology can be utilised, necessitating GAIA assessment in the workplace. Existing instruments prove inadequate in providing a comprehensive measurement of GAIA. In response, this research adopts a mixed-method approach, comprising qualitative and quantitative insights from multiple studies. Drawing on multiple samples, this study develops and validates a second-order, reflective-reflective GAIA measure, comprising dimensions of integrative appropriation, adoptive appropriations, customised appropriation, interface appropriation and ethical appropriation. The research encompasses four studies with a distinctive focus on item generation, scale purification, scale refinement and nomological validation. The GAIA scale developed herein offers a robust and comprehensive measure that can be used to explicate, assess, and improve GAIA in the workplace.

Suggested Citation

  • Khatri, Puja & Duggal, Harshleen Kaur & Thomas, Asha & Corvello, Vincenzo & Prałat, Ewa & Shiva, Atul, 2026. "Development and validation of the generative artificial intelligence appropriation (GAIA) Scale: A comprehensive measurement tool for assessing user engagement and utilisation," Technovation, Elsevier, vol. 150(C).
    • Handle: RePEc:eee:techno:v:150:y:2026:i:c:s0166497225002111
    DOI: 10.1016/j.technovation.2025.103379
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    References listed on IDEAS

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