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<p>Since the emergence of generative artificial intelligence, its adoption has steadily increased, generating significant business value by transforming business models and management processes. In Ghana, despite awareness of this technology, businesses are lagging competitors for various reasons, including inadequate managerial capability, an unsustainable business ecosystem, and the premature stage of generative artificial intelligent (GAI). The study seeks to assess the digital maturity of industries in Ghana concerning GAI adoption, utilizing Technology-Organization-Environment (TOE) Theory. Data was gathered from 250 respondents across diverse firms, and a model tested through Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) using SmartPLS 4. The analysis provided moderate support for the model, showing that firms were more prone to adopt GAI when they perceived its compatibility as mature. However, the results did not support the significance of top management support (TMS). Additionally, relative advantage (RAD) and competitive pressure (COP) exhibited a negative influence on adoption, while government support (GOS) had a positive and significant impact. Study implications and limitations are further discussed and recommendations for future research provided.</p>

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Assessing the Digital Maturity of Industries in Adopting GAI

  • 期刊论文

  • 发表日期：

    2026年3月27日

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  • Etse Dadson Gomado,
  • Forgor Lempogo,
  • Vladimir Fedorovich Ukolov,
  • William Leslie Brown-Acquaye,
  • Olabode Jones Ajimijere

摘要

Since the emergence of generative artificial intelligence, its adoption has steadily increased, generating significant business value by transforming business models and management processes. In Ghana, despite awareness of this technology, businesses are lagging competitors for various reasons, including inadequate managerial capability, an unsustainable business ecosystem, and the premature stage of generative artificial intelligent (GAI). The study seeks to assess the digital maturity of industries in Ghana concerning GAI adoption, utilizing Technology-Organization-Environment (TOE) Theory. Data was gathered from 250 respondents across diverse firms, and a model tested through Confirmatory Factor Analysis (CFA) and Structural Equation Modeling (SEM) using SmartPLS 4. The analysis provided moderate support for the model, showing that firms were more prone to adopt GAI when they perceived its compatibility as mature. However, the results did not support the significance of top management support (TMS). Additionally, relative advantage (RAD) and competitive pressure (COP) exhibited a negative influence on adoption, while government support (GOS) had a positive and significant impact. Study implications and limitations are further discussed and recommendations for future research provided.