Author
Listed:
- Emmanuel Duncan
(Lunara Learning Hub)
Abstract
Despite AI and data analytics' growing importance for competitiveness, empirical evidence in Ghana is scarce. Organizations are urged to adopt digital transformation, but the link to performance is unclear. This study explores AI and analytics adoption in Ghanaian enterprises, assessing its impact on performance and identifying adoption barriers. A cross-sectional survey of 1,107 professionals, including HR managers, business leaders, and C-suite executives, was conducted. Adoption rates were analyzed using descriptive statistics, while correlation and multiple regression were employed to test the relationship between AI, data analytics, and organizational performance. Results show that 67% of enterprises have adopted AI, data analytics, or both, while 33% remain non-adopters. Among adopters, 30.3% integrate AI and analytics, 28.9% use analytics only, and 7.7% use AI only. Sectoral adoption varies, with Financial Services (85%) leading, while Retail and the Public Sector lag at 50%. Both AI and analytics significantly improve performance, with stronger results when integrated. Organizations should prioritize analytics as a foundation for AI, invest in workforce capability, and secure leadership commitment to scale adoption successfully. Wider adoption of AI and data analytics in Ghanaian enterprises has the potential to reshape work and service delivery across sectors contributing to national digital transformation. The findings advance understanding of how digital technologies influence performance in emerging market particularly Ghana. Its findings inform the design of strategies and policies that harness data-driven decision-making to drive organizational performance.
Suggested Citation
Download full text from publisher
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:bjf:ijltem:v:14:y:2025:i:10:a:549. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Dr. Pawan Verma (email available below). General contact details of provider: https://www.ijltemas.in/ .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.