Article citationsMore>>

Broring, A., Schmid, S., Schindhelm, C.K., Khelil, A., Kabisch, S., Kramer, D., Le Phuoc, D., Mitic, J., Anicic, D. and Teniente Lopez, E. (2017) Enabling IoT Ecosystems through Platform Interoperability. IEEE Software, 1, 54-61. https://doi.org/10.1109/MS.2017.2

has been cited by the following article:

• TITLE: Ratings of Driving Factors for the Adoption and Implementation of Artificial Intelligence in the Public Sector

  AUTHORS: Samuel Narh Dorhetso, Bismark Dzahene Quarshie

  KEYWORDS: Privacy and Security, Innovation, Artificial Intelligence, Government, Technology, Best-Worst Method

  JOURNAL NAME: Open Access Library Journal, Vol.10 No.5, May 30, 2023

  ABSTRACT: The study constructed an estimation of the significance of driving factors that influence artificial intelligence (AI) adoption and implementation in the public sector, and accentuated a critical research area that is currently understudied. A theoretical framework, underpinned by the diffusion of innovation (DOI) theory, was developed from a mingling of the technology, organization, and environment (TOE) framework and the human, organisation, and technology (HOT) fit model. The best-worst method was used to scrutinize and rank the identified driving factors according to their weighted averages. The findings of the study pointed to privacy and security; reliability, serviceability and functionality; regulation; interpretability and ease of use; IT infrastructure and data; and ethical issues as the highest ranked driving factors for AI adoption and implementation in government institutions. The study has significant implications for policy makers and practitioners, as it would augment their perspectives on how to adopt and implement AI innovations.