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.
Cite this paper
Dorhetso, S. N. and Quarshie, B. D. (2023). Ratings of Driving Factors for the Adoption and Implementation of Artificial Intelligence in the Public Sector. Open Access Library Journal, 10, e9982. doi: http://dx.doi.org/10.4236/oalib.1109982.
Davenport, T., Guha, A., Grewal, D. and Bressgott, T. (2020) How Artificial Intelligence Will Change the Future of Marketing. Journal of the Academy of Marketing Science, 48, 24-42. https://doi.org/10.1007/s11747-019-00696-0
Holzinger, A., Tjoa, A.M. and Kieseberg, P. (2021) Digital Transformation for Sustainable Development Goals (SDGs)—A Security, Safety and Privacy Perspective on AI. International Cross-Domain Conference, CD-MAKE 2021, 17-20 August 2021, 1-20. https://doi.org/10.1007/978-3-030-84060-0_1
Lauterbach, A. (2019) Artificial Intelligence and Policy: QuoVadis? Digital Policy, Regulation and Governance, 21, 238-263.
https://doi.org/10.1108/DPRG-09-2018-0054
Sun, T.Q. and Medaglia, R. (2019) Mapping the Challenges of Artificial Intelligence in the Public Sector: Evidence from Public Healthcare. Government Information Quarterly, 36, 368-383. https://doi.org/10.1016/j.giq.2018.09.008
van Noordt, C. and Misuraca, G. (2020) Exploratory Insights on Artificial Intelligence for Government in Europe. Social Science Computer Review, 40, 426-444.
https://doi.org/10.1177/0894439320980449
Alexopoulos, C., Lachana, Z., Androutsopoulou, A., Diamantopoulou, V., Charalabidis, Y. and Loutsaris, M.A. (2019) How Machine Learning Is Changing e-Government. Proceedings of the 12th International Conference on Theory and Practice of Electronic Governance—ICEGOV2019, Melbourne, 3-5 April 2019, 354-363. https://doi.org/10.1145/3326365.3326412
Schrader, D.E. and Ghosh, D. (2018) Proactively Protecting against the Singularity: Ethical Decision Making in AI. IEEE Security and Privacy, 16, 56-63.
https://doi.org/10.1109/MSP.2018.2701169
Stock, T. and Seliger, G. (2016) Opportunities of Sustainable Manufacturing in Industry 4.0. Procedia CIRP, 40, 536-541. https://doi.org/10.1016/j.procir.2016.01.129
Pandey, S.K., Davis, R.S., Pandey, S. and Peng, S. (2016) Transformational Leadership and the Use of Normative Public Values: Can Employee Be Inspired to Serve Larger Public Purpose? Public Administration, 1, 204-222.
https://doi.org/10.1111/padm.12214
Yusof, N.M., Paul, R.J. and Stergioulas, L.K. (2006) Towards a Framework for Health Information Systems Evaluation. The 39th Hawaii International Conference on System Science, Kauai, 4-7 January 2006, 95a.
https://doi.org/10.1109/HICSS.2006.491
Karunasena, K. and Deng, H. (2012) Critical Factors for Evaluating the Public Value of e-Government in Sri Lanka. Government Information Quarterly, 1, 76-84.
https://doi.org/10.1016/j.giq.2011.04.005
Schedler, K., Guenduez, A.A. and Frischknecht, R. (2019) How Smart Can Government Be? Exploring Barriers to the Adoption of Smart Government. Information Polity, 24, 3-20. https://doi.org/10.3233/IP-180095
De Vries, H., Bekkers, V. and Tummers, L. (2016) Innovation in the Public Sector: A Systematic Review and Future Research Agenda. Public Administration, 94, 146-166. https://doi.org/10.1111/padm.12209
Abouelmehdi, K., Beni-Hessane, A. and Khaloufi, H. (2018) Big Healthcare Data: Preserving Security and Privacy. Journal of Big Data, 5, Article No. 1.
https://doi.org/10.1186/s40537-017-0110-7
Thesmar, D., Sraer, D., Pinheiro, L., Dadson, N., Veliche, R. and Greenberg, P. (2019) Combining the Power of Artificial Intelligence with the Richness of Healthcare Claims Data: Opportunities and Challenges. PharmacoEconomics, 37, 745-752.
https://doi.org/10.1007/s40273-019-00777-6
Nilashi, M., Ahmadi, H., Ahani, A., Ravangard, R. and Ibrahim, O.B. (2016) Determining the Importance of Hospital Information System Adoption Factors Using Fuzzy Analytic Network Process (ANP). Technological Forecasting and Social Change, 111, 244-264. https://doi.org/10.1016/j.techfore.2016.07.008
Morse, R.S. (2010) Integrative Public Leadership: Catalysing Collaboration to Create Public Value. The Leadership Quarterly, 2, 231-245.
https://doi.org/10.1016/j.leaqua.2010.01.004
Edler, J., Ruhland, S., Hafner, S., Rigby, J., Hommen, L., Rolfstam, M., Charles, E., Tsipouri, L. and Papadakou, M. (2006) Innovation and Public Procurement. Review of Issues at Stake. In Study for the European Commission (No ENTR/03/24). Fraunhofer Institute for Systems and Innovation Research, Karlsruhe.
Edquist, C., Hommen, L. and Tsipouri, L. (2000) Public Technology Procurement and Innovation. Vol. 16, Springer, Berlin.
https://doi.org/10.1007/978-1-4615-4611-5
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
Vellido, A. (2019) Societal Issues Concerning the Application of Artificial Intelligence in Medicine. Kidney Diseases, 5, 11-17. https://doi.org/10.1159/000492428
Lee, Y.J. and Park, J.Y. (2018) Identification of Future Signal Based on the Quantitative and Qualitative Text Mining: A Case Study on Ethical Issues in Artificial Intelligence. Quality and Quantity, 52, 653-667.
https://doi.org/10.1007/s11135-017-0582-8
Wang, Z.G., Xu, R., Lin, H. and Wang, R.J. (2019) Energy Performance Contracting, Risk Factors, and Policy Implications: Identification and Analysis of Risks Based on the Best-Worst Network Method. Energy, 170, 1-13.
https://doi.org/10.1016/j.energy.2018.12.140
Rezaei, J. (2016) Best-Worst Multi-Criteria Decision Making Method: Some Properties and a Linear Model. Omega, 64, 126-130.
https://doi.org/10.1016/j.omega.2015.12.001
Fu, J.R., Farn, C.K. and Chao, W.P. (2006) Acceptance of Electronic Tax Filing: A Study of Taxpayer Intentions. Information & Management, 43, 109-126.
https://doi.org/10.1016/j.im.2005.04.001
Yang, Y., Lau, A.K.W., Lee, P.K.C., Yeung, A.C.L. and Cheng, T.C.E. (2018) Efficacy of China’s Strategic Environmental Management in Its Institutional Environment. International Journal of Operations & Production Management, 39, 138-163.
https://doi.org/10.1108/IJOPM-11-2017-0695
Dorhetso, S.N. (2023) Ratings of Barriers and Challenges of Financial Technology in a Developing Economy. In: Ahmed, S., Addae, J. and Ofori, K., Eds., Exploring the Dark Side of FinTech and Implications of Monetary Policy, IGI Global, Hershey, 67-92. https://doi.org/10.4018/978-1-6684-6381-9.ch004
Dorhetso, S.N., Boakye, L.Y. and Amofa-Sarpong, K. (2023) Determinants of Small and Medium-Sized Enterprises Access to Financial Services in Ghana. In: Sustainable Education and Development—Sustainable Industrialization and Innovation, ARCA 2022, Springer, Cham, 278-292.
https://doi.org/10.1007/978-3-031-25998-2_21
Orji, I.J. and Wei, S. (2016) A Detailed Calculation Model for Costing of Green Manufacturing. Industrial Management & Data Systems, 116, 65-86.
https://doi.org/10.1108/IMDS-04-2015-0140