Barriers to artificial intelligence adoption in smart cities: A systematic literature review and research agenda
Government Information Quarterly, ISSN: 0740-624X, Vol: 40, Issue: 3, Page: 101814
2023
- 30Citations
- 201Captures
- 1Mentions
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Most Recent News
Reports Summarize Artificial Intelligence Findings from University of Laval (Barriers To Artificial Intelligence Adoption In Smart Cities: a Systematic Literature Review and Research Agenda)
2023 AUG 07 (NewsRx) -- By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News -- Research findings on Artificial
Review Description
Artificial intelligence (AI) plays a prominent role in smart cities' development and offers benefits to different services such as finance, healthcare, security, agriculture, transport, education, and manufacturing. Despite the expected benefits, the adoption of AI varies from one smart city to another, due in part to barriers that can inhibit a smart city from adopting AI. The aim of this paper is to provide a comprehensive view of the barriers faced by smart cities. Through a systematic literature review, this study identifies 18 primary and secondary barriers grouped into three main categories — technology, environment, and organization. This research contributes to the literature by developing a typology of AI adoption barriers based on the Technology-Organization-Environment (TOE) perspective. The typology provides a novel mapping of the barriers to AI adoption faced by smart cities and suggests directions for further investigation through a cohesive research agenda. At a practical level, the findings will allow policymakers, planners, and citizens to make more informed decisions about AI adoption. Practical implications are also proposed for guiding smart cities to increase the adoption of AI.
Bibliographic Details
http://www.sciencedirect.com/science/article/pii/S0740624X2300014X; http://dx.doi.org/10.1016/j.giq.2023.101814; http://www.scopus.com/inward/record.url?partnerID=HzOxMe3b&scp=85150472058&origin=inward; https://linkinghub.elsevier.com/retrieve/pii/S0740624X2300014X; https://dx.doi.org/10.1016/j.giq.2023.101814
Elsevier BV
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