Artificial intelligence research: A review on dominant themes, methods, frameworks and future research directions
This article presents an analysis of artificial intelligence (AI) in information systems and innovation-related journals to determine the current issues and stock of knowledge in AI literature, research methodology, frameworks, level of analysis and conceptual approaches to identify research gaps fo...
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| Format: | Journal Article |
| Language: | Inglés |
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Elsevier
2024
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| Online Access: | https://hdl.handle.net/10568/159746 |
| _version_ | 1855532886498738176 |
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| author | Ofosu-ampong, Kingsley |
| author_browse | Ofosu-ampong, Kingsley |
| author_facet | Ofosu-ampong, Kingsley |
| author_sort | Ofosu-ampong, Kingsley |
| collection | Repository of Agricultural Research Outputs (CGSpace) |
| description | This article presents an analysis of artificial intelligence (AI) in information systems and innovation-related journals to determine the current issues and stock of knowledge in AI literature, research methodology, frameworks, level of analysis and conceptual approaches to identify research gaps for future investigations. A total of 85 peer-reviewed articles from 2020 to 2023 were used in the analysis. The findings show that extant literature is skewed towards the prevalence of technological issues and highlights the relatively lower focus on other themes, such as contextual knowledge co-creation issues, conceptualisation, and application domains. While there have been increasing technological issues with artificial intelligence, the three identified areas of security concern are data security, model security and network security. Furthermore, the review found that contemporary AI, which continually drives the boundaries of computational capabilities to tackle increasingly intricate decision-making challenges, distinguishes itself from earlier iterations in two primary aspects that significantly affect organisational learning in dealing with AI's potential: autonomy and learnability. This study contributes to AI research by providing insights into current issues, research methodology, level of analysis and conceptual approaches, and AI framework to help identify research gaps for future investigations. |
| format | Journal Article |
| id | CGSpace159746 |
| institution | CGIAR Consortium |
| language | Inglés |
| publishDate | 2024 |
| publishDateRange | 2024 |
| publishDateSort | 2024 |
| publisher | Elsevier |
| publisherStr | Elsevier |
| record_format | dspace |
| spelling | CGSpace1597462025-11-11T19:00:09Z Artificial intelligence research: A review on dominant themes, methods, frameworks and future research directions Ofosu-ampong, Kingsley literature reviews artificial intelligence innovation technology research classification This article presents an analysis of artificial intelligence (AI) in information systems and innovation-related journals to determine the current issues and stock of knowledge in AI literature, research methodology, frameworks, level of analysis and conceptual approaches to identify research gaps for future investigations. A total of 85 peer-reviewed articles from 2020 to 2023 were used in the analysis. The findings show that extant literature is skewed towards the prevalence of technological issues and highlights the relatively lower focus on other themes, such as contextual knowledge co-creation issues, conceptualisation, and application domains. While there have been increasing technological issues with artificial intelligence, the three identified areas of security concern are data security, model security and network security. Furthermore, the review found that contemporary AI, which continually drives the boundaries of computational capabilities to tackle increasingly intricate decision-making challenges, distinguishes itself from earlier iterations in two primary aspects that significantly affect organisational learning in dealing with AI's potential: autonomy and learnability. This study contributes to AI research by providing insights into current issues, research methodology, level of analysis and conceptual approaches, and AI framework to help identify research gaps for future investigations. 2024-06 2024-11-14T14:46:52Z 2024-11-14T14:46:52Z Journal Article https://hdl.handle.net/10568/159746 en Open Access application/pdf Elsevier Ofosu-ampong, K. (2024) Artificial intelligence research: A review on dominant themes, methods, frameworks and future research directions. Telematics and Informatics Reports 14: 100127. ISSN: 2772-5030 |
| spellingShingle | literature reviews artificial intelligence innovation technology research classification Ofosu-ampong, Kingsley Artificial intelligence research: A review on dominant themes, methods, frameworks and future research directions |
| title | Artificial intelligence research: A review on dominant themes, methods, frameworks and future research directions |
| title_full | Artificial intelligence research: A review on dominant themes, methods, frameworks and future research directions |
| title_fullStr | Artificial intelligence research: A review on dominant themes, methods, frameworks and future research directions |
| title_full_unstemmed | Artificial intelligence research: A review on dominant themes, methods, frameworks and future research directions |
| title_short | Artificial intelligence research: A review on dominant themes, methods, frameworks and future research directions |
| title_sort | artificial intelligence research a review on dominant themes methods frameworks and future research directions |
| topic | literature reviews artificial intelligence innovation technology research classification |
| url | https://hdl.handle.net/10568/159746 |
| work_keys_str_mv | AT ofosuampongkingsley artificialintelligenceresearchareviewondominantthemesmethodsframeworksandfutureresearchdirections |