A framework for understanding artificial intelligence research: insights from practice. Issue 2 (4th February 2021)

• Record Type: Journal Article
• Authors: Bawack, Ransome Epie; Fosso Wamba, Samuel; Carillo, Kevin Daniel André
• Is Part Of: Journal of enterprise information management. Volume 34:Issue 2(2021)
• Page Start: 645
• Page End: 678
• Publication Date: 2021-02-04
• Subjects: Artificial intelligence -- Industry context -- Literature review -- Fortune 500 -- Information systems research; Management information systems -- Periodicals; Business logistics -- Periodicals; Business -- Data processing -- Periodicals; Management -- Data processing -- Periodicals; 658.05
• DOI: 10.1108/JEIM-07-2020-0284
• Languages: English
• ISSNs: 1741-0398

Abstract

Abstract : Purpose: The current evolution of artificial intelligence (AI) practices and applications is creating a disconnection between modern-day information system (IS) research and practices. The purpose of this study is to propose a classification framework that connects the IS discipline to contemporary AI practices. Design/methodology/approach: We conducted a review of practitioner literature to derive our framework's key dimensions. We reviewed 103 documents on AI published by 25 leading technology companies ranked in the 2019 list of Fortune 500 companies. After that, we reviewed and classified 110 information system (IS) publications on AI using our proposed framework to demonstrate its ability to classify IS research on AI and reveal relevant research gaps. Findings: Practitioners have adopted different definitional perspectives of AI (field of study, concept, ability, system), explaining the differences in the development, implementation and expectations from AI experienced today. All these perspectives suggest that perception, comprehension, action and learning are the four capabilities AI artifacts must possess. However, leading IS journals have mostly published research adopting the "AI as an ability" perspective of AI with limited theoretical and empirical studies on AI adoption, use and impact. Research limitations/implications: First, the framework is based on the perceptions of AI by a limited number of companies, although it includes all the companies leading current AI practices. Secondly, the IS literature reviewed is limited to a handful of journals. Thus, the conclusions may not be generalizable. However, they remain true for the articles reviewed, and they all come from well-respected IS journals. Originality/value: This is the first study to consider the practitioner's AI perspective in designing a conceptual framework for AI research classification. The proposed framework and research agenda are used to show how IS could become a reference discipline in contemporary AI research.