Artificial intelligence for decision making in the era of Big Data - evolution, challenges and research agenda

By Yanqing Duan, John S. Edwards, Yogesh Kumar Dwivedi. International Journal of Information Management, 2019

2,126 citations recorded by the source corpus.

Artificial intelligence (AI) has been in existence for over six decades and has experienced AI winters and springs. The rise of super computing power and Big Data technologies appear to have empowered AI in recent years. The new generation of AI is rapidly expanding and has again become an attractive topic for research. This paper aims to identify the challenges associated with the use and impact of revitalised AI based systems for decision making and offer a set of research propositions for information systems (IS) researchers. The paper first provides a view of the history of AI through the relevant papers published in the International Journal of Information Management (IJIM). It then discusses AI for decision making in general and the specific issues regarding the interaction and integration of AI to support or replace human decision makers in particular. To advance research on the use of AI for decision making in the era of Big Data, the paper offers twelve research propositions for IS researchers in terms of conceptual and theoretical development, AI technology-human interaction, and AI implementation.

Subfields: Industrial and Manufacturing Engineering, Management Information Systems, Management Science and Operations Research

View Paper: https://www.semanticscholar.org/paper/0574a3abc98f0e6bd035a4ff4ea107cfba45d3d4

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Citations in the corpus

The promise and challenges of generative AI in education (2024, neutral): “Traditional AI algorithms, such as decision trees, random forests, support vector machines, and k-means clustering, provided useful but limited capabilities (Duan, Edwards, and Dwivedi 2019).”
An integrated artificial intelligence framework for knowledge creation and B2B marketing rational decision making for improving firm performance (2021, methodology): “Big data powered artificial intelligence and external market knowledge creation BDAI has proven to be highly beneficial in this digital age (Duan et al., 2019).”
Artificial intelligence in operations management and supply chain management: an exploratory case study (2021, background): “AI refers to the ability of machines to learn from experience and make decisions on series of performance as a human with intelligence (Duan, Edwards, and Dwivedi 2019).”
Artificial intelligence in business: State of the art and future research agenda (2020, background): “Finally, Duan, Edwards, and Dwivedi (2019) analyze relevant articles published in International Journal of Information Management to identify issues and challenges around AI for decision making in the era of big data, proposing theoretical development and AI implementation.”
Use of AI-based tools for healthcare purposes: a survey study from consumers’ perspectives (2020, background): “Society generally is yet to fully grasp many of the ethical and regulatory considerations associated with AI and big data [86].”
Artificial Intelligence (AI): Multidisciplinary perspectives on emerging challenges, opportunities, and agenda for research, practice and policy (2019, background): “While many of them start to be highlighted in a booming number of viewpoints on AI in the public sector (Desouza, 2018; Duan et al., 2019), we focus here on two specific ones that we consider outstanding: relieving cognitive resources of public workers, and fostering citizen trust.”
Understanding managers’ attitudes and behavioral intentions towards using artificial intelligence for organizational decision-making (2021, background): “The reasons for this included (Duan et al., 2019): the bad reputation of some expert systems, that meant it was not an attractive label; the difficulty of representing all of an expert ’ s knowledge in a system, leading some to feel that the use of the term “ expert ” was unreasonable; and the…”