The DOI matches its Crossref record; 24 of the 24 references it cites were found in a registry or an index.
Artificial intelligence in business: State of the art and future research agenda
Abstract
Abstract This study provides an overview of state-of-the-art research on Artificial Intelligence in the business context and proposes an agenda for future research. First, by analyzing 404 relevant articles collected through Web of Science and Scopus, this article presents the evolution of research on AI in business over time, highlighting seminal works in the field, and the leading publication venues. Next, using a text-mining approach based on Latent Dirichlet Allocation, latent topics were extracted from the literature and comprehensively analyzed. The findings reveal 18 topics classified into four main clusters: societal impact of AI, organizational impact of AI, AI systems, and AI methodologies. This study then presents several main developmental trends and the resulting challenges, including robots and automated systems, Internet-of-Things and AI integration, law, and ethics, among others. Finally, a research agenda is proposed to guide the directions of future AI research in business addressing the identified trends and challenges.
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Cites
ImageNet classification with deep convolutional neural networks
Krizhevsky, A., Sutskever, I., & Hinton, G. E. (2017) · Communications of the ACM · doi:10.1145/3065386 · Cited by 112,155
LeCun, Y., Bengio, Y., & Hinton, G. (2015) · Nature · doi:10.1038/nature14539 · Cited by 84,126
Preferred Reporting Items for Systematic Reviews and Meta-Analyses: the PRISMA statement
Moher, D., Liberati, A., Tetzlaff, J., Altman, D. G., & for the PRISMA Group (2009) · BMJ · doi:10.1136/bmj.b2535 · Cited by 83,823
Perceived usefulness, perceived ease of use, and user acceptance of information technology
Davis, F. D. (1989) · MIS Quarterly · doi:10.2307/249008 · Cited by 68,483
Preferred Reporting Items for Systematic Reviews and Meta-Analyses: The PRISMA Statement
Moher, D., Liberati, A., Tetzlaff, J., & Altman, D. G. (2009) · Journal of Clinical Epidemiology · doi:10.1016/j.jclinepi.2009.06.005 · Cited by 19,575
Gephi: An Open Source Software for Exploring and Manipulating Networks
Bastian, M., Heymann, S., & Jacomy, M. (2009) · Proceedings of the International AAAI Conference on Web and Social Media · doi:10.1609/icwsm.v3i1.13937 · Cited by 11,675
Barredo Arrieta, A., Díaz-Rodríguez, N., Del Ser, J., Bennetot, A., Tabik, S., Barbado, A., Garcia, S., Gil-Lopez, S., Molina, D., Benjamins, R., et al. (2020) · Information Fusion · doi:10.1016/j.inffus.2019.12.012 · Cited by 10,423 · cited 2×
“However, recent AI techniques framed under the eXplainable AI (XAI) algorithms are paving the way for future transparent applications based on AI (Arrieta, 2020).”
BackgroundMethodI.—COMPUTING MACHINERY AND INTELLIGENCE
TURING, A. M. (1950) · Mind · doi:10.1093/mind/lix.236.433 · Cited by 10,290
Intelligent agents: theory and practice
Wooldridge, M., & Jennings, N. R. (1995) · The Knowledge Engineering Review · doi:10.1017/s0269888900008122 · Cited by 6,810
Griffiths, T. L., & Steyvers, M. (2004) · Proceedings of the National Academy of Sciences · doi:10.1073/pnas.0307752101 · Cited by 6,049
Cited by
Ledro, C., Nosella, A., & Vinelli, A. (2022) · Journal of Business & Industrial Marketing · doi:10.1108/jbim-07-2021-0332 · Cited by 216
The role of artificial intelligence in the procurement process: State of the art and research agenda
Guida, M., Caniato, F., Moretto, A., & Ronchi, S. (2023) · Journal of Purchasing and Supply Management · doi:10.1016/j.pursup.2023.100823 · Cited by 144
Ortega-Bolaños, R., Bernal-Salcedo, J., Germán Ortiz, M., Galeano Sarmiento, J., Ruz, G. A., & Tabares-Soto, R. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-024-10740-3 · Cited by 76
Should Artificial Intelligent Agents be Your Co-author? Arguments in Favour, Informed by ChatGPT
Polonsky, M. J., & Rotman, J. D. (2023) · Australasian Marketing Journal · doi:10.1177/14413582231167882 · Cited by 59
Jeon, Y. A. (2022) · Journal of Business Research · doi:10.1016/j.jbusres.2022.03.028 · Cited by 39
Metadata
- Authors
- Loureiro, S. M. C., Guerreiro, J., & Tussyadiah, I.
- Venue
- Journal of Business Research
- Publisher
- Elsevier BV
- Published
- 2021-05
- Volume
- 129
- Pages
- 911-926
- ISSN
- 0148-2963 (ISSN-L)
- OpenAlex
- W3100936220
- Cited by
- 554
- References
- 24
- FWCI
- 18.28
- Type
- journal article
- Language
- en
- Open access
- closed
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- Loureiro, S. M. C., Guerreiro, J., & Tussyadiah, I. (2021). Artificial intelligence in business: State of the art and future research agenda. Journal of Business Research. https://doi.org/10.1016/j.jbusres.2020.11.001
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
