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A publicação pode ser exportada nos seguintes formatos: referência da APA (American Psychological Association), referência do IEEE (Institute of Electrical and Electronics Engineers), BibTeX e RIS.

Exportar Referência (APA) 
Matias, S., Dias, Á. L. & Pereira, L. (2026). Artificial intelligence adoption in event logistics: Barriers, critical success factors, and expert consensus from a Delphi study. Logistics. 10 (2)
Exportar Referência (IEEE) 
S. Matias et al.,  "Artificial intelligence adoption in event logistics: Barriers, critical success factors, and expert consensus from a Delphi study", in Logistics, vol. 10, no. 2, 2026
Exportar BibTeX 
@article{matias2026_1773834417626,
	author = "Matias, S. and Dias, Á. L. and Pereira, L.",
	title = "Artificial intelligence adoption in event logistics: Barriers, critical success factors, and expert consensus from a Delphi study",
	journal = "Logistics",
	year = "2026",
	volume = "10",
	number = "2",
	doi = "10.3390/logistics10020048",
	url = "https://www.mdpi.com/journal/logistics"
}
Exportar RIS 
TY  - JOUR
TI  - Artificial intelligence adoption in event logistics: Barriers, critical success factors, and expert consensus from a Delphi study
T2  - Logistics
VL  - 10
IS  - 2
AU  - Matias, S.
AU  - Dias, Á. L.
AU  - Pereira, L.
PY  - 2026
SN  - 2305-6290
DO  - 10.3390/logistics10020048
UR  - https://www.mdpi.com/journal/logistics
AB  - Background: Artificial Intelligence (AI) is increasingly adopted across logistics and service operations, yet limited research explains how it supports back-end event logistics or what factors enable or hinder its implementation. This study investigates how AI can be applied across event logistics processes and identifies the key barriers and critical success factors shaping its adoption. Methods: A sequential exploratory qualitative design was employed. First, semi-structured interviews with experienced event professionals generated context-specific insights. These findings informed a two-round Delphi study with 10 experts, where items were prioritised and consensus assessed using Kendall’s coefficient of concordance (W) and chi-square tests. Results: The results indicate that AI delivers the greatest value in pre-event planning activities, particularly scheduling and supplier coordination. Resistance to change and the lack of industry-specific AI tools emerged as the main adoption barriers, while technological infrastructure, system integration, and change management were identified as critical success factors. Conclusions: The study provides practical guidance for event organisers and technology providers by highlighting where AI investments are most likely to generate operational benefits and how organisational readiness can be strengthened. It also underscores the need for improved sustainability-focused tools and better data practices.
ER  -