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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) 
Loureiro, S. M. C., Ali, F. & Ali, M. (2024). Symmetric and asymmetric modeling to understand drivers and consequences of hotel chatbot engagement. International Journal of Human–Computer Interaction. 40 (3), 782-794
Exportar Referência (IEEE) 
S. M. Loureiro et al.,  "Symmetric and asymmetric modeling to understand drivers and consequences of hotel chatbot engagement", in Int. Journal of Human–Computer Interaction, vol. 40, no. 3, pp. 782-794, 2024
Exportar BibTeX 
@article{loureiro2024_1732201995049,
	author = "Loureiro, S. M. C. and Ali, F. and Ali, M.",
	title = "Symmetric and asymmetric modeling to understand drivers and consequences of hotel chatbot engagement",
	journal = "International Journal of Human–Computer Interaction",
	year = "2024",
	volume = "40",
	number = "3",
	doi = "10.1080/10447318.2022.2124346",
	pages = "782-794",
	url = "https://www.tandfonline.com/doi/full/10.1080/10447318.2022.2124346"
}
Exportar RIS 
TY  - JOUR
TI  - Symmetric and asymmetric modeling to understand drivers and consequences of hotel chatbot engagement
T2  - International Journal of Human–Computer Interaction
VL  - 40
IS  - 3
AU  - Loureiro, S. M. C.
AU  - Ali, F.
AU  - Ali, M.
PY  - 2024
SP  - 782-794
SN  - 1044-7318
DO  - 10.1080/10447318.2022.2124346
UR  - https://www.tandfonline.com/doi/full/10.1080/10447318.2022.2124346
AB  - Drawing on action identification and complexity theories, this study explores lifestyle congruency and chatbot identification as drivers of engagement, leading to chatbot advocacy. Data collected from 304 individuals were assessed symmetrically through PLS-SEM. Moreover, configuration causal paths were assessed through fsQCA. Findings reveal the role of chatbot identification in the relationship between lifestyle congruency and customer chatbot engagement. Lifestyle congruency and chatbot identification significantly influence all the dimensions of chatbot engagement. Nevertheless, only customer referrals and customer influence lead to chatbot advocacy. Findings from fsQCA reveal six and seven different paths, leading to high and low levels of chatbot advocacy, respectively. This is one of the first studies to apply both symmetrical and asymmetrical analysis to examine different casual paths to chatbot advocacy. 
ER  -