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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) 
Ashfaq, M., Yun, J., Yu, S. & Loureiro, S. M. C. (2020). I, Chatbot: modeling the determinants of users’ satisfaction and continuance intention of AI-powered service agents. Telematics and Informatics. 54
Exportar Referência (IEEE) 
M. Ashfaq et al.,  "I, Chatbot: modeling the determinants of users’ satisfaction and continuance intention of AI-powered service agents", in Telematics and Informatics, vol. 54, 2020
Exportar BibTeX 
@article{ashfaq2020_1713528004479,
	author = "Ashfaq, M. and Yun, J. and Yu, S. and Loureiro, S. M. C.",
	title = "I, Chatbot: modeling the determinants of users’ satisfaction and continuance intention of AI-powered service agents",
	journal = "Telematics and Informatics",
	year = "2020",
	volume = "54",
	number = "",
	doi = "10.1016/j.tele.2020.101473",
	url = "https://www.sciencedirect.com/journal/telematics-and-informatics"
}
Exportar RIS 
TY  - JOUR
TI  - I, Chatbot: modeling the determinants of users’ satisfaction and continuance intention of AI-powered service agents
T2  - Telematics and Informatics
VL  - 54
AU  - Ashfaq, M.
AU  - Yun, J.
AU  - Yu, S.
AU  - Loureiro, S. M. C.
PY  - 2020
SN  - 0736-5853
DO  - 10.1016/j.tele.2020.101473
UR  - https://www.sciencedirect.com/journal/telematics-and-informatics
AB  - Chatbots are mainly text-based conversational agents that simulate conversations with users. This study aims to investigate drivers of users’ satisfaction and continuance intention toward chatbot-based customer service. We propose an analytical framework combining the expectation-confirmation model (ECM), information system success (ISS) model, TAM, and the need for interaction with a service employee (NFI-SE). Analysis of data collected from 370 actual chatbot users reveals that information quality (IQ) and service quality (SQ) positively influence consumers’ satisfaction, and that perceived enjoyment (PE), perceived usefulness (PU), and perceived ease of use (PEOU) are significant predictors of continuance intention (CI). The need for interaction with an employee moderates the effects of PEOU and PU on satisfaction. The findings also revealed that satisfaction with chatbot e-service is a strong determinant and predictor of users’ CI toward chatbots. Thus, chatbots should enhance their information and service quality to increase users’ satisfaction. The findings imply that digital technologies services, such as chatbots, could be combined with human service employees to satisfy digital users.
ER  -