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Cited by 987

The DOI matches its Crossref record; 25 of the 25 references it cites were found in a registry or an index.

I, Chatbot: Modeling the determinants of users’ satisfaction and continuance intention of AI-powered service agents

Ashfaq, Muhammad, Yun, Jiang, Yu, Shubin, Loureiro, Sandra Maria Correia·2020·Telematics and Informatics
DOI 10.1016/j.tele.2020.101473
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Abstract

Abstract 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.

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Metadata

Authors
Ashfaq, M., Yun, J., Yu, S., & Loureiro, S. M. C.
Venue
Telematics and Informatics
Publisher
Elsevier BV
Published
2020-11
Volume
54
Pages
101473
ISSN
0736-5853 (ISSN-L)
DOI
10.1016/j.tele.2020.101473
OpenAlex
W3044483028
Cited by
987
References
25
FWCI
30.78
Type
journal article
Language
en
Open access
closed

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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.