The DOI matches its Crossref record; 8 of the 8 references it cites were found in a registry or an index.
Customer–brand relationship in the era of artificial intelligence: understanding the role of chatbot marketing efforts
Abstract
Purpose: This study aims to explore the role of artificial intelligence (AI)-powered chatbot marketing efforts (CMEs) in the establishment of relationships between brands and their customers, extending the link between relationship marketing and online consumer behavioral intentions. Design/methodology/approach: Data are collected from 1,072 customers in the USA, who used chatbot marketing activities from any of 30 brands leading their industries in messaging innovation. Structural equation modeling is used for data analysis. Findings: Results show that interaction, information, accessibility, entertainment and customization are important CMEs components. CMEs have significant direct effects on the quality of communication with chatbot agents and indirectly affect customer–brand relationships (CBR) and customer response. In addition, the findings demonstrate that CBR mediates the association between communication quality and customer response. Originality/value: Implications of this study can enable practitioners to understand the effects of AI on user experiences and provide a guide for the development of CMEs strategies and relationship building.
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Cites
Hu, L., & Bentler, P. M. (1999) · Structural Equation Modeling A Multidisciplinary Journal · doi:10.1080/10705519909540118 · Cited by 108,788
Consumers and Their Brands: Developing Relationship Theory in Consumer Research
Fournier, S. (1998) · Journal of Consumer Research · doi:10.1086/209515 · Cited by 7,542
Artificial intelligence in service
Huang, M.-H., & Rust, R. T. (2018) · Journal of Service Research · doi:10.1177/1094670517752459 · Cited by 3,978
The future of social media in marketing
Appel, G., Grewal, L., Hadi, R., & Stephen, A. T. (2020) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-019-00695-1 · Cited by 1,893
Brandtzaeg, P. B., & Følstad, A. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-70284-1_30 · Cited by 788
Cheng, Y., & Jiang, H. (2020) · Journal of Broadcasting & Electronic Media · doi:10.1080/08838151.2020.1834296 · Cited by 469
Self-citationPredicting consumer responses to a chatbot on facebook
Zarouali, B., Van den Broeck, E., Walrave, M., & Poels, K. (2018) · Cyberpsychology, Behavior, and Social Networking · doi:10.1089/cyber.2017.0518 · Cited by 229
Cheng, Y., Liang, J., & Leung, L. (2015) · New Media & Society · doi:10.1177/1461444814521362 · Cited by 129
Self-citation
Cited by
User trust in artificial intelligence: A comprehensive conceptual framework
Yang, R., & Wibowo, S. (2022) · Electronic Markets · doi:10.1007/s12525-022-00592-6 · Cited by 170
Rahman, M. S., Bag, S., Hossain, M. A., Abdel Fattah, F. A. M., Gani, M. O., & Rana, N. P. (2023) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2023.103273 · Cited by 170
The Moral Psychology of Artificial Intelligence
Bonnefon, J.-F., Rahwan, I., & Shariff, A. (2024) · Annual Review of Psychology · doi:10.1146/annurev-psych-030123-113559 · Cited by 151
Hassan, N., Abdelraouf, M., & El-Shihy, D. (2025) · Future Business Journal · doi:10.1186/s43093-025-00476-z · Cited by 96
Lefkeli, D., Karataş, M., & Gürhan-Canli, Z. (2024) · International Journal of Research in Marketing · doi:10.1016/j.ijresmar.2023.08.011 · Cited by 62
Metadata
- Authors
- Cheng, Y., & Jiang, H.
- Venue
- Journal of Product & Brand Management
- Publisher
- Emerald
- Published
- 2021-06-03
- Volume
- 31
- Issue
- 2
- Pages
- 252-264
- ISSN
- 1061-0421 (ISSN-L)
- OpenAlex
- W3168740456
- Cited by
- 393
- References
- 8
- FWCI
- 17.29
- Type
- journal article
- Language
- en
- Open access
- closed
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