The DOI matches its Crossref record; 69 of the 69 references it cites were found in a registry or an index.
How chatbots' social presence communication enhances consumer engagement: the mediating role of parasocial interaction and dialogue
Abstract
Purpose: This study presents one of the earliest empirical investigations on how brand chatbots' anthropomorphic design and social presence communication strategies may improve consumer evaluation outcomes via the mediators of parasocial interaction and perceived dialogue. Design/methodology/approach: This study employs a 2 (high vs. low social presence communication) by 2 (anthropomorphic vs. non-anthropomorphic bot profile) between-subject experimental design to evaluate how chatbots' high social presence communication and anthropomorphic profile design may enhance perceptions of parasocial interactions and dialogue with the chatbot, which in turn drive user engagement, interaction satisfaction and attitude toward the represented brand. Findings: The influences of chatbots' high social presence communication on consumer engagement outcomes are mediated by perceived parasocial interaction and dialogue. Additionally, chatbots' anthropomorphic profile design can boost the positive effects of social presence communication via the psychological mediators. Originality/value: This study advances the interactive marketing literature by focusing on an emerging interactive technology, chatbots. Additionally, distinct from prior chatbot studies that focused on the utilitarian use of chatbots for online customer support, this study not only examines which factors of chatbot communication and profile design may drive chatbot effectiveness but also examines the mechanism underlying the messaging and design effects on consumer engagement. The findings highlight the mediating role of interpersonal factors of parasocial interaction and perceived dialogue.
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“The USA is also the most represented country among the top 10 most cited publications, including notable works by Go and Sundar(2019), Tsai etal.(2021), and Pentina etal.(2023).”
From Consumer Response to Anthropomorphism of Text‐Based AI Chatbots: A Systematic Literature Review and Future Research Directions (2025) · cites this paper as Tsai et al. 2021
In the citing paper
“Mediation effects proposed in H5a–H8b are consistent with Tsai et al. [65], Xu et al. [71], Labrecque [32], and Huang and Gursoy [24], who demonstrate the roles of para-social interaction and emotional support as key relational bridges in AI-user engagement.”
From Effects of attractions and social attributes on peoples' usage intention and media dependence towards chatbot: The mediating role of parasocial interaction and emotional support (2025) · cites this paper as Tsai et al. [65]
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Metadata
- Authors
- Tsai, W.-H. S., Liu, Y., & Chuan, C.-H.
- Affiliations
- University of Miami (US)
- Florida International University (US)
- Venue
- Journal of Research in Interactive Marketing
- Publisher
- Emerald
- Published
- 2021-06-18
- Volume
- 15
- Issue
- 3
- Pages
- 460-482
- ISSN
- 2040-7122 (ISSN-L)
- OpenAlex
- W3174303984
- Cited by
- 394
- References
- 69
- Citations by year
- 2026: 65 · 2025: 120 · 2024: 118 · 2023: 63 · 2022: 22 · 2021: 6
- FWCI
- 18.47
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Journal of Research in Interactive Marketing (publishedVersion)
- Topics
- Social Sciences › Business, Management and Accounting › Marketing › AI in Service Interactions
- Social Sciences › Business, Management and Accounting › Marketing › Digital Marketing and Social Media
- Social Sciences › Psychology › Social Psychology › Social Robot Interaction and HRI
- Keywords
- chatbots, anthropomorphic design, parasocial interaction, customer engagement, interaction satisfaction, brand attitude, interactive marketing
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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