The DOI matches its Crossref record; 9 of the 9 references it cites were found in a registry or an index.
Virtual Customer Service Agents: Using Social Presence and Personalization to Shape Online Service Encounters
Open accessAbstract
By performing tasks traditionally fulfilled by service personnel and having a humanlike appearance, virtual customer service agents bring classical service elements to the web, which may positively influence customer satisfaction through eliciting social responses and feelings of personalization. This paper sheds light on these dynamics by proposing and testing a model drawing upon the theories of implicit personality, social response, emotional contagion, and social interaction. The model proposes friendliness, expertise, and smile as determinants of social presence, personalization, and online service encounter satisfaction. An empirical study confirms the cross-channel applicability of friendliness and expertise as determinants of social presence and personalization. Overall, the study underlines that integration between technology and personal aspects may lead to more social online service encounters.
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Citations
Cites
The Service Encounter: Diagnosing Favorable and Unfavorable Incidents
Bitner, M. J., Booms, B. H., & Tetreault, M. S. (1990) · Journal of Marketing · doi:10.2307/1252174 · Cited by 4,860
Computer-Mediated Communication: Impersonal, Interpersonal, and Hyperpersonal Interaction
WALTHER, J. B. (1996) · Communication Research · doi:10.1177/009365096023001001 · Cited by 4,798
Relationship Quality in Services Selling: An Interpersonal Influence Perspective
Crosby, L. A., Evans, K. R., & Cowles, D. (1990) · Journal of Marketing · doi:10.1177/002224299005400306 · Cited by 4,763
Machines and Mindlessness: Social Responses to Computers
Nass, C., & Moon, Y. (2000) · Journal of Social Issues · doi:10.1111/0022-4537.00153 · Cited by 4,022 · cited 6×
“Adding more fundamental human characteristics to the humancomputer interaction, like use of language, interactivity, and conversing using social roles, were shown to evoke more social responses (Nass & Moon, 2000).”
BackgroundInfluentialSocial presence as a predictor of satisfaction within a computer‐mediated conferencing environment
Gunawardena, C. N., & Zittle, F. J. (1997) · American Journal of Distance Education · doi:10.1080/08923649709526970 · Cited by 2,005
SERVICE WITH A SMILE: EMOTIONAL CONTAGION IN THE SERVICE ENCOUNTER.
Pugh, S. D. (2001) · Academy of Management Journal · doi:10.2307/3069445 · Cited by 1,393 · cited 1×
“Journal of Computer-Mediated Communication”
The Effects of Personalization and Familiarity on Trust and Adoption of Recommendation Agents1
Komiak, S. Y. X., & Benbasat, I. (2006) · MIS Quarterly · doi:10.2307/25148760 · Cited by 1,374
Qiu, L., & Benbasat, I. (2009) · Journal of Management Information Systems · doi:10.2753/mis0742-1222250405 · Cited by 737
Can a Retail Web Site Be Social?
Wang, L. C., Baker, J., Wagner, J. A., & Wakefield, K. (2007) · Journal of Marketing · doi:10.1509/jmkg.71.3.143 · Cited by 672
Cited by
AI-based chatbots in customer service and their effects on user compliance
Adam, M., Wessel, M., & Benlian, A. (2021) · Electronic Markets · doi:10.1007/s12525-020-00414-7 · Cited by 1,195
“…research has shown that few cues are sufficient for users to identify with computer agents (Xu and Lombard 2017) and virtual service agents (Verhagen et al. 2014), we hypothesize that even verbal ADCs, which are not as directly and easily observable as nonverbal cues like embodiments, can…”
BackgroundAraujo, T. (2018) · Computers in Human Behavior · doi:10.1016/j.chb.2018.03.051 · Cited by 1,156
A Systematic Review of Social Presence: Definition, Antecedents, and Implications
Oh, C. S., Bailenson, J. N., & Welch, G. F. (2018) · Frontiers in Robotics and AI · doi:10.3389/frobt.2018.00114 · Cited by 1,032
“…interpersonal evaluations (e.g., similarity attraction, social penetration theory, social identity theory, preference for consistency, etc.) to technology-mediated contexts to explore their relevance in interpersonal perceptions (e.g., Reeves and Nass, 1996; Jin, 2012; Verhagen et al., 2014).”
BackgroundAn Emerging Theory of Avatar Marketing
Miao, F., Kozlenkova, I. V., Wang, H., Xie, T., & Palmatier, R. W. (2022) · Journal of Marketing · doi:10.1177/0022242921996646 · Cited by 688
Jiang, H., Cheng, Y., Yang, J., & Gao, S. (2022) · Computers in Human Behavior · doi:10.1016/j.chb.2022.107329 · Cited by 362
Bots with Feelings: Should AI Agents Express Positive Emotion in Customer Service?
Han, E., Yin, D., & Zhang, H. (2023) · Information Systems Research · doi:10.1287/isre.2022.1179 · Cited by 307
Nicolescu, L., & Tudorache, M. T. (2022) · Electronics · doi:10.3390/electronics11101579 · Cited by 280
Pizzi, G., Scarpi, D., & Pantano, E. (2021) · Journal of Business Research · doi:10.1016/j.jbusres.2020.11.006 · Cited by 265
Creepiness: Its antecedents and impact on loyalty when interacting with a chatbot
Rajaobelina, L., Prom Tep, S., Arcand, M., & Ricard, L. (2021) · Psychology & Marketing · doi:10.1002/mar.21548 · Cited by 252
Borsci, S., Malizia, A., Schmettow, M., van der Velde, F., Tariverdiyeva, G., Balaji, D., & Chamberlain, A. (2022) · Personal and Ubiquitous Computing · doi:10.1007/s00779-021-01582-9 · Cited by 266
The effect of social presence and chatbot errors on trust
Toader, D.-C., Boca, G., Toader, R., Măcelaru, M., Toader, C., Ighian, D., & Rădulescu, A. T. (2019) · Sustainability · doi:10.3390/su12010256 · Cited by 233
The Impact of Anthropomorphism on Consumers’ Purchase Decision in Chatbot Commerce
Han, M. C. (2021) · Journal of Internet Commerce · doi:10.1080/15332861.2020.1863022 · Cited by 220
Lee, S., Lee, N., & Sah, Y. J. (2020) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2019.1699748 · Cited by 204
Thomaz, F., Salge, C., Karahanna, E., & Hulland, J. (2020) · Journal of the Academy of Marketing Science · doi:10.1007/s11747-019-00704-3 · Cited by 204
“In examining specific anthropomorphic features, researchers have shown that certain personality characteristics such as friendliness and expertise (Verhagen et al., 2014) as well as embodiment and communication style (Qiu & Benbasat, 2009) influence perceptions of social presence.”
BackgroundTexting with Humanlike Conversational Agents: Designing for Anthropomorphism
Seeger, A.-M., University of Mannheim, G., Pfeiffer, J., Justus Liebig University Giessen, G., Heinzl, A., & University of Mannheim, G. (2021) · Journal of the Association for Information Systems · doi:10.17705/1jais.00685 · Cited by 200
Janson, A. (2023) · Computers in Human Behavior · doi:10.1016/j.chb.2023.107954 · Cited by 187
Anthropomorphism in AI-enabled technology: A literature review
Li, M., & Suh, A. (2022) · Electronic Markets · doi:10.1007/s12525-022-00591-7 · Cited by 181
Privacy Concerns in Chatbot Interactions
Ischen, C., Araujo, T., Voorveld, H., van Noort, G., & Smit, E. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-39540-7_3 · Cited by 152
Sweet escape: The role of empathy in social media engagement with human versus virtual influencers
Mirowska, A., & Arsenyan, J. (2023) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2023.103008 · Cited by 154
Chatbot ads with a human touch: A test of anthropomorphism, interactivity, and narrativity
Sun, Y., Chen, J., & Sundar, S. S. (2024) · Journal of Business Research · doi:10.1016/j.jbusres.2023.114403 · Cited by 115
Feine, J., Gnewuch, U., Morana, S., & Maedche, A. (2020) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-39540-7_6 · Cited by 99
Ahmed, S., & Aziz, N. A. (2025) · International Journal of Human–Computer Interaction · doi:10.1080/10447318.2024.2400395 · Cited by 67
User Perception of Text-Based Chatbot Personality
Ruane, E., Farrell, S., & Ventresque, A. (2021) · Lecture Notes in Computer Science · doi:10.1007/978-3-030-68288-0_3 · Cited by 57
Intention to adopt services by AI avatar: A protection motivation theory perspective
Park, J., Yun, J., & Chang, W. (2024) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2024.103929 · Cited by 51
Murtaza, Z., Sharma, I., & Carbonell, P. (2024) · Technological Forecasting and Social Change · doi:10.1016/j.techfore.2024.123806 · Cited by 46
Cheng, Z., Fan, W., Shao, B., Jia, W., & Zhang, Y. (2024) · Journal of Retailing and Consumer Services · doi:10.1016/j.jretconser.2023.103585 · Cited by 45
Pathak, A., & Bansal, V. (2024) · Computers in Human Behavior: Artificial Humans · doi:10.1016/j.chbah.2024.100094 · Cited by 40
University of Göttingen, Diederich, S., Lembcke, T.-B., University of Göttingen, Brendel, A. B., Dresden, T., Kolbe, L. M., & University of Göttingen (2021) · AIS Transactions on Human-Computer Interaction · doi:10.17705/1thci.00143 · Cited by 40
Potdevin, D., Clavel, C., & Sabouret, N. (2021) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2021.102612 · Cited by 28
Be Friendly, Not Friends: How LLM Sycophancy Shapes User Trust
Sun, Y., & Wang, T. (2026) · Proceedings of the 2026 CHI Conference on Human Factors in Computing Systems · doi:10.1145/3772318.3791079 · Cited by 16
“Our findings also confirmed that complimentary demeanor enhances perceived social presence, leading users to treat LLM agents as socially aware entities [87].”
Cybersex with human- and machine-cued partners: Gratifications, shortcomings, and tensions.
Banks, J., & Van Ouytsel, J. (2020) · Technology, Mind, and Behavior · doi:10.1037/tmb0000008 · Cited by 14
van der Goot, M. J., Koubayová, N., & van Reijmersdal, E. A. (2024) · AI & SOCIETY · doi:10.1007/s00146-023-01818-7 · Cited by 14
Gnewuch, U., & Reinkemeier, F. (2026) · MIS Quarterly · doi:10.25300/misq/2025/18742 · Cited by 5
Metadata
- Authors
- Verhagen, T., van Nes, J., Feldberg, F., & van Dolen, W.
- Venue
- Journal of Computer-Mediated Communication
- Publisher
- Oxford University Press (OUP)
- Published
- 2014-02-17
- Volume
- 19
- Issue
- 3
- Pages
- 529-545
- ISSN
- 1083-6101 (ISSN-L)
- OpenAlex
- W2091954440
- Cited by
- 403
- References
- 9
- FWCI
- 6.93
- Type
- journal article
- Language
- en
- Open access
- bronze
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