The DOI matches its Crossref record; 4 of the 4 references it cites were found in a registry or an index.
Silicon sycophants: the effects of computers that flatter
Open accessAbstract
A laboratory experiment examines the claims that (1) humans are susceptible to flattery from computers and (2) the effects of flattery from computers are the same as the effects of flattery from humans. In a cooperative task with a computer, subjects (N=41) received one of three types of feedback from a computer: “sincere praise”, “flattery” (insincere praise) or “generic feedback”. Compared to generic-feedback subjects, flattery subjects reported more positive affect, better performance, more positive evaluations of the interaction and more positive regard for the computer, even though subjects knew that the flattery from the computer was simply noncontingent feedback. Subjects in the sincere praise condition responded similarly to those in the flattery condition. The study concludes that the effects of flattery from a computer can produce the same general effects as flattery from humans, as described in the psychology literature. These findings may suggest significant implications for the design of interactive technologies.
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Cites
Grice, H. P. (1975) · Speech Acts · doi:10.1163/9789004368811_003 · Cited by 23,221
Illusion and well‐being: A social psychological perspective on mental health
Taylor, S. E., & Brown, J. D. (1988) · Psychological Bulletin · doi:10.1037/0033-2909.103.2.193 · Cited by 7,814
Are Machines Gender Neutral? Gender‐Stereotypic Responses to Computers With Voices
Nass, C., Moon, Y., & Green, N. (1997) · Journal of Applied Social Psychology · doi:10.1111/j.1559-1816.1997.tb00275.x · Cited by 670
Self-citationAnthropomorphism, agency, and ethopoeia: computers as social actors
Nass, C., Steuer, J., Tauber, E., & Reeder, H. (1993) · INTERACT '93 and CHI '93 conference companion on Human factors in computing systems - CHI '93 · doi:10.1145/259964.260137 · Cited by 162
Self-citation
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Tourangeau, R., & Yan, T. (2007) · Psychological Bulletin · doi:10.1037/0033-2909.133.5.859 · Cited by 3,057
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Oinas-Kukkonen, H., & Harjumaa, M. (2009) · Communications of the Association for Information Systems · doi:10.17705/1cais.02428 · Cited by 1,663
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Fogg, B. J. (2002) · Ubiquity · doi:10.1145/764008.763957 · Cited by 1,554
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Adam, M., Wessel, M., & Benlian, A. (2021) · Electronic Markets · doi:10.1007/s12525-020-00414-7 · Cited by 1,195
“1995), and flattery effects (Fogg and Nass 1997) are also present in HCI.”
BackgroundHassanein, K., & Head, M. (2007) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2006.11.018 · Cited by 986
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Gambino, A., Fox, J., & Ratan, R. (2020) · Human-Machine Communication · doi:10.30658/hmc.1.5 · Cited by 626
“Participants prefer computers that flatter them more than those that do not (Fogg & Nass, 1997) and evaluate a computer more positively if the computer requests the evaluation rather than providing an evaluation separately on paper (Nass et al., 1994).”
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Madhavan, P., & Wiegmann, D. A. (2007) · Theoretical Issues in Ergonomics Science · doi:10.1080/14639220500337708 · Cited by 577
Chattaraman, V., Kwon, W.-S., Gilbert, J. E., & Ross, K. (2019) · Computers in Human Behavior · doi:10.1016/j.chb.2018.08.048 · Cited by 510
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Liu, B., & Sundar, S. S. (2018) · Cyberpsychology, Behavior, and Social Networking · doi:10.1089/cyber.2018.0110 · Cited by 447
Persuasive computers: perspectives and research directions
Fogg, B. (1998) · Proceedings of the SIGCHI conference on Human factors in computing systems - CHI '98 · doi:10.1145/274644.274677 · Cited by 434
Self-citation“It doesn’t matter what you are!” Explaining social effects of agents and avatars
von der Pütten, A. M., Krämer, N. C., Gratch, J., & Kang, S.-H. (2010) · Computers in Human Behavior · doi:10.1016/j.chb.2010.06.012 · Cited by 389
The influence of empathy in human-robot relations
Leite, I., Pereira, A., Mascarenhas, S., Martinho, C., Prada, R., & Paiva, A. (2013) · International Journal of Human-Computer Studies · doi:10.1016/j.ijhcs.2012.09.005 · Cited by 344
MOON, Y., & NASS, C. (1996) · Communication Research · doi:10.1177/009365096023006002 · Cited by 291
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Zhou, M. X., Mark, G., Li, J., & Yang, H. (2019) · ACM Transactions on Interactive Intelligent Systems · doi:10.1145/3232077 · Cited by 176
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“Prior work has shown that expressions of praise, affirmation, and flattery often promote positive impressions [21].”
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Richards, R., Coss, C., & Quinn, J. (2017) · Lecture Notes in Computer Science · doi:10.1007/978-3-319-57738-8_9 · Cited by 14
Metadata
- Authors
- FOGG, B. J., & NASS, C.
- Venue
- International Journal of Human-Computer Studies
- Publisher
- Elsevier BV
- Published
- 1997-05
- Volume
- 46
- Issue
- 5
- Pages
- 551-561
- ISSN
- 1071-5819 (ISSN-L)
- OpenAlex
- W2009970621
- Cited by
- 367
- References
- 4
- FWCI
- 12.43
- Type
- journal article
- Language
- en
- Open access
- hybrid
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