research-article

Humanizing chatbots: : The effects of visual, identity and conversational cues on humanness perceptions

Authors:

S. Shyam SundarAuthors Info & Claims

Volume 97, Issue C

Pages 304 - 316

https://doi.org/10.1016/j.chb.2019.01.020

Published: 01 August 2019 Publication History

Abstract

Chatbots are replacing human agents in a number of domains, from online tutoring to customer-service to even cognitive therapy. But, they are often machine-like in their interactions. What can we do to humanize chatbots? Should they necessarily be driven by human operators for them to be considered human? Or, will an anthropomorphic visual cue on the interface and/or a high-level of contingent message exchanges provide humanness to automated chatbots? We explored these questions with a 2 (anthropomorphic visual cues: high vs. low anthropomorphism) × 2 (message interactivity: high vs. low message interactivity) × 2 (identity cue: chat-bot vs. human) between-subjects experiment (N = 141) in which participants interacted with a chat agent on an e-commerce site about choosing a digital camera to purchase. Our findings show that a high level of message interactivity compensates for the impersonal nature of a chatbot that is low on anthropomorphic visual cues. Moreover, identifying the agent as human raises user expectations for interactivity. Theoretical as well as practical implications of these findings are discussed.

Highlights

•

A compensation effect of high anthropomorphic visual cues on low message interactivity.

•

Another compensation effect of high message interactivity on low anthropomorphic visual cues.

•

An expectancy violation effects when the identity cue is combined with message interactivity.

•

Revealing the identity of the machine can capitalize on expectations.

References

[1]

B.E. Ashforth, R.H. Humphrey, The ubiquity and potency of labeling in organizations, Organization Science 8 (1) (1997) 43–58.

[2]

A. Baylor, Y. Kim, The role of gender and ethnicity in pedagogical agent perception, World conference on E-learning in corporate, government, healthcare, and higher education, Vol. 2003, 2003, pp. 1503–1506. No. 1.

[3]

S. Bellur, S.S. Sundar, Talking health with a machine: How does message interactivity affect attitudes and cognitions?, Human Communication Research 43 (1) (2017) 25–43,.

[4]

G. Bente, S. Rüggenberg, N.C. Krämer, F. Eschenburg, Avatar-mediated networking: Increasing social presence and interpersonal trust in net‐based collaborations, Human Communication Research 34 (2) (2008) 287–318,.

[5]

F. Biocca, C. Harms, J. Burgoon, Toward a more robust theory and measure of social presence: Review and suggested criteria, Presence 12 (5) (2003) 456–480,.

Digital Library

[6]

M. Buhrmester, T. Kwang, S.D. Gosling, Amazon's Mechanical Turk a new source of inexpensive, yet high-quality, data?, Perspectives on Psychological Science 6 (1) (2011) 3–5,.

[7]

J.K. Burgoon, J.B. Walther, Nonverbal expectancies and the evaluative consequences of violations, Human Communication Research 17 (2) (1990) 232–265,.

[8]

J.N. Cappella, J.O. Greene, A discrepancy‐arousal explanation of mutual influence in expressive behavior for adult and infant‐adult interaction, Communication Monographs 49 (2) (1982) 89–114,.

[9]

D. Cyr, M. Head, H. Larios, B. Pan, Exploring human images in website design: A multi-method approach, MIS Quarterly 33 (3) (2009) 539–566,.

[10]

D. Gefen, D.W. Straub, Managing user trust in B2C e-services, e-Service Journal 2 (2) (2003) 7–24,.

[11]

S.A. Gelman, G.D. Heyman, Carrot-eaters and creature-believers: The effects of lexicalization on children's inferences about social categories, Psychological Science 10 (6) (1999) 489–493,.

[12]

S. Ghose, J.J. Barua, Toward the implementation of a topic specific dialogue based natural language chatbot as an undergraduate advisor, in: Informatics, electronics & vision (ICIEV), 2013 international conference on, IEEE, 2013, May, pp. 1–5.

[13]

G.R. Goethals, R.E. Nelson, Similarity in the influence process: The belief-value distinction, Journal of Personality and Social Psychology 25 (1) (1973) 117,.

[14]

L. Gong, C. Nass, When a talking‐face computer agent is half‐human and half-humanoid: Human identity and consistency preference, Human Communication Research 33 (2) (2007) 163–193,.

[15]

J.K. Goodman, C.E. Cryder, A. Cheema, Data collection in a flat world: The strengths and weaknesses of Mechanical Turk samples, Journal of Behavioral Decision Making 26 (3) (2013) 213–224,.

[16]

S.D. Gosling, S. Vazire, S. Srivastava, O.P. John, Should we trust web-based studies? A comparative analysis of six preconceptions about internet questionnaires, American Psychologist 59 (2) (2004) 93,.

[17]

A.F. Hayes, An introduction to mediation, moderation, and conditional process analysis, The Guilford Press, New York, 2013.

[18]

S. Hrastinski, Asynchronous and synchronous e-learning, Educause Quarterly 31 (4) (2008) 51–55.

[19]

Y. Hu, S.S. Sundar, Effects of online health sources on credibility and behavioral intentions, Communication Research 37 (1) (2010) 105–132,.

[20]

D. Kahneman, D.T. Miller, Norm theory: Comparing reality to its alternatives, Psychological Review 93 (2) (1986) 136–153,.

[21]

M.L. Kent, M. Taylor, Toward a dialogic theory of public relations, Public Relations Review 28 (1) (2002) 21–37,.

[22]

J. Kim, Balancing uniqueness and assimilation in computer-mediated groups, Computers in Human Behavior 26 (4) (2010) 778–784,.

Digital Library

[23]

Y. Kim, S.S. Sundar, Anthropomorphism of computers: Is it mindful or mindless?, Computers in Human Behavior 28 (1) (2012) 241–250,.

Digital Library

[24]

T. Koda, Agents with faces: A study on the effects of personification of software agents, (Unpublished master thesis) Massachusetts Institute of Technology, Boston, MA, USA, 1996.

[25]

Y.J. Koh, S.S. Sundar, Heuristic versus systematic processing of specialist versus generalist sources in online media, Human Communication Research 36 (2) (2010) 103–124,.

[26]

E.-J. Lee, S.Y. Oh, Effects of visual cues on social perceptions and self-categorization in computer-mediated communication, in: S.S. Sundar (Ed.), The handbook of the psychology of communication technology, Wiley Blackwell, Malden, MA, 2015, pp. 115–136.

[27]

W. Mason, S. Suri, Conducting behavioral research on Amazon's Mechanical Turk, Behavior Research Methods 44 (1) (2012) 1–23.

[28]

W.A. Mason, D.J. Watts, Financial incentives and the performance of crowds, in: Proceedings of the ACM SIGKDD workshop on human computation, ACM, New York, 2009, pp. 77–85.

[29]

K.L. Nowak, The influence of anthropomorphism and agency on social judgment in online chat environments, Journal of Computer-Mediated Communication 9 (2) (2004),.

[30]

K.L. Nowak, C. Rauh, The influence of the avatar on online perceptions of anthropomorphism, androgyny, credibility, homophily, and attraction, Journal of Computer-Mediated Communication 11 (1) (2005) 153–178,.

[31]

M. Palmer, Interpersonal communication and virtual reality: Mediating interpersonal relationships, in: F. Biocca, M. Levy (Eds.), Communication in the age of virtual reality, Lawrence Erlbaum Associates, Mahwah, NJ, 1995, pp. 277–299.

[32]

S. Rafaeli, Interactivity: From new media to communication, in: R.P. Hawkins, J.M. Wiemann, S. Pingree (Eds.), Advancing communication science: Merging mass and interpersonal processes, Sage, Newbury Park, CA, 1988, pp. 110–134.

[33]

R.E. Rice, Mediated group communication, in: R.E. Rice (Ed.), The new media: Communication, research, and technology, Sage, Beverly Hills, CA, 1984, pp. 129–154.

[34]

K.A. Rocca, J.C. McCroskey, The interrelationship of student ratings of instructors' immediacy, verbal aggressiveness, homophily, and interpersonal attraction, Communication Education 48 (4) (1999) 308–316,.

[35]

E.M. Rogers, D.K. Bhowmik, Homophily-heterophily: Relational concepts for communication research, Public Opinion Quarterly 34 (4) (1970) 523–538,.

[36]

T. Seltzer, M.A. Mitrook, The dialogic potential of weblogs in relationship building, Public Relations Review 33 (2) (2007) 227–229,.

[37]

J. Short, E. Williams, B. Christie, The social psychology of telecommunications, John Wiley & Sons, New York, NY, 1976.

[38]

S.S. Sundar, The MAIN model: A heuristic approach to understanding technology effects on credibility, in: M.J. Metzger, A.J. Flanagin (Eds.), Digital, media, youth, and credibility, The MIT Press, Cambridge, MA, 2008, pp. 72–100,.

[39]

S.S. Sundar, Media effects 2.0: Social and psychological effects of communication technologies, in: R.L. Nabi, M.B. Oliver (Eds.), The SAGE handbook of media processes and effects, Sage Publications, Thousand Oaks, CA, 2009, pp. 545–560.

[40]

S.S. Sundar, S. Bellur, J. Oh, H. Jia, H.S. Kim, Theoretical importance of contingency in human-computer interaction: Effects of message interactivity on user engagement, Communication Research 43 (5) (2016) 595–625,.

[41]

S.S. Sundar, E. Go, H.S. Kim, B. Zhang, Communicating art, virtually! Psychological effects of technological affordances in a virtual museum, International Journal of Human-Computer Interaction 31 (2015) 385–401,.

[42]

S.S. Sundar, A. Oeldorf-Hirsch, J.F. Nussbaum, R.A. Behr, Retirees on Facebook: Can online social networking enhance their health and wellness?, in: Proceedings of the 2011 annual conference extended abstracts on human factors in computing systems (CHI EA'11), 2011, pp. 2287–2292.

Digital Library

[43]

S.S. Sundar, Q. Xu, S. Bellur, Designing interactivity in media interfaces: A communications perspective, in: Proceedings of the SIGCHI conference on human factors in computing systems, ACM, 2010, April, pp. 2247–2256.

[44]

S. Suri, D.J. Watts, Cooperation and contagion in Web-based, networked public goods experiments, PLoS One 6 (3) (2011) e16836.

[45]

A. Tversky, D. Kahneman, Judgment under uncertainty: Heuristics and biases, Science 185 (4157) (1974) 1124–1131.

[46]

A. Wexelblat, Don't make that face: A report on anthropomorphizing an interface, Intelligent Environments 173 (179) (1998) 98-02.

Cited By

Mai DVan Hanh N(2024)Whether English Proficiency and English Self-Efficacy Influence the Credibility of ChatGPT-Generated English Content of EMI StudentsInternational Journal of Computer-Assisted Language Learning and Teaching10.4018/IJCALLT.34997214:1(1-21)Online publication date: 11-Mar-2024
https://dl.acm.org/doi/10.4018/IJCALLT.349972
Gnewuch UMorana SHinz OKellner RMaedche A(2024)More Than a Bot? The Impact of Disclosing Human Involvement on Customer Interactions with Hybrid Service AgentsInformation Systems Research10.1287/isre.2022.015235:3(936-955)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1287/isre.2022.0152
Guo Y(2024)A study on the mechanism of the impact of anthropomorphic features of virtual chatbots on user experienceProceedings of the 2024 International Conference on Digital Society and Artificial Intelligence10.1145/3677892.3677897(32-37)Online publication date: 24-May-2024
https://dl.acm.org/doi/10.1145/3677892.3677897
Show More Cited By

Index Terms

Humanizing chatbots: The effects of visual, identity and conversational cues on humanness perceptions
1. Human-centered computing
  1. Human computer interaction (HCI)

Index terms have been assigned to the content through auto-classification.

Recommendations

The effects of source cues on online news perception

Among various interface cues, expertise, identity, and bandwagon cues have been consistently found to have significant effects on media users' perceptions of online news content. To examine the effects of these three types of heuristic cues in the ...
Estimating the Impact of “Humanizing” Customer Service Chatbots
In this work, we investigate how applying human-like characteristics to customer service chatbots can influence retail outcomes. This is an important managerial question as creating effective chatbot experiences through messaging platforms has proven ...
We study the impacts of “humanizing” artificial intelligence (AI)-enabled autonomous customer service agents (chatbots). Implementing a field experiment in collaboration with a dual channel clothing retailer based in the United States, we automate a used ...
User Engagement with Chatbots: A Discursive Psychology Approach
CUI '20: Proceedings of the 2nd Conference on Conversational User Interfaces

Conversational agents have transcended into multiple industries with increased ability for user engagement in intelligent conversation. Conversations with chatbots are different from interpersonal communication in terms of turn-taking, intentions, and ...

Comments

Information & Contributors

Information

Published In

cover image Computers in Human Behavior

Computers in Human Behavior Volume 97, Issue C

Aug 2019

317 pages

ISSN:0747-5632

Issue’s Table of Contents

Elsevier Ltd.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 August 2019

Author Tags

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

96
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 14 Oct 2024

Other Metrics

View Author Metrics

Citations

Cited By

Mai DVan Hanh N(2024)Whether English Proficiency and English Self-Efficacy Influence the Credibility of ChatGPT-Generated English Content of EMI StudentsInternational Journal of Computer-Assisted Language Learning and Teaching10.4018/IJCALLT.34997214:1(1-21)Online publication date: 11-Mar-2024
https://dl.acm.org/doi/10.4018/IJCALLT.349972
Gnewuch UMorana SHinz OKellner RMaedche A(2024)More Than a Bot? The Impact of Disclosing Human Involvement on Customer Interactions with Hybrid Service AgentsInformation Systems Research10.1287/isre.2022.015235:3(936-955)Online publication date: 1-Sep-2024
https://dl.acm.org/doi/10.1287/isre.2022.0152
Guo Y(2024)A study on the mechanism of the impact of anthropomorphic features of virtual chatbots on user experienceProceedings of the 2024 International Conference on Digital Society and Artificial Intelligence10.1145/3677892.3677897(32-37)Online publication date: 24-May-2024
https://dl.acm.org/doi/10.1145/3677892.3677897
Gao FFang KChan W(2024)Humanizing Artifacts: An Educational Game For Cultural Heritage Artifacts and History Using Generative AI: Humanizing ArtifactsCompanion Proceedings of the 2024 Annual Symposium on Computer-Human Interaction in Play10.1145/3665463.3678792(91-96)Online publication date: 14-Oct-2024
https://dl.acm.org/doi/10.1145/3665463.3678792
Rashik MJasim MKucher KSarvghad AMahyar N(2024)Beyond Text and Speech in Conversational Agents: Mapping the Design Space of AvatarsProceedings of the 2024 ACM Designing Interactive Systems Conference10.1145/3643834.3661563(1875-1894)Online publication date: 1-Jul-2024
https://dl.acm.org/doi/10.1145/3643834.3661563
Qian CWexler J(2024)Take It, Leave It, or Fix It: Measuring Productivity and Trust in Human-AI CollaborationProceedings of the 29th International Conference on Intelligent User Interfaces10.1145/3640543.3645198(370-384)Online publication date: 18-Mar-2024
https://dl.acm.org/doi/10.1145/3640543.3645198
Lo P(2024)An Autoethnographic Reflection of Prompting a Custom GPT Based on OneselfExtended Abstracts of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613905.3651096(1-9)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613905.3651096
Li ZLiang MLc RLuo Y(2024)StayFocused: Examining the Effects of Reflective Prompts and Chatbot Support on Compulsive Smartphone UseProceedings of the 2024 CHI Conference on Human Factors in Computing Systems10.1145/3613904.3642479(1-19)Online publication date: 11-May-2024
https://dl.acm.org/doi/10.1145/3613904.3642479
Hou TTseng YYuan C(2024)Is this AI sexist? The effects of a biased AI’s anthropomorphic appearance and explainability on users’ bias perceptions and trustInternational Journal of Information Management: The Journal for Information Professionals10.1016/j.ijinfomgt.2024.10277576:COnline publication date: 17-Jul-2024
https://dl.acm.org/doi/10.1016/j.ijinfomgt.2024.102775
Lu ZMin QJiang LChen Q(2024)The effect of the anthropomorphic design of chatbots on customer switching intention when the chatbot service failsInternational Journal of Information Management: The Journal for Information Professionals10.1016/j.ijinfomgt.2024.10276776:COnline publication date: 17-Jul-2024
https://dl.acm.org/doi/10.1016/j.ijinfomgt.2024.102767
Show More Cited By

View Options

View options

Get Access

Login options

Check if you have access through your login credentials or your institution to get full access on this article.

Full Access

Get this Publication

Media

Figures

Other

Tables

View Issue’s Table of Contents