I-It, I-Thou, I-Robot: The Perceived Humanness of AI in Human-Machine Communication

Westerman, David; Edwards, Autumn; Edwards, Chad; Luo, Zhenyang; Spence, Patric R.

doi:10.1080/10510974.2020.1749683

Cited by 67 publications

(20 citation statements)

References 56 publications

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“…This pattern deepens our understanding of the humanness nature of conversational AI since it indicates that users can perceive different dimensions of the humanness of an AI system diversely. While prior studies focused on the overall humanness perception and its effect on user behavior (Go & Sundar, 2019;Lankton, McKnight, & Tripp, 2015;Westerman et al, 2020), this study suggests that disaggregating the humanness cues embedded in an AI system could be favorable for knowing the relative value of individual humanness factors (Nass & Moon, 2000).…”

Section: Summary Of Key Findingsmentioning

confidence: 81%

“…Although we observed several scales in the technology humanness literature that may be useful to measure the overall perceived humanness of conversational AI (Cho, Molina, & Wang, 2019;Westerman et al, 2020;Westerman, Cross, & Lindmark, 2019), none of them can be directly adopted to measure the two specific types of AI humanness (i.e., voice humanness and understanding humanness) posited in this study. As a result, we developed the measurement items for these two humanness constructs by following several procedural recommendations concerning scale development (Hinkin, 1995;MacKenzie, Podsakoff, & Podsakoff, 2011).…”

Section: Methodsmentioning

confidence: 99%

“…Understanding humanness perception is defined as the extent to which a user feels that a conversational AI system can understand what he/she says. Because the responses to the third and fourth questions provide potential content domains for understanding humanness and voice humanness, respectively, we combine these responses, the operational definitions, and prior research pertains to technology humanness (Go & Sundar, 2019;Lankton, McKnight, & Tripp, 2015;Westerman et al, 2020) to develop items for the two humanness constructs. Initially, we developed six items for each of the two humanness.…”

Section: Methodsmentioning

confidence: 99%

“…Voice Humanness (Go & Sundar, 2019;Schuetzler et al, 2020;Westerman et al, 2020) VH1 My voice assistant's pronunciation is natural.…”

Section: No Itemsmentioning

confidence: 99%

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Dual humanness and trust in conversational AI: A person-centered approach

Gong

2021

Computers in Human Behavior

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Section: Summary Of Key Findingsmentioning

confidence: 81%

Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

“…Voice Humanness (Go & Sundar, 2019;Schuetzler et al, 2020;Westerman et al, 2020) VH1 My voice assistant's pronunciation is natural.…”

Section: No Itemsmentioning

confidence: 99%

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Dual humanness and trust in conversational AI: A person-centered approach

Gong

2021

Computers in Human Behavior

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“…Considering chatbots as social actors (cf, social response theory), individuals can be expected to apply readily available learned human social scripts to interactions with a chatbot as well, especially when specific cues signal that it enacts a particular role [ 27 , 38 ]. Prior conceptual conversational agent studies have developed taxonomies, typologies, and classifications of different types of chatbots, for example, differentiating chatbots for domain-specific or for general-purpose use [ 39 ], for specific applications (eg, business-to-business customer services [ 40 ] and health care [ 41 ]), for different purposes [ 42 , 43 ], for single- or multiple-user use cases [ 44 , 45 ], or for specific periods [ 46 ].…”

Section: Introductionmentioning

confidence: 99%

The Effects of Health Care Chatbot Personas With Different Social Roles on the Client-Chatbot Bond and Usage Intentions: Development of a Design Codebook and Web-Based Study

Nißen¹,

Rüegger²,

Stieger³

et al. 2022

J Med Internet Res

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Background The working alliance refers to an important relationship quality between health professionals and clients that robustly links to treatment success. Recent research shows that clients can develop an affective bond with chatbots. However, few research studies have investigated whether this perceived relationship is affected by the social roles of differing closeness a chatbot can impersonate and by allowing users to choose the social role of a chatbot. Objective This study aimed at understanding how the social role of a chatbot can be expressed using a set of interpersonal closeness cues and examining how these social roles affect clients’ experiences and the development of an affective bond with the chatbot, depending on clients’ characteristics (ie, age and gender) and whether they can freely choose a chatbot’s social role. Methods Informed by the social role theory and the social response theory, we developed a design codebook for chatbots with different social roles along an interpersonal closeness continuum. Based on this codebook, we manipulated a fictitious health care chatbot to impersonate one of four distinct social roles common in health care settings—institution, expert, peer, and dialogical self—and examined effects on perceived affective bond and usage intentions in a web-based lab study. The study included a total of 251 participants, whose mean age was 41.15 (SD 13.87) years; 57.0% (143/251) of the participants were female. Participants were either randomly assigned to one of the chatbot conditions (no choice: n=202, 80.5%) or could freely choose to interact with one of these chatbot personas (free choice: n=49, 19.5%). Separate multivariate analyses of variance were performed to analyze differences (1) between the chatbot personas within the no-choice group and (2) between the no-choice and the free-choice groups. Results While the main effect of the chatbot persona on affective bond and usage intentions was insignificant (P=.87), we found differences based on participants’ demographic profiles: main effects for gender (P=.04, ηp2=0.115) and age (P<.001, ηp2=0.192) and a significant interaction effect of persona and age (P=.01, ηp2=0.102). Participants younger than 40 years reported higher scores for affective bond and usage intentions for the interpersonally more distant expert and institution chatbots; participants 40 years or older reported higher outcomes for the closer peer and dialogical-self chatbots. The option to freely choose a persona significantly benefited perceptions of the peer chatbot further (eg, free-choice group affective bond: mean 5.28, SD 0.89; no-choice group affective bond: mean 4.54, SD 1.10; P=.003, ηp2=0.117). Conclusions Manipulating a chatbot’s social role is a possible avenue for health care chatbot designers to tailor clients’ chatbot experiences using user-specific demographic factors and to improve clients’ perceptions and behavioral intentions toward the chatbot. Our results also emphasize the benefits of letting clients freely choose between chatbots.

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Artificial Intelligence in Organizational Communication: Challenges, Opportunities, and Implications

Gulbrandsen,

Just

2024

New Perspectives in Organizational Communication

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I-It, I-Thou, I-Robot: The Perceived Humanness of AI in Human-Machine Communication

Cited by 67 publications

References 56 publications

Dual humanness and trust in conversational AI: A person-centered approach

Dual humanness and trust in conversational AI: A person-centered approach

The Effects of Health Care Chatbot Personas With Different Social Roles on the Client-Chatbot Bond and Usage Intentions: Development of a Design Codebook and Web-Based Study

Artificial Intelligence in Organizational Communication: Challenges, Opportunities, and Implications

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