Bots vs. humans: how schema congruity, contingency-based interactivity, and sympathy influence consumer perceptions and patronage intentions

Lou, Chen; Kang, Hyunjin; Tse, Caleb H.

doi:10.1080/02650487.2021.1951510

Cited by 55 publications

(20 citation statements)

References 65 publications

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“…Moreover, people are reluctant to accept moral decisions proposed by AI because AI's tradeoff strategy is objectionable in such conditions (Dietvorst & Bartels, 2021). Also, research in personality psychology found that AI‐enabled service chatbots are perceived to be less warm and less empathetic (Lou et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

The searching artificial intelligence: Consumers show less aversion to algorithm‐recommended search product

Xie

Zhang

et al. 2022

Psychology and Marketing

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Though artificial intelligence (AI) recommendation is a hot topic in recent marketing research, previous research has shown a convergent tendency for aversion to AI recommendation. It is imperative to find ways to promote AI usage and reduce consumers’ AI aversion. This study fills this void by exploring the effect of AI (vs. human) recommenders on consumers’ preferences for search versus experience products in the context of e‐commerce. Two studies provide convergent evidence that consumers show less avoidance of algorithms when recommending search products compared to experience products. A behavioral experiment (Study 1, N = 112) validates that consumers are less likely to purchase experience products recommended by AI, while there are no significant differences between AI versus human recommenders when recommending search products. Using event‐related potential (ERP), a further consumer neuroscience study (Study 2, N = 26) shows that consumers have a higher level of cognitive conflict (i.e., a larger magnitude of N2) when AI (vs. human) recommends experience products, while the effect disappears for search products. This paper shows that for search products, marketers can obtain similar evaluations using AI recommenders, which is relatively cheaper and more time‐saving compared with human recommenders. Therefore, our work provides important implications for theory and practice on e‐commerce and marketing communication.

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Section: Introductionmentioning

confidence: 99%

The searching artificial intelligence: Consumers show less aversion to algorithm‐recommended search product

Xie

Zhang

et al. 2022

Psychology and Marketing

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“…This incongruity may produce negative user reactions [ 50 , 51 ]. This way, we shed new light on the existing chatbot research focusing on incongruities in chatbot perception [ 60 ]. Particularly, our results highlight that the positive effects of chatbot empathy, suggested by the CASA approach [ 43 , 44 ], diminish not only when a chatbot is too humanlike, as proposed by UVM theory [ 11 ], but also in the presence of incongruity between the declared empathy and the chatbot’s actual outputs.…”

Section: Discussionmentioning

confidence: 99%

The Effects of Expressing Empathy/Autonomy Support Using a COVID-19 Vaccination Chatbot: Experimental Study in a Sample of Belgian Adults

Trzebiński¹,

Claessens²,

Buhmann³

et al. 2023

JMIR Form Res

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Background Chatbots are increasingly used to support COVID-19 vaccination programs. Their persuasiveness may depend on the conversation-related context. Objective This study aims to investigate the moderating role of the conversation quality and chatbot expertise cues in the effects of expressing empathy/autonomy support using COVID-19 vaccination chatbots. Methods This experiment with 196 Dutch-speaking adults living in Belgium, who engaged in a conversation with a chatbot providing vaccination information, used a 2 (empathy/autonomy support expression: present vs absent) × 2 (chatbot expertise cues: expert endorser vs layperson endorser) between-subject design. Chatbot conversation quality was assessed through actual conversation logs. Perceived user autonomy (PUA), chatbot patronage intention (CPI), and vaccination intention shift (VIS) were measured after the conversation, coded from 1 to 5 (PUA, CPI) and from –5 to 5 (VIS). Results There was a negative interaction effect of chatbot empathy/autonomy support expression and conversation fallback (CF; the percentage of chatbot answers “I do not understand” in a conversation) on PUA (PROCESS macro, model 1, B=–3.358, SE 1.235, t186=2.718, P=.007). Specifically, empathy/autonomy support expression had a more negative effect on PUA when the CF was higher (conditional effect of empathy/autonomy support expression at the CF level of +1SD: B=–.405, SE 0.158, t186=2.564, P=.011; conditional effects nonsignificant for the mean level: B=–0.103, SE 0.113, t186=0.914, P=.36; conditional effects nonsignificant for the –1SD level: B=0.031, SE=0.123, t186=0.252, P=.80). Moreover, an indirect effect of empathy/autonomy support expression on CPI via PUA was more negative when CF was higher (PROCESS macro, model 7, 5000 bootstrap samples, moderated mediation index=–3.676, BootSE 1.614, 95% CI –6.697 to –0.102; conditional indirect effect at the CF level of +1SD: B=–0.443, BootSE 0.202, 95% CI –0.809 to –0.005; conditional indirect effects nonsignificant for the mean level: B=–0.113, BootSE 0.124, 95% CI –0.346 to 0.137; conditional indirect effects nonsignificant for the –1SD level: B=0.034, BootSE 0.132, 95% CI –0.224 to 0.305). Indirect effects of empathy/autonomy support expression on VIS via PUA were marginally more negative when CF was higher. No effects of chatbot expertise cues were found. Conclusions The findings suggest that expressing empathy/autonomy support using a chatbot may harm its evaluation and persuasiveness when the chatbot fails to answer its users’ questions. The paper adds to the literature on vaccination chatbots by exploring the conditional effects of chatbot empathy/autonomy support expression. The results will guide policy makers and chatbot developers dealing with vaccination promotion in designing the way chatbots express their empathy and support for user autonomy.

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“…(Kervyn et al 2013). Warmth capture's people's perception of someone's cooperative intentions, while competence captures their perceived ability to execute those intentions (Fiske et al 2002;Lou et al 2021).…”

Section: Human-machine Communication Through a Social Perceptions Lensmentioning

confidence: 99%

Female chatbots are helpful, male chatbots are competent?

2022

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Chatbots have in recent years increasingly been used by organizations to interact with their customers. Interestingly, most of these chatbots are gendered as female, displaying stereotypical notions in their avatars, profile pictures and language. Considering the harmful effects associated with gender-based stereotyping at a societal level—and in particular the detrimental effects to women—it is crucial to understand the effects of such stereotyping when transferred and perpetuated by chatbots. The current study draws on the Stereotype Content Model (SCM) and explores how the warmth (high vs. low) of a chatbot’s language and the chatbot’s assigned gender elicit stereotypes that affect the perceived trust, helpfulness, and competence of the chatbot. In doing so, this study shows how established stereotype theory can be used as a framework for human-machine communication research. Moreover, its results can serve as a foundation to explore ways of mitigating the perpetuation of stereotyping and bring forward a broader discussion on ethical considerations for human-machine communication.

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Bots vs. humans: how schema congruity, contingency-based interactivity, and sympathy influence consumer perceptions and patronage intentions

Cited by 55 publications

References 65 publications

The searching artificial intelligence: Consumers show less aversion to algorithm‐recommended search product

The searching artificial intelligence: Consumers show less aversion to algorithm‐recommended search product

The Effects of Expressing Empathy/Autonomy Support Using a COVID-19 Vaccination Chatbot: Experimental Study in a Sample of Belgian Adults

Female chatbots are helpful, male chatbots are competent?

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