When do you trust AI? The effect of number presentation detail on consumer trust and acceptance of AI recommendations

Kim, Jungkeun; Giroux, Marilyn; Lee, Jacob C.

doi:10.1002/mar.21498

Cited by 86 publications

(97 citation statements)

References 91 publications

(130 reference statements)

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“…The present research makes several key contributions. First, we confirm the pivotal role of trust in AI adoption [ 16 , 23 , 24 , 38 ], particularly in the context of medical decisions [ 13 – 15 ], here in the context of a global health pandemic. Our results suggest that a mere one unit increase in the measure of perceived trust in the medical AI results in a seven-fold increase in the likelihood of people choosing medical AI over a human physician.…”

Section: Discussionsupporting

confidence: 66%

“…This is generalized by research showing that AI algorithms are preferred over humans in decisions in which utilitarian goals are activated [ 20 ], suggesting the compensatory nature of medical AI adoption, and that “people may prefer algorithmic to human judgment […] when interacting with the provider might endanger his or her life (i.e., triage services for COVID-19)” [ 21 , p. 447]. In addition, several studies suggest that the adoption of AI agents depends on psychological constructs, such as consumer trust [ 22 – 24 ], and personality traits, such as open-mindedness [ 25 ]; yet, mistrust in medical AI has never been tested in the context of a global health pandemic, nor the potential role of other drivers, such as mistrust in human physicians, and perceived uniqueness neglect from human physicians. Indeed, people who experience various forms of marginalization may develop mistrust towards other humans’ decisions [ 26 ], and may even increase their interactions with artificial products that mimic human intelligence [ 27 ].…”

Section: Introductionmentioning

confidence: 99%

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Drivers and social implications of Artificial Intelligence adoption in healthcare during the COVID-19 pandemic

et al. 2021

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The COVID-19 pandemic continues to impact people worldwide–steadily depleting scarce resources in healthcare. Medical Artificial Intelligence (AI) promises a much-needed relief but only if the technology gets adopted at scale. The present research investigates people’s intention to adopt medical AI as well as the drivers of this adoption in a representative study of two European countries (Denmark and France, N = 1068) during the initial phase of the COVID-19 pandemic. Results reveal AI aversion; only 1 of 10 individuals choose medical AI over human physicians in a hypothetical triage-phase of COVID-19 pre-hospital entrance. Key predictors of medical AI adoption are people’s trust in medical AI and, to a lesser extent, the trait of open-mindedness. More importantly, our results reveal that mistrust and perceived uniqueness neglect from human physicians, as well as a lack of social belonging significantly increase people’s medical AI adoption. These results suggest that for medical AI to be widely adopted, people may need to express less confidence in human physicians and to even feel disconnected from humanity. We discuss the social implications of these findings and propose that successful medical AI adoption policy should focus on trust building measures–without eroding trust in human physicians.

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Section: Discussionsupporting

confidence: 66%

Section: Introductionmentioning

confidence: 99%

Drivers and social implications of Artificial Intelligence adoption in healthcare during the COVID-19 pandemic

et al. 2021

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“…We also find that there is no difference in perceptions of personalisation, meaning that an AI influencer is perceived as being as able to personalise – content or recommendations – as a human influencer. We propose that this is a spill-over effect from consumers becoming increasingly comfortable with AI recommendation systems (Kim et al , 2021), as in the case of Netflix, which are seen as being able to learn from behaviour and make intelligent recommendations. It may be that developers of AI influencers need to play up this element as a key competitive advantage of AI influencers.…”

Section: Discussionmentioning

confidence: 99%

“…The fact that there is no difference in personalisation means an AI influencer is seen as being able to personalise content or recommendations in a manner like a human influencer. This is interesting and likely driven by consumers becoming increasingly comfortable with AI recommendation systems (Kim et al , 2021), it may be that the perceived efficacy of such systems spill over to other domains of AI, such as AI influencers. We also find that WOM intentions are higher for an AI influencer regardless of agency.…”

Section: Experimental Studiesmentioning

confidence: 99%

Unreal influence: leveraging AI in influencer marketing

Sands

Campbell

Plangger

et al. 2022

EJM

132

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Purpose This paper aims to examine how consumers respond to social media influencers that are created through artificial intelligence (AI) and compares effects to traditional (human) influencers. Design/methodology/approach Across two empirical studies, the authors examine the efficacy of AI social media influencers. With Study 1, the authors establish baseline effects for AI influencers and investigate how social-psychological distance impacts consumer perceptions. The authors also investigate the role of an influencer’s agency – being autonomous or externally managed – to test the boundaries of the results and determine the interactive effects between influencer type and influencer agency. Study 2 acts as an extension and validation of Study 1, whereby the authors provide generalisability and overlay the role of need for uniqueness as a moderated mediator. Findings The authors show that there are similarities and differences in the ways in which consumers view AI and human influencers. Importantly, the authors find no difference in terms of intention to follow or personalisation. This suggests that consumers are equally open to follow an AI or human influencer, and they perceive the level of personalisation provided by either influencer type as similar. Furthermore, while an AI influencer is generally perceived as having lower source trust, they are more likely to evoke word-of-mouth intentions. In understanding these effects, the authors show that social distance mediates the relationship between influencer type and the outcomes the authors investigate. Results also show that AI influencers can have a greater effect on consumers who have a high need for uniqueness. Finally, the authors find that a lack of influencer agency has a detrimental effect. Research limitations/implications The studies investigate consumers’ general response to AI influencers within the context of Instagram, however, future research might examine consumers’ response to posts promoting specific products across a variety of category contexts and within different social media platforms. Practical implications The authors find that in some ways, an AI influencer can be as effective as a human influencer. Indeed, the authors suggest that there may be a spill-over effect from consumer experiences with other AI recommendation systems, meaning that consumers are open to AI influencer recommendations. However, the authors find consistent evidence that AI influencers are trusted less than traditional influencers, hence the authors caution brands from rushing to replace human influencers with their AI counterparts. Originality/value This paper offers novel insight into the increasingly prominent phenomenon of the AI influencer. Specifically, it takes initial steps towards developing understanding as to how consumers respond to AI influencers and contrast these effects with human influencers.

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“…Second, recent advances in digital transformation, such as artificial intelligence, machine learning and big data, have fueled the proliferation of digital marketing practices. These advances have not only affected how consumers live but also changed how firms do business and interact with consumers, including retailing (Kim et al , 2021a; Reinartz et al , 2019), business strategy (Huang and Rust, 2021; Verhoef et al , 2021), service (Zaki, 2019) and personal engagement (Kumar et al , 2019). The leading global and local firms must prioritize “digital transformation” for their businesses.…”

mentioning

confidence: 99%