When expert recommendation contradicts peer opinion: Relative social influence of valence, group identity and artificial intelligence

Wang, Jinping; Molina, María de Miguel; Sundar, S. Shyam

doi:10.1016/j.chb.2020.106278

Cited by 45 publications

(19 citation statements)

References 48 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…These findings extend the work conducted in previous studies that highlight issues associated with the use of AI in services (e.g. Shank et al, 2019 ; Wang et al, 2020 ). Our findings reveal that customers are willing to sacrifice important elements of conventional services if AI-enabled services are personalised and offer a high-quality service.…”

Section: Discussionsupporting

confidence: 88%

“…Together, these findings provide empirical support for findings from previous studies that highlight the significance of trust in AI technology (e.g. Wang, Molina, & Sundar, 2020 ).…”

Section: Discussionsupporting

confidence: 87%

“…Despite the significant role these issues can play, previous studies have mainly focused on the use of AI from a technical and organisational perspective ( Jarrahi, 2018 ). As a result, there is a lack of research on how customers perceive AI technology as part of their shopping experience, and how this leads to a more pleasant experience and stronger relationships with brands ( Shank et al, 2019 ; Wang, Molina & Sunder, 2020 ).…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Customer experiences in the age of artificial intelligence

Ameen

Tarhini

Reppel

et al. 2021

Computers in Human Behavior

270

203

View full text Add to dashboard Cite

Artificial intelligence (AI) is revolutionising the way customers interact with brands. There is a lack of empirical research into AI-enabled customer experiences. Hence, this study aims to analyse how the integration of AI in shopping can lead to an improved AI-enabled customer experience. We propose a theoretical model drawing on the trust-commitment theory and service quality model. An online survey was distributed to customers who have used an AI- enabled service offered by a beauty brand. A total of 434 responses were analysed using partial least squares-structural equation modelling. The findings indicate the significant role of trust and perceived sacrifice as factors mediating the effects of perceived convenience, personalisation and AI-enabled service quality. The findings also reveal the significant effect of relationship commitment on AI-enabled customer experience. This study contributes to the existing literature by revealing the mediating effects of trust and perceived sacrifice and the direct effect of relationship commitment on AI-enabled customer experience. In addition, the study has practical implications for retailers deploying AI in services offered to their customers.

show abstract

Section: Discussionsupporting

confidence: 88%

“…Together, these findings provide empirical support for findings from previous studies that highlight the significance of trust in AI technology (e.g. Wang, Molina, & Sundar, 2020 ).…”

Section: Discussionsupporting

confidence: 87%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Customer experiences in the age of artificial intelligence

Ameen

Tarhini

Reppel

et al. 2021

Computers in Human Behavior

270

203

View full text Add to dashboard Cite

show abstract

“…Thus, relevant stimuli (i.e., recommended results by AI technology versus human experts) will cause further assessment and emotion during the secondary appraisal stage. Individuals' evaluation and emotions will defer depending on the types of recommendation source (i.e., AI intelligence versus human expert) [12][13][14]. In the context of AI service acceptance, consumers will evaluate its costs and benefits based on perceived performance and expectation [15] and then form the emotions toward AI service usage.…”

Section: Aidua Modelmentioning

confidence: 99%

“…Examples include how information filtering is structured or which types of filtering systems are utilized in the field [7][8][9][10] and which recommendation algorithms can effectively improve recommendation quality [9][10][11]. Several scholars have focused on consumer-related issues, such as the comparison of AI versus human expert recommendation services [12][13][14] and consumer acceptance of AI devices in service encounters [6], especially using the technology acceptance model (TAM) [15,16]. However, research on AI recommendation services from a consumer perspective is still limited because the literature does not explore consumers' psychological mechanism of how and why individuals accept AI recommendation services.…”

Section: Introductionmentioning

confidence: 99%

AI Recommendation Service Acceptance: Assessing the Effects of Perceived Empathy and Need for Cognition

Yoon

Lee

2021

JTAER

View full text Add to dashboard Cite

This study investigated the effect of perceived technology quality and personalization quality on behavioral intentions, mediated by perceived empathy in using an artificial intelligence (AI) recommendation service. The study was based on a theoretical model of artificial intelligent device use acceptance. We also tested the moderating effect of individuals’ need for cognition, influencing empathy. Data collection was conducted through an online survey using a nationally recognized consumer research panel service in Korea. The participants were asked to respond to their preferences and needs on sneakers; then, they randomly experienced the AI (versus human expert) recommendation service that offers a recommended product. A total of 200 data were analyzed using SPSS 21.0 for descriptive statistics, reliability analysis, and PROCESS analysis, and AMOS 21.0 for confirmatory factor analysis and structural equation modeling (SEM). Results revealed that, compared with the human (expert) recommendation service, the AI recommendation service increased perceived technology quality, which increased personalization quality. Technology and personalization quality had a positive influence on behavioral intentions, mediated by perceived empathy. In addition, when individuals had a high level of need for cognition, the effect of personalization quality on empathy was stronger. However, individuals with a low level of need for cognition perceived greater empathy, as technology quality increased. The findings of the current study improve understanding of how consumers accept AI technology-driven services in the online shopping context.

show abstract