Attachment and trust in artificial intelligence

Gillath, Omri; Ai, Ting; Branicky, M.S.; Keshmiri, Shawn; Davison, Robert B.; Spaulding, Ryan

doi:10.1016/j.chb.2020.106607

Cited by 196 publications

(133 citation statements)

References 54 publications

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“…Specifically, in Study 3, the higher trust towards the expert human advisor is mainly driven by the higher emotional trust while the higher trust towards the robot advisor results from higher cognitive trust. It appears that different trust dimensions may act in different directions in adopting AI-based SSTs, with trust in reliance and integrity acting as facilitators of AI-based SST adoption (Lee and See, 2004) and lack of emotional trustimpeding its adoption (Gillath et al, 2021).…”

Section: Discussionmentioning

confidence: 99%

Who do you choose? Comparing perceptions of human vs robo-advisor in the context of financial services

Zhang

Pentina

Fan

2021

JSM

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Purpose This study aims to investigate the differences in consumers’ perceptions of trust, performance expectancy and intention to hire between human financial advisors with high/low expertise and robo-advisors. Design/methodology/approach Three experiments were conducted. The respondents were randomly assigned to human advisors with high/low expertise or a robo-advisor. Data were analyzed using MANCOVA. Findings The results suggest that consumers prefer human financial advisors with high expertise to robo-advisors. There are no significant differences between robo-advisors and novice financial advisors regarding performance expectancy and intention to hire. Originality/value This pioneering study extends the self-service technology adoption theory to examine adoption of robo-advisors vs human financial advisors with different expertise levels. To the best of the authors’ knowledge, it is among the first studies to address multi-dimensionality of trust in the context of artificial intelligence-based self-service technologies.

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Section: Discussionmentioning

confidence: 99%

Who do you choose? Comparing perceptions of human vs robo-advisor in the context of financial services

Zhang

Pentina

Fan

2021

JSM

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“…As the widespread loneliness of young people nowadays, they tend to develop an intimate relationship and social expectation with it (Turkle, 2017). They gradually offer cognitive trust, develop affective trust and love IPA (Gillath et al, 2021). It can be predicted that the personality of the agent may gradually increase its impact strength to the whole trustworthiness, especially when the personality of agent matching with users’.…”

Section: Discussionmentioning

confidence: 99%

“…Users tend to develop affective relationships in long‐term interaction with IPA. Exposure to attachment security cues can lead to increased trust (Gillath et al, 2021), such as love, loss, anxiety.…”

Section: Theoretical Model and Hypothesismentioning

confidence: 99%

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Why Do You Trust Siri?: The Factors Affecting Trustworthiness of Intelligent Personal Assistant

Huang

2021

Proceedings of the Association for Information Science and Tech

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Trust greatly contributes to human-AI collaboration, however, human's trust to IPA is hard to establish and lacks exploration. The purpose of this paper is to recognize the factors that affect the trustworthiness of IPA. 358 questionnaires were analyzed by PLS-SEM to construct the model, while thematic analysis was used to discover expectance of IPA. Chi-square tests and T-test were used to distinguish the difference between two user groups. Three factors that capability of system, personality of agent, and availability of interface have a significant impact on the trustworthiness of IPA. The capability of system is the most essential as the threshold with users' plenty of expectations. Most users pay less attention to the availability of interface and the personality of agent has a great impact on the trustworthiness of IPA. The factors found enrich the trusted AI research and inspire insights of design of IPA.

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“…The antecedents of trust in AI are summarized and shown in Table 1 . The categories of variables that may influence trust in AI include machine performance (e.g., machine capabilities 10 or response quality/timeliness 9 ), transparency (e.g., causability 11 , explainability 11 , 12 ), representation (e.g., humanness 6 , facial features 13 , dynamic features 13 , emotional expressions 13 , virtual agents 14 ), voice (e.g., voice consistent 7 and perceived voice personality 15 ), interaction (e.g., interaction quality 16 , consumer-chatbot relationship type 8 , reciprocal self-disclosure 17 , human-in-the-loop 18 ), emotion (e.g., attachment style 19 ), and user personal traits (e.g., big five personality characteristics 20 ). Related studies also cover a wide range of contexts, including human-robot interaction 10 , 13 , conversational assistants 9 , 15 , 16 , recommendation systems 11 , medical computer vision 12 , speech recognition systems 14 , in-vehicle assistants 7 , and private or public services 6 , 18 .…”

Section: Literature Reviewmentioning

confidence: 99%

An empirical investigation of trust in AI in a Chinese petrochemical enterprise based on institutional theory

Jia

Zhou

Yao

et al. 2021

Sci Rep

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Despite its considerable potential in the manufacturing industry, the application of artificial intelligence (AI) in the industry still faces the challenge of insufficient trust. Since AI is a black box with operations that ordinary users have difficulty understanding, users in organizations rely on institutional cues to make decisions about their trust in AI. Therefore, this study investigates trust in AI in the manufacturing industry from an institutional perspective. We identify three institutional dimensions from institutional theory and conceptualize them as management commitment (regulative dimension at the organizational level), authoritarian leadership (normative dimension at the group level), and trust in the AI promoter (cognitive dimension at the individual level). We hypothesize that all three institutional dimensions have positive effects on trust in AI. In addition, we propose hypotheses regarding the moderating effects of AI self-efficacy on these three institutional dimensions. A survey was conducted in a large petrochemical enterprise in eastern China just after the company had launched an AI-based diagnostics system for fault detection and isolation in process equipment service. The results indicate that management commitment, authoritarian leadership, and trust in the AI promoter are all positively related to trust in AI. Moreover, the effect of management commitment and trust in the AI promoter are strengthened when users have high AI self-efficacy. The findings of this study provide suggestions for academics and managers with respect to promoting users’ trust in AI in the manufacturing industry.

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Attachment and trust in artificial intelligence

Cited by 196 publications

References 54 publications

Who do you choose? Comparing perceptions of human vs robo-advisor in the context of financial services

Who do you choose? Comparing perceptions of human vs robo-advisor in the context of financial services

Why Do You Trust Siri?: The Factors Affecting Trustworthiness of Intelligent Personal Assistant

An empirical investigation of trust in AI in a Chinese petrochemical enterprise based on institutional theory

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