An Agent Model for Analysis of Trust Dynamics in Short-Term Human-Robot Interaction

Aziz, Azizi Ab; Hussain, Wadhah A. Abdul; Ahmad, Faudziah; Yusof, Nooraini; Ahmad, Farzana Kabir

doi:10.1007/978-3-319-48517-1_8

Cited by 1 publication

(3 citation statements)

References 17 publications

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“…Interaction session was focused. Researchers found that voice query was longer than text query (Ab Aziz et al, 2016) and voice query was considered to be verbose (Gupta & Bendersky, 2015; Bentley et al, 2018). Speech recognition quality, pronunciation characteristics, speed, volume and pitch were adopted to measure voice query (Hassan et al, 2015).…”

Section: Literature Reviewmentioning

confidence: 99%

“…The model was analyzed through available literature on trust and human‐robot interaction by meta‐analysis method (Hancock et al, 2011). Later when researchers explored its application in various scenarios, the model is developed (Hoff & Bashir, 2013; Ab Aziz et al, 2016; Wagner et al, 2018; Lewis et al, 2018) and expend as basis of trust in human‐technology (Siau & Wang, 2018).…”

Section: Theoretical Model and Hypothesismentioning

confidence: 99%

“…The performance of robot had the greatest association with trust actually (Hancock et al, 2011). Wherever automation system or robot domain, system property contributes to the trust (Ab Aziz et al, 2016). The level of capability was one of the antecedents to trust development in human‐AI (Glikson & Woolley, 2020).…”

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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