Human Computer / Robot Interaction

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Paper Number

2697

Paper Type

short

Description

As advice-giving systems (AGS) become more cognitive and human-like, they can influence decision-making to a new level. It becomes increasingly important to cast our attention to this new type of intelligent systems and explore how users perceive and react to their persuasive influence. This paper proposes a theoretical framework based on the Persuasion Knowledge Model, which identifies users’ perceptions to develop three types of knowledge (agent knowledge, persuasion knowledge, and topic knowledge) when presented with persuasive suggestions. These perceptions will further lead to users’ trust (cognitive, affective), and influence advice-taking (behavioral trust), and reuse intentions of the intelligent systems. This study contributes to the IS literature by proposing the framework of factors that influence various dimensions of trust and system reuse intentions under the new context of intelligent AGS with persuasive features.

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10-HCI

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Dec 12th, 12:00 AM

Perceiving Intelligent Advice-Giving Systems – A Trust Perspective Under the Persuasion Knowledge Model

As advice-giving systems (AGS) become more cognitive and human-like, they can influence decision-making to a new level. It becomes increasingly important to cast our attention to this new type of intelligent systems and explore how users perceive and react to their persuasive influence. This paper proposes a theoretical framework based on the Persuasion Knowledge Model, which identifies users’ perceptions to develop three types of knowledge (agent knowledge, persuasion knowledge, and topic knowledge) when presented with persuasive suggestions. These perceptions will further lead to users’ trust (cognitive, affective), and influence advice-taking (behavioral trust), and reuse intentions of the intelligent systems. This study contributes to the IS literature by proposing the framework of factors that influence various dimensions of trust and system reuse intentions under the new context of intelligent AGS with persuasive features.

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