Document Type



Doctor of Philosophy (PhD)


Business Administration

First Advisor's Name

George Marakas

First Advisor's Committee Title

Committee chair

Second Advisor's Name

Miguel Aguirre-Urreta

Second Advisor's Committee Title

Committee member

Third Advisor's Name

Hyeyoung Hah

Third Advisor's Committee Title

Committee member

Fourth Advisor's Name

Wensong Wu

Fourth Advisor's Committee Title

Committee member


Intelligent Advice-Giving Systems, Persuasion Knowledge Model, Persuasive Systems, IS Trust Model, Anthropomorphism

Date of Defense



With artificial intelligence (AI) penetrating into a broad range of industries in the current age, it has an impact on our daily living in a more and more profound way. Interacting with AI-based systems for advice has become a common practice as well. As advice-giving systems (AGS) become more cognitive and human-like, they can influence users’ decision-making to a new level. Therefore, it becomes increasingly important to explore this new type of intelligent system and examine how users perceive and react to the system’s persuasive influence. Based on the persuasion knowledge model, this paper identifies various persuasive designs (anthropomorphic features, explanation facilities, and intervention styles) and studies how they affect users’ knowledge levels, trust perceptions (cognitive, affective), and eventually their acceptance of advice (behavioral trust) and reuse intentions. The research model has been tested in an online experiment and collected 442 valid responses. In general, the findings give empirical support for the proposed research model in the paper.

The study contributes to (1) the human-computer interaction literature on the effectiveness of different persuasive design characteristics of intelligent AGS. (2) to traditional decision support systems literature on the mechanism that users use under the persuasive influence of the new type of intelligent AGS (persuasive decision-aid systems). (3) to the trust in automation literature by studying various types of trust toward intelligent AGS and their relationships. (4) to the persuasion literature by incorporating the persuasion knowledge model to understand users’ attitudes and behaviors toward intelligent agents. (5) to the literature on algorithm aversion and algorithm appreciation by resolving the contradictory findings with a holistic theoretical framework. (6) to the anthropomorphism literature by exploring various aspects of anthropomorphism perceptions on trust. The paper also made insightful implications for practice.



Previously Published In

Yu, T. (2021), Exploring the Effects of Persuasive Designs of Intelligent Advice-Giving Systems on Users’ Trust Perceptions, Advice Acceptance and Reuse Intentions. In the Proceedings of International Conference on Information Systems (ICIS), Austin, Texas, USA.



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