Please use this identifier to cite or link to this item:
Title: AI-enabled investment advice: will users buy it?
Authors: Chua, Alton Yeow Kuan
Pal, Anjan
Banerjee, Snehasish
Keywords: Social sciences::Communication
Issue Date: 2023
Source: Chua, A. Y. K., Pal, A. & Banerjee, S. (2023). AI-enabled investment advice: will users buy it?. Computers in Human Behavior, 138, 107481-.
Journal: Computers in Human Behavior
Abstract: The objective of this paper is to develop and empirically validate a conceptual model that explains individuals' behavioral intention to accept AI-based recommendations as a function of attitude toward AI, trust, perceived accuracy and uncertainty level. The conceptual model was tested through a between-participants experiment using a simulated AI-enabled investment recommendation system. A total of 368 participants were randomly and evenly assigned to one of the two experimental conditions, one depicting low-uncertainty investment recommendation involving blue-chip stocks while the other depicting high-uncertainty investment recommendation involving penny stocks. Results show that attitude toward AI was positively associated with behavioral intention to accept AI-based recommendations, trust in AI, and perceived accuracy of AI. Furthermore, uncertainty level moderated how attitude, trust and perceived accuracy varied with behavioral intention to accept AI-based recommendations. When uncertainty was low, a favorable attitude toward AI seemed sufficient to promote reliance on automation. However, when uncertainty was high, a favorable attitude toward AI was a necessary but no longer sufficient condition for AI acceptance. Thus, the paper contributes to the human-AI interaction literature by not only shedding light on the underlying psychological mechanism of how users decide to accept AI-enabled advice but also adding to the scholarly understanding of AI recommendation systems in tasks that call for intuition in high involvement services.
ISSN: 0747-5632
DOI: 10.1016/j.chb.2022.107481
Schools: Wee Kim Wee School of Communication and Information 
Rights: © 2022 Elsevier Ltd. All rights reserved.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:WKWSCI Journal Articles

Citations 50

Updated on Apr 18, 2024

Web of ScienceTM
Citations 50

Updated on Oct 27, 2023

Page view(s)

Updated on Apr 18, 2024

Google ScholarTM




Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.