AI Saliency and Robo Advisor Adoption

Last registered on August 10, 2023


Trial Information

General Information

AI Saliency and Robo Advisor Adoption
Initial registration date
August 03, 2023

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
August 10, 2023, 1:23 PM EDT

First published corresponds to when the trial was first made public on the Registry after being reviewed.



Primary Investigator

University of Hong Kong

Other Primary Investigator(s)

PI Affiliation
University of Hong Kong
PI Affiliation
Lingnan University

Additional Trial Information

In development
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
As people recognize the capabilities of AI, it raises the question of whether this positive attitude will extend to products that utilize AI technology. Specifically, in household finance, financial companies have introduced AI-assisted investment robo-advisors to optimize investment outcomes for investors. To investigate further, we plan to collaborate with a Chinese household investment mobile application. We aim to provide users with information about AI and then observe whether these users demonstrate a heightened preference for robo-advisor investment products when making choices among various financial products.

We are outlining an experiment where we aim to replace the splash screen of the mobile software for the experimental group with slogans associated with AI hot topics, carefully linking these slogans to various fields and professionals. On the other hand, the control group will be presented with a splash screen featuring sayings that are unrelated to AI. By comparing the responses of the experimental and control groups, we can gauge whether AI hot topics have a positive impact on users' inclination toward adopting AI-assisted investment portfolios. Furthermore, by examining the influence of individuals from different professions and levels of prominence, we intend to analyze whether authoritative figures influence users' perspectives on AI investment capabilities.
External Link(s)

Registration Citation

An, Jiafu, Wenzhi Ding and Xincheng Wang. 2023. "AI Saliency and Robo Advisor Adoption." AEA RCT Registry. August 10.
Experimental Details


We show the treatment in the splash screen when the user opens the mobile application.

The treatment consists of three parts: (1) Slogan; (2) Name of quoted person; (3) Title of quoted person. Since our treated users are Chinese, we will describe the treatment in Chinese and English.

We have three types of slogans:
S1. No slogan
S2. "The age of AI has begun" (人工智能时代已经开始)
S3. "AI will replace X% human jobs" (人工智能将取代X%的人类工作)

We have these combinations of quoted persons and their titles:
Q1. Fick Parson, ChatGPT Creator (菲克帕森,ChatGPT缔造者)
Q2. Sam Altman, ChatGPT Creator (山姆阿尔特曼,ChatGPT缔造者)
Q3. Bill Gates, ChatGPT Creator (比尔盖茨,ChatGPT缔造者)
Q4. Sam Altman, Tech Tycoon (山姆阿尔特曼,科技大亨)
Q5. Bill Gates, Tech Tycoon (比尔盖茨,科技大亨)
Q6. Elon Musk, Tech Tycoon (马斯克,科技大亨)
Q7. Warren Buffet, Finance Tycoon (巴菲特,科技大亨)
Q8. Rupert Murdoch, Media Tycoon (默多克,科技大亨)

We will not test all combinations due to the sample size limitation. The treatment combinations to be tested are:

- S1 (1 group)
- S2+Q1-8 (8 groups)
- S3+Q3 (where X=20, 50, 80, 3 groups)

Therefore, there will be 12 groups in total.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
The net inflow investment amount into the robo-advisor portfolios.
Primary Outcomes (explanation)
We will obtain the change in investment for each user in each portfolio daily. Then we will classify the product into "robo-advisor portfolio" and "other portfolio" based on whether it is called an "All Weather Portfolio." The company defines "All Weather Portfolio" as a series of portfolios driven by AI algorithms and promotes them in this way.

We will summarize all inflow and outflow of the "robo-advisor portfolio" and "other portfolio" during the 14-day observation window. The outcome is our primary outcome.

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We plan to show the users the intervented splash screen throughout the 14-day experimental period. We will randomly draw 5,000 users who logged into the mobile application within four weeks to one week before the experiment. Then we will randomize them into 12 groups. We repeat the randomization for several rounds to ensure the users are balanced regarding several pre-trial traits, such as age, gender, and asset amount, register date.

The treatments are described in the "Intervention" part.
Experimental Design Details
Randomization Method
Pre-treatment trait balanced sampling. The randomization is done in the office by a computer, and the group of users is pre-determined based on the randomization outcome.
Randomization Unit
Individual user-level.
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
5000 active users.
Sample size: planned number of observations
5000 active users.
Sample size (or number of clusters) by treatment arms
400 active users for each treatment arm.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Institutional Review Boards (IRBs)

IRB Name
Human Research Ethics Committee
IRB Approval Date
IRB Approval Number


Post Trial Information

Study Withdrawal

There is information in this trial unavailable to the public. Use the button below to request access.

Request Information


Is the intervention completed?
Data Collection Complete
Data Publication

Data Publication

Is public data available?

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials