Choice Architecture for a Better On-Demand Economy: A Field Experiment on Upfront Rating Disclosure in Mobile Search

37 Pages Posted: 15 Feb 2024 Last revised: 20 Mar 2024

See all articles by Siliang Tong

Siliang Tong

Nanyang Business School, Nanyang Technological University

Shuang Zheng

Dalian University of Technology

Yixing Chen

Mendoza College of Business, University of Notre Dame

Shrihari Sridhar

Texas A&M University - Department of Marketing

Xianneng Li

Dalian University of Technology

Date Written: January 15, 2024

Abstract

On-demand delivery platforms (e.g., DoorDash) connect consumers with restaurants at scale, and have transformed how we eat. However, such platforms have been scrutinized for squeezing the profit margins of small restaurants that are independent, niche, or in non-prime locations. Further, as consumers are sensitive to delivery delays, these platforms predominantly sort restaurant options by delivery time in response to consumer search, putting these restaurant owners in an even more disadvantaged position. We explore whether and how a design change in the choice architecture, upfront disclosure of restaurant ratings in autocomplete search listing, can shift consumer choices to reduce disparities across restaurants. We partner with a large on-demand delivery app in Asia to randomize consumers into two conditions: consumers in the treatment (control) group are shown restaurant ratings in addition to the status quo information (status quo information only) as soon as they start entering a search query and see the autocomplete list of restaurant options. We uncover several findings. First, upfront disclosure of restaurant ratings increases both order frequency and average order value, leading to a 3.25% lift in overall spending. Second, the disclosure effect is primarily driven by increased spending at restaurants that are higher-rated but also independent, niche, or located in distant areas. Third, heterogeneity in the consumer responses supports the argument that upfront rating disclosure imposes a trade-off between restaurant quality and delivery time. Fourth, subsequent analyses with tapstream data provide suggestive mechanism evidence: upfront disclosure of restaurant ratings expands consumers' consideration set to include disadvantaged restaurants that were ranked lower in the autocomplete list. Collectively, we shed light on the design of online choice architecture for on-demand service platforms and provide substantive implications for different stakeholders in the on-demand economy.

Suggested Citation

Tong, Siliang and Zheng, Shuang and Chen, Yixing and Sridhar, Shrihari and Li, Xianneng, Choice Architecture for a Better On-Demand Economy: A Field Experiment on Upfront Rating Disclosure in Mobile Search (January 15, 2024). Nanyang Business School Research Paper No. 24-07, Available at SSRN: https://ssrn.com/abstract=4695864 or http://dx.doi.org/10.2139/ssrn.4695864

Siliang Tong (Contact Author)

Nanyang Business School, Nanyang Technological University ( email )

Singapore, 639798
Singapore

Shuang Zheng

Dalian University of Technology ( email )

Huiying Rd
DaLian, LiaoNing, Liaoning 116024
China

Yixing Chen

Mendoza College of Business, University of Notre Dame ( email )

396 Mendoza College of Business
Notre Dame, IN 46556
United States

Shrihari Sridhar

Texas A&M University - Department of Marketing ( email )

430 Wehner
College Station, TX 77843-4218
United States

HOME PAGE: http://mays.tamu.edu/directory/shriharisridhar/

Xianneng Li

Dalian University of Technology ( email )

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