Author
Listed:
- Beidi Hu
(Booth School of Business, University of Chicago, Chicago, Illinois 60637)
- Celia Gaertig
(Haas School of Business, University of California, Berkeley, Berkeley, California 94720)
- Berkeley J. Dietvorst
(Booth School of Business, University of Chicago, Chicago, Illinois 60637)
Abstract
Businesses across industries, such as food delivery apps and GPS navigation systems, routinely provide customers with time estimates in inherently uncertain contexts. How does the format of these time estimates affect customers’ satisfaction? In particular, should companies provide customers with a point estimate representing the best estimate, or should they communicate the inherent uncertainty in outcomes by providing a range estimate? In eight preregistered experiments ( N = 5,323), participants observed time estimates provided by an app, and we manipulated whether the app presented the time estimates as a point estimate (e.g., “Your food will arrive in 45 minutes.”) or a range (e.g., “Your food will arrive in 40–50 minutes.”). After participants learned about the app’s prediction performance by sampling a set of past outcomes, we measured participants’ evaluation of the app. We find that participants judged the app more positively when it provided a range rather than a point estimate. These results held across different domains, different time durations, different underlying outcome distributions, and an incentive-compatible design. We also find that this preference is not simply due to people’s dislike of late outcomes, as participants also rated ranges more positively than conservative point estimates corresponding to the upper (i.e., later) bound of the range. These findings suggest that companies can increase customer satisfaction with realized time estimates by communicating the uncertainty inherent in these time estimates.
Suggested Citation
Beidi Hu & Celia Gaertig & Berkeley J. Dietvorst, 2025.
"How Should Time Estimates Be Structured to Increase Customer Satisfaction?,"
Management Science, INFORMS, vol. 71(9), pages 7497-7515, September.
Handle:
RePEc:inm:ormnsc:v:71:y:2025:i:9:p:7497-7515
DOI: 10.1287/mnsc.2023.00137
Download full text from publisher
Corrections
All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:inm:ormnsc:v:71:y:2025:i:9:p:7497-7515. See general information about how to correct material in RePEc.
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
We have no bibliographic references for this item. You can help adding them by using this form .
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Chris Asher (email available below). General contact details of provider: https://edirc.repec.org/data/inforea.html .
Please note that corrections may take a couple of weeks to filter through
the various RePEc services.