Research
Daki is organized around this question. This page separates what a first survey found, what Daki currently hypothesizes, what still needs testing, and what a Harvard-focused pilot could measure.
They are linked: if diners can choose earlier, that choice might also become an earlier signal for the people who plan and cook.
Hypothesis 1 · Diners
Giving diners the option to plan ahead may help them make choices that better reflect their preferences, needs and schedules.
Could be wrong if planning feels like a chore, few people use it, or plans rarely match what people end up eating.
Hypothesis 2 · Dining teams
Giving dining teams earlier, aggregated feedback may help them understand demand and adjust communication, ordering or preparation sooner.
Could be wrong if signals are too sparse, unrepresentative, or arrive too late to change any real decision.
Making the idea intuitive
Shopping with a list does not remove choice. It means arriving with a rough plan instead of deciding everything in the aisle. Daki asks whether a light plan for meals could do something similar.
A longer-term concept: a voluntary planning layer where people could select meals in advance and, if they wish, connect those intentions to their calendars.
Planning ahead is not always better. Many people prefer to decide in the moment, and that should stay easy. The research question is whether offering the option helps particular people, and whether it produces information dining teams can use.
The strongest signals were about seeing options clearly and keeping decisions quick.
Respondents could select more than one feature or factor. Among the 90 Harvard-related responses, 48.9% reported difficulty deciding at least sometimes, compared with 72.1% of the 68 from other universities.
About this evidence. These figures come from one survey of 158 respondents collected September 19–29, 2026. It was a convenience sample, and the answers reflect stated behavior and interest. The survey does not demonstrate repeat use, product-market fit, customer demand or institutional impact.
Keeping claims honest
Proposed, not final. Measures and thresholds would be agreed with any pilot partner before testing begins.
| Measure | What it would tell us | Side | Possible method |
|---|---|---|---|
| Decision time | Whether people reach a choice faster | Diners | Time from opening to a chosen meal |
| Confidence | Whether people feel better about the choice | Diners | Short in-product rating |
| Recommendation relevance | Whether suggested options fit | Diners | Select, save and skip actions; reasons given |
| Advance-planning behavior | Who plans ahead, how far, and how often plans hold | Diners | Share of meals chosen in advance |
| Repeat use | Whether anyone comes back unprompted | Diners | Return within 7 and 28 days |
| Menu-data accuracy | Whether information shown matches what is served | Both | Freshness checks and correction reports |
| Actionable aggregate signals | Whether dining teams can use what they see | Dining teams | Structured interviews with dining staff |
Two interactive concepts turn the hypotheses into something people can react to. They use illustrative data, and they help Daki learn what to test with real diners and dining teams.
Hypothesis 1 · Diners
Does seeing relevant options, with reasons, make a decision feel easier? Does anyone use the optional planning?
Try the diner experienceHypothesis 2 · Dining teams
Would aggregated, simulated signals be understandable and potentially useful to dining professionals, and what would make them misleading?
View the dining-team conceptLimits
Students can join the waitlist to take part in early testing. Dining professionals and researchers who want to help design or evaluate the experiment are warmly invited to get in touch.
The waitlist is a short Google Form. Joining does not give immediate access; it helps Daki recruit early testers and understand dining contexts for the first Harvard-focused experiment.
We would like to learn what information would be useful to you before anything is built for dining teams.
maverickyasuda@college.harvard.edu