Do Long Wait Times Cause Bad Reviews?
A bike repair shop owner scrolls through last month’s reviews on a phone during a quiet Tuesday morning, an illustrative scene rather than a real venue. One review carries a single star. It mentions waiting three weeks for a wheel to be trued. The easy conclusion: fix the wait, fix the rating.
Large studies of online reviews put a number on that instinct, and it complicates it. Waiting appears in bad reviews far more often than in good ones, but it ranks sixth among what one-star reviewers write about. How a customer was treated ranks first, by a wide margin. Understanding the gap between the felt wait and the clock helps explain why.
This article works through what one-star and five-star reviews contain, and why a review that mentions waiting is not proof the wait was long. It also covers what predicts satisfaction better than a stopwatch does. The evidence comes from published research on doctors, restaurants and emergency departments, plus a fresh comparison of Britain’s own waiting data against its own experience data.
Key Takeaways
- Waiting is in bad reviews, but it is not what fills them. Across 14,659 one-star and five-star reviews of American doctors on one review site, 154 of the 1,977 one-star reviews mentioned wait time and 1,145 mentioned the doctor’s manner. Billing was mentioned in 155, one more than waiting. (Laryngoscope Investig Otolaryngol, 2024)
- What predicts how satisfied someone says they were is the wait they perceived, not the wait the clock recorded. A 2025 systematic review of emergency departments, pooling a sample above 3,491 patients, named perceived waiting time for triage among its key predictors. (Cureus, 2025)
- Experiments published in the Journal of Retailing in 2023 found people tolerate waiting longer than they expected, up to a point. That tolerance breaks down when the business itself supplied the estimate.
- Almost nobody reviews anything, and what they write does not all survive. Of 2,425 surgeons in the United States checked on one review site, 148 had even a single review with a comment attached (Cureus, 2024). Of the reviews contributed to Yelp in 2025, 70% were recommended by its software (Yelp, 2026).
- The manipulation runs the other way from what most owners assume. Of the fake reviews Trustpilot removed in 2024, 3.4 million carried five stars and 627,000 carried one. (Trustpilot, 2025)
What do people actually put in a one-star review?
Waiting is there, but it’s a long way from the top. A 2024 study in Laryngoscope Investig Otolaryngol trained a model to sort 14,659 user comments from 883 profiles of American ear, nose and throat doctors on one review site. It used only one-star and five-star reviews, to capture the most extreme feedback. Doctors with fewer than 10 reviews were left out.
Wait time ranked sixth among specific complaints in the one-star reviews, mentioned in 154 of 1,977. Bedside manner ranked first, mentioned in 1,145. One review can land in more than one row, so the percentages below do not add up to 100.
| Rank | What the one-star review mentions | n of 1,977 | % |
|---|---|---|---|
| 1 | Bedside manner | 1,145 | 57.92 |
| 2 | Staff and mid-levels | 313 | 15.83 |
| 3 | Procedure or surgery | 301 | 15.23 |
| 4 | Treatment plan | 281 | 14.21 |
| 5 | Billing | 155 | 7.84 |
| 6 | Wait time | 154 | 7.79 |
| 7 | Office scheduling | 116 | 5.87 |
| 8 | Accessibility | 57 | 2.88 |
| 9 | Facilities | 24 | 1.21 |
Source: Stenzel JG, Schultz NR, et al., “Automated classification of online reviews of otolaryngologists,” Laryngoscope Investigative Otolaryngology, 2024, read the study. Every percentage is the count divided by 1,977. The paper’s own printed Accessibility percentage does not match its count, so this table recalculates it the same way as every other row.
Being ranked sixth still means wait time outranked three other groups: office scheduling, accessibility and facilities. It is a real complaint, just not the leading one. A trained language model did the sorting, with accuracy scores of 0.71 to 0.97 by complaint type, rather than a human reading every comment by hand. That matters here. Automated classification captures topic, not severity.
Billing edged out waiting by a single review. And most one-star reviews say nothing about waiting at all. Subtracting the 154 that mention it from 1,977 leaves 1,823 one-star reviews, or 92.21%, worked out from the paper’s own published counts.
Do good reviews mention the wait?
Hardly ever, and that cuts both ways. In the same Healthgrades study, wait time appeared in just 159 of 12,682 five-star reviews, against 154 of the far smaller pool of 1,977 one-star reviews. Waiting is rarer in good reviews. It is also rare in bad ones.
A separate 2024 study in Cureus looked only at five-star reviews of American orthopaedic foot and ankle surgeons, and found much the same pattern from a different angle. Of 3,215 comments categorised, wait time appeared in 136, or 4.2%. Good outcomes accounted for 940 comments and bedside manner for 921.
So the honest reading is not that waiting does not matter. It shows up more when a visit went badly than when it went well. It is simply not what most bad reviews are about, and it is nowhere near the biggest factor in either direction.
Both studies used only the extremes, one-star and five-star, or five-star alone. Neither included the two, three and four-star reviews where most people probably feel torn. So neither claim applies to a typical review left about an ordinary, unremarkable visit.
Does a review that mentions waiting mean the wait was long?
No, and none of the research covered here tested that question directly. A review is written after the fact, from memory, often by someone who left annoyed. It records that waiting felt like part of the story. It does not record how many minutes passed.
A 2025 study in the Asia Marketing Journal comes closest to testing the connection directly, and even it stops short. Its authors analysed online restaurant reviews and found a significant negative link between waiting-related comments and overall ratings. They named waiting as one of four recurring themes in the text, alongside food, the physical setting and service quality.
That is a link between comments and ratings, not between measured minutes and ratings. Nobody in that study timed a single diner’s wait with a stopwatch. A review that talks about waiting and a review that reflects a long wait are not necessarily the same review.
This is not a technicality. An owner who reads “the wait” in a review and assumes ten minutes felt like thirty is guessing. The review names a feeling, not a duration, and treating the two as interchangeable can send attention toward the wrong fix.
Working out whether a wait was long starts earlier than the review, with deciding which two events bracket a wait in the first place. A star rating skips that step entirely.
What actually predicts how people rate a service?
The wait people perceived, and whether it beat what they expected. Both matter more than the number of minutes on a clock, in research that looked for the strongest drivers of satisfaction.
A systematic review of patient satisfaction in emergency departments was published in Cureus in December 2025. It pooled a sample exceeding 3,491 patients across the included studies, and named perceived waiting time for triage as one of the strongest drivers. Overall doctor satisfaction and whether the visit met the patient’s expectations were the other two. The word “perceived” is doing real work there. It is not the same as measured.
A second line of evidence, from a study published in the Journal of Retailing in 2023, adds a twist. Across three studies, waiting longer than expected reduced satisfaction only slightly, while waiting less than expected raised it sharply. The researchers found two limits to that pattern. It broke down when the wait ran much longer than expected. It also broke down when the customer’s expectation came from the business itself, such as a quoted wait time.
That last point is the one worth remembering. A venue that quotes a wait and then misses it loses the tolerance that would have protected it. The customer stops comparing the wait to a guess and starts comparing it to a promise. Knowing what actually shortens a felt wait matters more once an estimate has been given.
Who leaves a review in the first place?
Very few people, and not a random few. Two figures, from two different kinds of measurement, both come out low enough to change how any review count should be read.
Of 2,425 orthopaedic surgeons checked on one review site, only 148, or 6.1%, had even a single review carrying a written comment. Nineteen surgeons in twenty had nothing to read at all, whatever their actual patient experience looked like.
Britain’s own A&E patient experience survey shows a similar pattern from the other direction. In June 2026, NHS England’s Friends and Family Test for accident and emergency departments received 116,266 responses against 1,414,840 in a column it labels Total Eligible. That is a response rate of 8.2%. An unprompted online review clears an even higher bar than that.
The Healthgrades study’s own authors name the reason in their limitations section. In their own words, “reviews can be skewed as physicians may encourage satisfied patients to leave positive feedback, leading to an overrepresentation of favorable reviews.”
A page of star ratings, on its own, cannot show:
- Who chose not to write anything, and why they stayed quiet.
- How long any customer waited, measured in minutes rather than remembered afterwards.
- Whether the wait or the welcome caused a particular star rating.
- What the people who left before being served would have said.
That last point matters most. Research on the people who give up before they are served and on the reasons people give for walking out both describe a group a star-rating page cannot see. These are the customers who left before anyone could serve them, let alone review them. None of this means the silent majority had nothing to say. It means nobody counted what they would have said.
What happens to the reviews people do leave?
A lot of them never surface, and the fakes that do get removed lean the opposite way from what most owners assume. Three of the largest review platforms now publish yearly figures on what happens after someone hits submit.
Yelp’s 2025 Trust and Safety Report states that about 22 million reviews were posted on the platform that year. Of those, 70% were recommended by its software as helpful and reliable. The remaining 30% splits three ways in Yelp’s own figures. Seventeen percent were not recommended for being unreliable, 11% were removed by its trust and safety team for policy violations, and 2% were removed by the reviewers themselves.
Google reported similarly in April 2026. Its systems and analysts blocked or removed over 292 million policy-violating reviews in 2025, while publishing more than 1 billion reviews it judged helpful. Google merges blocked and removed into one combined figure and never states the split, so no rate can be worked out from what it publishes.
The biggest surprise comes from Trustpilot. Its 2025 Trust Report says 4.5 million fake reviews were removed from the platform in 2024, and 90% of those were caught by its detection models. A chart in that same report breaks the removed fakes down by star rating: 3.4 million were five-star, against 627,000 that were one-star. On this evidence, fake reviews mostly inflate a business’s rating rather than damage it.
None of these figures can be stacked against each other. Yelp, Google and Trustpilot each define their groups differently, and Yelp’s “not recommended” bucket includes reviews that are merely unreliable or asked-for, not only fraudulent ones. Treat each number as a fact about one platform, not a shared industry rate. For a single business, the practical point is simpler: a handful of visible reviews is never the complete record, in either direction.
Britain publishes both numbers, and warns against comparing them
NHS England says plainly that its two headline measures should not be set side by side. This analysis does exactly that anyway, on purpose, to show what the warning guards against. The results are worked out for this article from two of NHS England’s own June 2026 releases, and nowhere else.
NHS England’s own Friends and Family Test guidance describes the results as “not statistically comparable against other organisations because of the various data collection methods” for any given month. Even so, the measure is meant to help inform service improvement and patient choice.
This comparison was worked out for this article. For 116 NHS trusts appearing in both files, June 2026 four-hour A&E performance for Type 1 departments was matched against each trust’s Friends and Family Test positive score.
Every check came back close to zero, and none was significant. Both figures spread widely enough across trusts to rule out a restricted range as the explanation. Four-hour performance ran from 49.8% to 91.3%, and Friends and Family Test positive scores ran from 55.6% to 100%.
One example makes the point better than a number can. The trust with the worst four-hour performance in the month, at 49.8%, scored 78.4% positive on the experience survey, above the national average of 77.4%. The best performer, at 91.3%, scored 78.2%, almost identical.
| Specification | n | Pearson r | Spearman rho |
|---|---|---|---|
| All-type four-hour performance vs FFT positive | 116 | +0.02 | +0.08 |
| Type 1 only four-hour performance vs FFT positive | 116 | +0.06 | +0.12 |
| All-type four-hour performance vs FFT negative | 116 | -0.06 | -0.09 |
| 12-hour waits per 1,000 attendances vs FFT positive | 116 | -0.13 | -0.20 |
| Trusts with 1,000+ FFT responses | 47 | -0.05 | -0.06 |
Source: worked out for this article from NHS England’s Friends and Family Test data, June 2026, and its A&E Attendances and Emergency Admissions 2026-27 provider data, June 2026, retrieved 2026-08-29.
What none of this settles
This analysis compares trusts, not people. A flat line across 116 trusts does not mean waiting has no effect on how any one patient felt about their visit. It means that at trust level, in one month, the two measures did not move together.
The Friends and Family Test is a solicited survey with a five-point scale, not an unprompted online review. The two review-site studies elsewhere in this article, on doctors’ star ratings, are both American, drawn from two different websites. The wider research on what predicts satisfaction draws on multiple countries, and each source covers its own limited period.
One more limit is worth naming here. Can a business simply make a negative review disappear? The Competition and Markets Authority states that its rules on how reviews are handled cover cases where negative reviews are hidden or star ratings present an inaccurate picture.
None of this is a reason to dismiss a wait complaint. It is a reason to read the whole review before deciding what caused it. None of the review studies here cover restaurants, garages, salons or tours directly, so applying their exact figures outside healthcare is a stretch, not a measurement.
Separating the wait from the welcome
The single clearest pattern in this evidence is worth carrying into any pile of reviews. How a customer was treated shows up far more often than how long they waited. In the one dataset checked review by review, bedside manner appeared in three-fifths of one-star reviews. Wait time appeared in under one in twelve.
A useful test for any single review is to read past the word “wait” and see what surrounds it. A complaint that stops at the delay is different from one that goes on to describe how staff spoke. Did anyone explain the hold-up, or did the person feel ignored while they stood there?
That distinction, treatment versus duration, is the one worth applying to a real complaint before assuming the fix is a shorter queue. Sometimes it is. Just as often, the person who left a single star was describing how the wait was handled, not how long it lasted.
Frequently Asked Questions
Do long waits actually lower a business’s star rating?
Waiting appears more often in one-star reviews than five-star ones, but it’s a minor factor next to how people were treated. In one review-site study, wait time featured in 154 of 1,977 one-star reviews of American doctors, against 1,145 mentioning the doctor’s manner. That gap holds even though the study only used one-star and five-star reviews, nothing in between. (Laryngoscope Investig Otolaryngol, 2024)
What do customers complain about most in bad reviews?
How they were treated, by a wide margin. In the same study, bedside manner appeared in 57.92% of one-star reviews, more than seven times the 7.79% that mentioned wait time. Billing, at 7.84%, was one place ahead of wait time at 7.79%, which was itself one place ahead of office scheduling at 5.87%. (Laryngoscope Investig Otolaryngol, 2024)
Can a business get a bad review about waiting taken down?
Not simply for mentioning a wait. Guidance from the Competition and Markets Authority covers how reviews are handled, such as when negative reviews are hidden or a star rating gives a misleading picture. Platforms also run their own removal systems: Yelp reports that 70% of reviews it received in 2025 were recommended by its software, meaning most stay visible. (CMA, 2026; Yelp, 2026)
How can you tell whether waiting is really your problem?
A 2025 study in the Asia Marketing Journal measured whether restaurant reviews mentioned waiting, not how many minutes anyone waited. It found a significant negative link between waiting-related comments and star ratings, but a mention of waiting is not evidence of a long wait. The review reflects how the delay felt afterwards, not a timed duration. (Asia Marketing Journal, 2025)
Sources
- Stenzel JG, Schultz NR, et al., “Automated classification of online reviews of otolaryngologists,” Laryngoscope Investigative Otolaryngology, retrieved 2026-08-29, https://pmc.ncbi.nlm.nih.gov/articles/PMC11558699/
- “Categorizing Extremely Positive Five-Star Online Reviews for Orthopedic Foot and Ankle Surgeons: A Retrospective Study,” Cureus, retrieved 2026-08-29, https://pmc.ncbi.nlm.nih.gov/articles/PMC11576059/
- Baek J, Choe Y, “Detrimental Impact of Waiting on Dining Experiences: Evidence from Online Restaurant Reviews,” Asia Marketing Journal, retrieved 2026-08-29, https://amj.kma.re.kr/journal/vol27/iss1/4/
- “Factors Affecting Patient Satisfaction in the Emergency Department: A Systematic Review,” Cureus, retrieved 2026-08-29, https://pmc.ncbi.nlm.nih.gov/articles/PMC12811694/
- Caruelle D, Lervik-Olsen L, Gustafsson A, “The clock is ticking, or is it? Customer satisfaction response to waiting shorter vs. longer than expected during a service encounter,” Journal of Retailing, University of Manchester Research Explorer record, retrieved 2026-08-29, https://research.manchester.ac.uk/en/publications/the-clock-is-tickingor-is-it-customer-satisfaction-response-to-wa/
- NHS England, “Friends and Family Test data, June 2026,” retrieved 2026-08-29, https://www.england.nhs.uk/publication/friends-and-family-test-data-june-2026/
- NHS England, “A&E Attendances and Emergency Admissions 2026-27,” retrieved 2026-08-29, https://www.england.nhs.uk/statistics/statistical-work-areas/ae-waiting-times-and-activity/ae-attendances-and-emergency-admissions-2026-27/
- NHS England, “Friends and Family Test,” retrieved 2026-08-29, https://www.england.nhs.uk/fft/
- Yelp, “2025 Trust and Safety Report,” retrieved 2026-08-29, https://blog.yelp.com/news/2025-trust-and-safety-report/
- Google, blog post on new measures protecting businesses on Maps, retrieved 2026-08-29, https://blog.google/products-and-platforms/products/maps/new-ways-were-protecting-businesses-on-maps/
- Trustpilot, “Trust Report 2025,” retrieved 2026-08-29, https://corporate.trustpilot.com/trust/trust-report-2025
- Competition and Markets Authority, “Fake and misleading reviews: 5 businesses under CMA investigation,” retrieved 2026-08-29, https://www.gov.uk/government/news/fake-and-misleading-reviews-5-businesses-under-cma-investigation