The hidden bias of restaurant reviews

Time to doubt the stars. Tourists are swayed more by vibes than value when rating restaurants

Featured faculty: Michael Zhang
Written by Putro Harnowo

Hungry while travelling, but couldn’t decide which restaurant to go to? No worries. Just grab your phone and check the ratings for nearby restaurants. These user-generated reviews appear to be more trustworthy than traditional advertising due to their open submissions, making them handy not only for tourists, but also for locals craving something special.

No wonder platforms like Google Reviews and Yelp are cherished. Local apps like Japan’s Tabelog, South Korea’s Naver and Kakao, Chinese Mainland’s Meituan, and Hong Kong’s OpenRice also thrive on their community-based and more curated ratings. However, these reviews should be taken with a grain of salt. Ratings often vary across different platforms, and a high rating doesn’t always guarantee quality.

“Online ratings can be biased, leading to misleading information for readers,” says Michael Zhang, the Wei Lun Professor of Business AI at the Department of Decisions, Operations and Technology at the Chinese University of Hong Kong (CUHK) Business School.

In his latest study, Why is the grass always greener on the other side? Tourist bias in online restaurant ratings, Professor Zhang finds that one key source is tourist bias in their reviews. “Tourists are about 13.4 per cent more likely to give restaurants a higher rating than locals. If most reviews come from tourists who rate places more positively, locals might feel the restaurant is overrated.”

This tendency is evidently found across all kinds of restaurants, whether chains or independent, cheap or high-end, and among tourists from both large and small cities, across all genders. The study also looks into the very different experiences and expectations about restaurants from local customers and tourists.

Understanding this bias would help businesses better manage customer satisfaction levels and tailor their offerings to meet their customers’ expectations. Meanwhile, customers can leverage the findings to assist their judgment when looking for restaurants.

“Tourists are about 13.4 per cent more likely to give restaurants a higher rating than locals. If most reviews come from tourists who rate places more positively, locals might feel the restaurant is overrated.”

Professor Michael Zhang

Why do tourists tend to give higher ratings?

Along with Xu Da Peng at South China University of Technology, Hong Hong at Tongji University, and Ye Qiang at the University of Science and Technology of China, Professor Zhang collected data from Chinese Mainland’s most popular restaurant review platform, focusing on 10 cities famous for their local cuisines, namely Changchun, Changsha, Guangzhou, Guiyang, Haikou, Suzhou, Tianjin, Xi’an, Xiamen, and Zhengzhou.

The sample consists of 70,950 reviews written by 747 users for nearly 40,000 restaurants with a wide range of prices and popularities. The researchers identify whether a reviewer is a local or traveller by analysing their location information relative to the location of the reviewed restaurants.

Professor Zhang and his team discovered that the positive emotional state while travelling is the reason for tourist bias. Travelling boosts their mood and makes tourists more willing to express their feelings instead of pondering on analytical or detailed reasoning. Reviews from tourists are also shorter but accompanied by more photos, suggesting they care more about sharing their experiences rather than commenting on taste and price. They are also more forgiving of restaurant flaws.

Tourists on the lookout for more than just functional dining are more sensitive to the emotional aspects, such as the restaurant’s vibe and the hospitality they experience, as they mention in their reviews. Meanwhile, locals write more about locations, cooking methods, and prices. The researchers further identify the distinctive words used by each group

Focusing on experience may give the impression that tourists seek more expensive or higher-quality restaurants, but a deeper analysis finds that both locals and tourists who write reviews often avoid fancy establishments. It’s just that reviews from tourists tend to be more positive.

How can restaurants and customers leverage tourist bias?

Given the different interests between locals and tourists, Professor Zhang suggests restaurants apply a dual-focus promotional strategy that addresses the distinct needs of both groups. Restaurants can emphasise service quality to attract tourists, create photo-worthy environments and highlight experiential aspects that tourists can share with others, while also training staff to provide attentive service that generates positive emotional experiences.

To retain local customers, restaurants should focus on fundamentals, such as competitive prices and consistent quality and taste, while offering value propositions that appeal to the rational mind and build a reputation through authentic cooking techniques that locals prefer to mention in reviews.

“To integrate both target markets, restaurants may separate seating areas or dining times to cater to different atmospheres, or differentiate menus by highlighting signature experiences for tourists and the most favourite dishes for local patrons,” he says. “Both groups value quality, so maintaining high standards across all dimensions benefits all target markets.”

For the customers, Professor Zhang suggests taking extra steps to find reviews that better match their preferences and weigh the ratings differently. “Tourists wanting to eat like a local should prioritise longer reviews that highlight the restaurant’s location, cooking methods, value for money, with more analytical wordings. Meanwhile, those seeking a memorable experience should consider reviews highlighting service and ambience, with more photos and emotional expressions.”

As restaurants located in touristy areas may have their overall ratings inflated by travellers, the actual local feedback might be closer to lower-rated reviews.

Taking bias into account for better reviews

To ensure the users receive more accurate reviews, Professor Zhang suggests platforms identify which reviews are submitted by tourists and locals, then calculate separate average ratings for each group and provide functionality that allows users to view ratings from either locals or tourists.

With ever-evolving technologies, machine learning can be deployed to train algorithms to scan and detect reviewer types. Algorithmic adjustments can also be carried out to moderate ratings from identified tourists, considering their tendency to inflate their rating submissions.

For transparency, platforms can add labels mentioning locals or tourists to their reviews or display notes or disclaimers about potential bias in highly rated restaurants with predominantly tourist reviews. Platforms can also show trend lines of ratings separated by reviewer type over time.

Furthermore, platforms can suggest personalised recommendations. For example, platforms can recommend restaurants emphasising service and environment for travellers, while suggesting restaurants highlighting location, cooking quality, and value for local users.

“Overall, we find that the tourist bias is consistent across restaurant types, whether chains or independent, as well as across price levels and cities, making the findings broadly applicable,” Professor Zhang adds.