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Upcoming Changes in Online Review and Rating Systems for Restaurants

Due to the coronavirus (COVID-19), more people are looking for food delivery options and almost reflexively, their respective reviews and ratings. However, nowadays, credibility problems of reviews and ratings have come up because of fake and malicious reviews. As a result, many companies have prepared various solutions to try to solve this problem. Naver, the Korean company that operates the country’s most popular portal site, announced that it would establish newly built review systems by the third quarter of this year. Therefore, the Sungkyun Times (SKT) will introduce review and rating systems, their problems, and how corporations are trying to improve them.

The Introduction of Online Review and Rating Systems

-An Age Full of Reviews and Ratings

Presently, people are living in a world filled with reviews and ratings. Many people leave their positive or negative evaluations online about what they have experienced. According to a survey related to consumer reviews in 2017 by Embrain, a marketing research company, 78.6% responded that they always check reviews before buying products. Also, 69.3% of people appeared to trust reviews and product evaluations written by consumers who have the first-hand experience with the products. People check the reviews because they want to lessen the uncertainty about their choices. ‘Experience consumption,’ which is the recent trend in consumption, is another reason. Experience consumption emphasizes experiences like “Have you ever done this before?” rather than “Do you have it?”. In particular, people depend on reviews even more when they consume experiential products like foods, in which the value cannot be measured before tasting them. Moreover, they consider and trust reviews that do not seem to have mutual relationships with products as objective. It means that they prefer reviews that truly give information to consumers rather than advertisements from companies.

-Influence of Reviews and Ratings

Reviews and ratings affect the number of visits to restaurants. According to a report written by Jung Hyunghak, a Doctor of Entrepreneurship & Small Business at Soongsil University, the higher the review rates, the higher the intention to visit the restaurants becomes. He also found that the number of reviews only influences the visiting intentions of inexpensive restaurants, not expensive ones. Moreover, consumers are more easily affected by negative reviews than positive ones since they perceive negative information as more valuable. As reviews are highly influential to stores, many stores are trying to manage reviews to get higher ratings and raise positive awareness. They use review events as a type of marketing, which provides additional services when consumers promise to write positive comments. Overall, the reviews are not exercised positively due to the increase of manipulative control and fake reviews through review agencies.

Problems Regarding Online Reviews and Ratings

-Frequent Fake Reviews Declining Credibility

Since the amount and rating of reviews exert influence on buying products and services, people abuse the review systems. Unlike the original purpose where ‘experienced’ consumers provide information to other potential consumers, fake reviews from ‘inexperienced’ consumers have appeared. According to Baemin, a food delivery platform, 70 thousand fake reviews were discovered in the first half of 2020. Compared to only 20 thousand fake reviews were in 2019, it is a severe and growing problem. Fake reviews can be divided into two kinds: one is to increase a store’s rating, eventually encouraging potential customers to visit. The other is to decrease the ratings of rival stores. To write reviews, orders must first come in; therefore, restaurants hire review agencies to write fake reviews. The review agency pays money first and gets paid back the principal plus a fee from restaurants. However, if fake reviews become commonplace, consumers would need to check whether they are sincere whenever they buy something. If this phenomenon persists, people will no longer trust reviews, and as a result, the original purpose of review systems will be ruined.

A Baemin Poster for Making Credible Review Culture (mk.co.kr)

-A Fatal Malicious Review

Malicious reviews also frequently occur in diverse forms depending on respective situations. Maeil Shinmun reported about a malicious case where someone gave low scores to a restaurant because the owner’s handwriting in a note sent with the food was ugly, even though the food was delicious. Another person uploaded a photo where the food was dumped in a toilet. Situations where restaurants received malicious comments because they did not satisfy consumers’ unreasonable demands also occur frequently. For example, someone asked for a double portion of fried rice for their child’s birthday even though they only paid for one. When the restaurant refused the demand, they gave the restaurant only a one-star rating even though their request exceeded the service scope. Nevertheless, most restaurants relent to customers’ unreasonable demands to avoid malicious reviews. They even apologize and compensate customers even though it is not their fault. These malicious reviews inflict damage on restaurants since consumers feel more sensitive to negative comments, and the amount and rate of reviews are important for a restaurant. Especially due to COVID-19, the competition between restaurants is more severe nowadays, so malicious reviews will be fatal to small restaurants.

Changes in the Review System

-Improving Systems Through AI and Big Data

1 l Block Fake Reviews

Review sections have slowly been changing. Many companies are using artificial intelligence (AI) to deter widespread fake reviews on websites. There were inspections using AI in the past, but they could only censor slang or filter out fake food photos. Also, they could only deal with uploaded reviews after the fact. To solve this issue, a real-time monitoring AI system was developed so that it analyzes if comments contain any fake information as soon as consumers write reviews and post them. When reviews are suspected as fake, they will not be posted on the sites. For example, Baemin temporarily blocks posts while a review is under examination. After further inspection by professionals, it is then decided whether the review will be blocked or revealed within 24 hours.

A System That Handles Fake Reviews (yna.co.kr)

2 l Provide Detailed Consumer Reviews

Reviews AI and big data are not only helpful in minimizing problems like blocking fake reviews. They can also maximize positive aspects by providing objective and detailed information to consumers. The present review systems are somewhat abstract since they are only composed of overall rating scores out of five stars and consumer comments. However, suppose companies subdivide evaluations into various categories like the taste, quantity, price, and revisit intention with visualized quantified data. They would be able to provide more detailed and objective information than before. For example, Membership Check, which is a restaurant review application, can make detailed evaluations possible through subdivided categories. Furthermore, based on information on a specific category, systems can recommend restaurants that suit consumers’ tastes. In that case, consumers can receive objective information. If the app recommends restaurants that perfectly suit one’s type, their time spent searching for restaurants will decrease. In the case of restaurants, they can analyze customer satisfaction in various aspects specifically and identify areas to improve. Lastly, a platform can gain credibility from both consumers and restaurants. Therefore, the number of users will increase, being more favorable to a particular platform.

Membership Check Application (hankyung.com)

-The Advent of New Review Systems?

While some ways to improve existing review systems using AI and big data were evaluated above, there is also a new wave coming with a different review system. Naver recently announced that they would remove rating systems altogether. Instead, Tag Cloud, which visualizes restaurants’ keywords with hashtags, will be implemented using AI. Review sites usually filled with only short comments will be reformed to check comments of subscribed reviewers. The new review system might solve problems that the original review systems had, like malicious reviews with low rates. Moreover, it appears that it goes beyond the fundamental limits of ratings. According to the Hankyoreh, a daily newspaper, some experts said that evaluating restaurants by star ratings does not help improve eating culture, and the rating loses its authority as reviews are subjective. While the original review system was just for evaluation, the new system will be redefined as a space that can record and share individuals’ types.

Naver Hashtag Cloud (hani.co.kr)

Review systems have been improved continuously by using AI and big data or reorganizing systems. As the changes have only begun, it cannot yet be predicted how the changes will affect us, whether positive or not. Nevertheless, it is expected that efforts for establishing better review spaces will be reflected in results.

백지은  ji0618love@g.skku.edu

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