HKUST Business School Magazine

Biz@HKUST Biz@HKUST 46 47 // Cover // Insight Data and Reputation Building on Online Platforms Studies show online reviews can improve market efficiency and consumer welfare. Assistant Professor HUANG Yangguang Department of Economics, HKUST Business School Reputation systems operated by online platforms play an important role in mitigating the problems caused by asymmetric information in internet marketplaces. Successful reputation systems can crowdsource information from consumers and provide a large volume of reviews about product quality, attributes, seller credibility, and so on. Such review data may take the form of ratings, stars, textual comments, badges, or recommended lists. One important reason for the great success of leading e-commerce platforms, such as Amazon, eBay, and Taobao, is their ability to alleviate asymmetric information by their reputation systems. Reviews recorded by online platforms are valuable public goods that greatly improve market efficiency and consumer welfare. For example, scholars have found that online review platforms improve the welfare of restaurant consumers by US$2.50 per person per meal. 1 The Effectiveness of Reputation Systems The effectiveness of reputation systems depends on two key conditions. First, on the consumer side, reviews are mostly user- generated content and likely to be underprovided due to the nature of public goods. Reviews may suffer from various credibility problems such as loopholes, fraud, manipulation, and bias. Even if reviews are truthful, the information in them usually includes a lot of noise with regards to assessments of quality. For some markets, reviews written by consumers are based on subjective experiences and may not be applicable to consumers with heterogeneous tastes. For example, many Chinese people do not trust the ratings of Chinese restaurants on Yelp because most reviews are provided by non-Chinese people. In some markets, the nature of credence goods can hinder consumers from leaving informative reviews and learning from others' experiences. Hence, the design of the reputation system and quality transparency greatly affect how much consumers can learn from reviews. Second, on the firm side, a successful reputation system must provide firms with a sufficiently strong dynamic incentive to build a reputation by accumulating reviews. Firms must be eager to improve their quality and maintain good services because they expect to gain benefits from obtaining good reviews. For example, eBay sellers have a strong incentive to compete for the “eBay Top Rated Seller” badge because consumers are willing to pay an average of 8.1% more to these reputable sellers. 2 However, intense competition can slow down review accumulation and reduce the reputation premium, which weakens the firm’s dynamic incentive of building a reputation. Sketch of the Theory Consider that a market consists of a mixture of high-quality and low- quality firms. Later-stage consumers learn about quality from reviews provided by earlier-stage consumers through a quality inference function that maps reviews into beliefs about quality. A high-quality firm will adopt an increasing price path because the low initial price leads to more favorable information being released through earlier-stage sales. As a result, the firm will have a higher perceived quality and can increase its price and enjoy the reputation premium in later stages. In contrast, a low-quality firm will employ a declining price path because setting a higher price results in less negative reviews. The quality inference process of consumers depends on the volume and informativeness of review data. In a market with higher quality opaqueness, consumers update their beliefs by a smaller margin given the same number of reviews. Similarly, intense competition makes setting a low introductory price less effective in promoting sales and generating positive reviews. These two factors reduce high-quality firms' dynamic incentives to build their reputation. Therefore, quality opaqueness and intense competition slow the process of quality inference, and high-quality firms need a longer duration to build a good reputation. Specifically, in a more competitive market, or a market with a poor reputation system, a high-quality firm needs to maintain a low price for a longer duration before it can convince the market of its high quality. Empirical Evidence We tested the theory using data from Zaihang (www.zaih.com ), a Figure 1: Competitive Intensity and Market Outcome Figure 2: Quality Opaqueness and Market Outcome leading online consulting platform in China. Zaihang is a platform in which individuals can register as “experts” in certain areas and provide consulting services (e.g., career development, psychology, education ...) to clients. Consumers search for experts by their service categories and information on webpages of experts, ratings, and reviews left by previous clients. If a consumer wants to purchase the consulting service, he or she makes an appointment through Zaihang. After receiving the service, the client can leave a rating and review the consulting experience. Zaihang focuses on offline services, so the market is segregated into many small markets with different geographic locations and service categories. The intensity of competition and quality opaqueness vary across these markets, which enable us to study their influence on the effectiveness of the reputation system. We measured the competitiveness of a market by the number of experts providing service products. In Figure 1, groups 1, 2, 3, and 4 represent the first, second, third, and fourth quartiles, respectively, of the number of experts among the 553 markets. There is a clear pattern that, for both high-quality and low-quality experts, the sales and duration of the first product are higher for more competitive markets. Hence, in more competitive markets, experts wait longer to accumulate sales and reviews before they adjust prices. We used the average length of textual reviews per sale to measure quality opaqueness. In Figure 1, groups 1, 2, 3, and 4 correspond to the fourth, third, second, and first quartile of market opaqueness. Markets with higher quality opaqueness tend to have longer first-product duration, more first-product sales, and a smaller price difference between new and old products. If consumers in some markets have a low tendency to leave reviews, or only write short and uninformative reviews, after purchasing a product, then experts in these markets will not have a strong incentive to accumulate reviews to build their reputation. Hence, we observe the pattern that the process of reputation building is slower and less transparent. 1 Fang, Limin. “The effects of online review platforms on restaurant revenue, survival rate, consumer learning and welfare.” Management Science (2021). 2 Resnick, Paul, et al. “The value of reputation on eBay: A controlled experiment.” Experimental Economics (2006).