The effect of experienced buyers’ feedback on consumer behavior: Evidence from the largest online marketplace in China

Abstract

Fake online reviews are so prevalent that e-commerce platforms attempt to control it from affecting the trustworthiness between buyers and sellers. The issue has also attracted sporadic scholarly endeavor to understand this new field. To address this issue, we propose a new model to examine three interrelated stakeholders of e-Commerce platforms: experienced buyers, future buyers and the online sellers in terms of purchasing behaviors and sales with three objectives. Experienced buyers influence future consumers’ behaviors and increase sales from sellers. Using data collected from the largest online e-commerce platform in China, we test relevant hypotheses. Our findings show that experienced buyers and their positive reviews increase future buyers’ purchasing and promote corporate sales. These findings contribute knowledge to the online feedback mechanism and literature on fake review studies. This study also provides a novel method to help buyers avoid fake online review from a market structure perspective.

Keywords

Online feedback mechanism fake online reviews e-commerce experienced consumer reviews

1 Introduction

With the rapid development of global e-Commerce, Online feedback mechanism is an important component of electronic markets, helping build trust and elicit cooperation [1, 2]. Feedback profile affects online transactions and the popularity of an item [3 –6], which are also predictive of future performance of online shops [7, 8]. One important reason for buyers to use online feedback is the lack of trust in the seller [9, 10]. With online feedback mechanism, buyers can simplify their purchasing decisions with the help of online reviews [11]. That is, good reviews on online sellers mean good trust-building with buyers and recognitions to products from buyers. As a result, online feedback mechanism is critical for buyers and sellers.

Online reviews, as an important component of online feedback mechanism, play a crucial role in the relationship between online buyers and sellers. Online sellers rely on reviews to promote trust with buyers, especially with those that are new to the sellers [12]. In this study, online reviews are considered to have two types: One is made by first time or inexperienced buyers who have little online purchasing records. The other is left by buyers who have abundant online transaction records and are defined as “experienced buyers”. Many online reviews are made by the former since thoughtless comments can be made easily without facing any consequences [13]. Given this reality, such online reviews can actually offer only little value in sometime, as a reference to future buyers. Since online sellers have realized that the trust and trustworthiness are crucial to surviving the online market, they rely on valuable feedbacks to sustain them with consumers [14].

Due to these reasons, fake positive online reviews are prevalent, which is made by online sellers employing professional teams. For example, on March 15, 2016, China Central Television revealed that many sellers in large online transaction platforms had been using fake online reviews to mislead buyers. In response to this media revelation, Alibaba, the largest online platform with more than 800 million users in China, took action to penalize 2200,000 online shop owners for their dishonest behaviors.

Previous studies have explored the relationship between online operation condition and feedback mechanism [15]. Some findings show that positive online reviews can bring a higher transaction volume [16]. However, these studies have not classified different buyers into groups. Researchers should consider these group variations due to their distinct purchasing experience. Moreover, previous researchers have not considered the existence of fake online reviews, a serious issue in current online markets. Evidence shows that with the appearance of fake online reviews, the significance of prior findings decreases and gradually loses effectiveness [17]. Given the prevalence of fake online reviews, actions have been taken to prevent common buyers from this problem. For example, machine learning, especially supervised learning, is used to filter fake information [18 –22]. However, the learning object is always a problem. It is found that those fake online reviews used for labels are difficult to detect. For different transaction items, researchers need to change the learning model [23], which is complicated. In the meantime, despite the advancement of computational technology, it is still difficult for online buyers to distinguish fake reviews from online ratings, which are not filtered due to the limitation of techniques.

To contribute knowledge to this understudied field and offer solutions to reduce the negative effect of fake online reviews on buyers, we propose a new model to examine three interrelated stakeholders of e-commerce platforms: experienced buyers, future buyers and the online seller in terms of purchasing behaviors and sales. Specifically, we intend to achieve the following objective, i.e. “How experienced buyers influence future consumer behaviors and increase sales from the seller?” For precision in this research, experienced online buyers are defined as buyers that have had over 250 transactions online. It is often difficult for sellers to employ a great number of experienced buyers to provide fake positive online reviews because it is much more difficult to be an experienced buyer than just registering a new account used to make fake online reviews. Experienced buyers are skillful in selecting items and sellers online. They demonstrate ‘authority effect’ to comment on purchasing experience compared with inexperienced online buyers.

The remainder of this paper is organized as follows. Section 2 reviews related literatures and presents research hypotheses. Section 3 discusses data, measures and analytical techniques. Section 4 presents our model construction. Section 5 confirm or refute hypotheses. Finally, Section 6 discusses our contributions, implications for practice and research limitations.

2 Literature review and hypotheses development

Online feedback mechanism is an important resource of electronic markets, helping people build trust in electronic markets [1, 2]. Internet provide a platform for online review system in which individuals share views and experiences on a wide range of topics, including products, online booking services [24 –27], web services [28, 29], and even word events [30, 31]. Feedback profile can affects the situation of online transactions and the popularity of an item [3 –6], which are also predictive of future performance of online shops [7, 8].

Prior studies on different electronic market, such as eBay, Alibaba.com and Amazon.com, reach consistent conclusions [15]. Dellarocas [15] found that online feedback system had a wide impact on organizations and operation and management activities by studying online marketplaces (eBay.com). The most use-ful online feedback mechanism is online review. As the research further develops, it demonstrates that online reviews [33, 34] impact on both price [35] and the probability of sale [36 –39]. For online sellers, good feedbacks with buyers’ trustworthiness make them survive in online transaction platform and have their business much better [40]. In the aspect of buyers, these online reviews can support their decision making [41, 42], which are also an important indicator of online traders’ business capacity in online market [43].

With further research, good online reviews and the transaction volume increase to bring more profit to sellers, which also attract more buyers. Ye [16] found that the volume of historical transactions had significantly positive impact on the future sales for online sellers (i.e. a seller holding a higher number of historical transactions got more future purchasing). This finding was concluded based on the study, of two large online markets, eBay.com and Taobao.com. Ye [16] also found that buyers would always pay attention to sales history when choosing online shops. Some researchers also illustrate the direct relationship between the operation situation and online reviews. Ye [44] argued that a higher valence of average online positive reviews of hotel brought more sales and bookings. In a study of online retail, Wang [45] found that sellers with feedback grade above a certain change-point attracted more buyers thereby achieving higher sales in volume. Ghose [46] argued that higher readability of reviews was associated with higher sales. Therefore, in daily operations, online reviews directly and indirectly influence sellers and buyers, and affect future opportunities of transactions.

With the great sales brought by positive online feedbacks, fake reviews are also posted to make more profit [13 , 48]. According to the research on the behavior of restaurants on Yelp.com, Luca [49] found that Sellers seek to hired fakers who had not visited the hotel, creating fake reviews [23]. Hu [50] demonstrated that fake reviewers with an economic incentive to mimic truthful reviewers, it is a challenge for textual analysis methodologies to provide durable mechanisms for detecting fake reviews [51, 52]. Many sellers in Taobao.com (C2 C), also in its relative platform–Tmall.com(B2 C), have employed professional fakers to help them create a higher ratio of positive online reviews. Since the contents of online reviews help online buyers [53], these create impressions to buyers that the items in these online shops have good quality, leading a large number of purchasing. However, this is actually a misleading to buyers [54] and would increase buyers’ uncertainty [55]. The true quality of those goods may be different and the real feedback of those shops may be also negative. Thus, prior empirical findings of online feedback, may be invalid to a great extent and negative due to the existence of fake information [56 –58]. Zhao [58] asserted that the effect of more positive reviews and frequent reviews had smaller impact on buyer choice on online retailing platform with fake products reviews. It would undermine the efficiency and effectiveness of the online mechanism proposed previously and currently for overcoming information asymmetry in the electronic market [17]. Moreover, the transaction volume increases as online buyers are misled by fake online feedback, which makes the transaction volume, the index associated with the online feedback, loses its effect on buyers’ decision-making.

In order to control the quality of online reviews and prevent buyers from fake reviews, research has been conducted to reduce by filtering fake online reviews. From the perspective of technology, the supervised learning is a major attempt to solve this problem [18 –22]. However, due to lacking of the labeled fake online reviews when using supervised learning technology, it is difficult in data mining and training [45]. Nevertheless, such a kind of work ignores the connectivity structure of review data and it needs to collect different data to build distinct model for various domains [23]. Thus, researchers proposed several behavior features derived from collusion among fake online reviews. Many fake reviews are still not filtered. For fake group review, Mukherjee [59] attempted to use a mining method to find candidate groups, then built behavior model and relation model to detect fake review groups. Although these computational techniques prove to be a relatively effective method to defend fake online reviews, they are difficult for buyers to use and understand. In daily online transaction, buyers still rely on textual reviews to help decision-making although online markets use relevant technologies to filter fake online reviews.

With motivations to advance prior research and provide solutions for buyers to recognize fake online reviews, our study aims to demonstrate that buyers with abundant online purchasing experience and historical records, namely experienced buyers, can impact the purchasing decision of future buyers. In society, “authority effect” is a powerful social influence principle and common phenomenon [68]. Bal-dassarri [60] showed in the experiment that people would be responsive to legitimate authority. To make a choice between different physicians, Stasiuk [61] found that people might be biased on those physicians who could recommend more active medical mode, which would make patients consider a higher level of expertise, namely medical expertise bias. Bar-Tal [62] also proposed that the expertise authority had important implications for adherence to treatment. The authority effect also applies in the online environment. Lim [63] experimented that the authority messages would increase website users’ interests in the authority speakers. Professional reviews and ratings and sales are connected [64, 65]. For example, Zhou [66] found that professional reviews and ratings promoted software downloads, and according to Inglis’ research [67], researchers and students considered an argument more persuasive if it was associated with an expert (e.g. mathematician).

Based on great influence of the “authority effects” on daily life, we argue that it can impact on online transactions. Since experienced buyers have already made a large amount of online purchasing and gained considerable experience in the process, this paper considers them a symbol of authority for buyers online. Thus, an online shop with more positive reviews from experienced buyers may increase buyers’ interests and make them purchase items. Moreover, future online buyers can be more likely to purchase from sellers with a higher number of experienced buyers than with a lower number of experienced buyers or without experienced buyers. Therefore, hypotheses H1a and H1b involve two relationships: one is between the number of experienced buyers and sales; the other is between the positive reviews made by experienced buyers and sales. Since the authority effect is frequently used in advertising [68], accordingly, we propose in hypothesis H1c that the preference information of seller preferred by experienced buyers, which can be found on the web page, affects the transaction volume positively.

To identify this proposition, we use a dummy variable to represent whether sellers are preferred by experienced buyers and not. If the transaction volumes from sellers preferred by experienced buyers are higher than those from sellers not being preferred, then the effect of advertising of experienced buyers can be proved true. Therefore, we predict:

H1a: Future buyers are more likely to make more purchases, thereby generating more sales from the seller if an online shop has more experienced buyers.

H1b: Future buyers are likely to make more purchases, thereby generating more sales from the seller if an online shop has higher ratio of positive reviews made by experienced buyers.

H1c: Future buyers are more likely to choose online shops preferred by experienced buyers, thereby generating more sales from these sellers.

3 Data

3.1 Data description

To test the hypotheses, we retrieved data from Taobao.com, the largest online C2 C platform in China with 800 million registered users. After searching an item in Taobao.com, the page shows buyers from online sellers on Taobao.com. The web page shows the number of sellers preferred by experienced buyers. Buyers with more than 250 transactions online are defined as experienced buyers in this study. Figure 1 the page of sellers preferred by experienced buyers on Taobao.com and Tmall.com, where the red box in the first row shows sellers preferred by experienced buyers. Figure 2 shows the rank of buyer levels. From 2015, buyers can find those recommended sellers preferred by experienced buyers on the website of Taobao.com.

Fig. 1

Yellow-Diamond shops (i.e. sellers preferred by experienced buyers) on Taobao.com and Tmall.com.

Fig. 2

The rank of buyer levels (After achieving 251 transcations, namely more than 250 points, a buyer level becomes to one yellow diamond).

The selected products from Taobao.com is the facial mask, which is a kind of product revealed by CCTV most seriously in the investigation in 15.03.2016, misleading buyers and having the most behaviors of improper competition.

Taobao.com lists the historical sales for each item in the last 30 days on each item’s description page. Buyers therefore can sort the search results based on sales records. To analyze the effect of experienced buyers’ behavior on future buyers’ purchase decisions, we first retrieve transaction information from all sellers for each sample, for each sale of the targeted products between 26th December 2015 and 26th January 2016. The study period selected is also considered to avoid the peak transaction periods, i.e. ‘Double 11’ and “Double 12” shopping festivals (which equal to the Black Friday in United States), and the Spring festival. The selected period of time thus allows us to observe daily online market by eliminating the effect of special events mentioned. We argue that experienced buyers’ purchase history means the quality of product and the seller’s credibility to the buyers. In summary, we have included in our dataset a total of 260 shops on Taobao.com, that is, 130 products from sellers preferred by experienced buyers and 130 products from ordinary sellers for comparative analysis.

3.2 Variables

This paper adapts previous research as follows to reflect the changing nature of online shopping and unique Chinese environment. First, we do not use the historical transaction volume, as a self-relative variable in our re-search model. Since the fake online reviews are so common in the Chinese e-commerce platform, the positive reviews left by ordinary buyers can be mixed with fake online reviews. As a result, the volume of historical transaction may affect the data reliability. Second, we use the ratio of positive reviews made by experienced buyers but exclude default positive reviews. This is one step further from previous studies using ratio of all positive reviews and eliminating fake reviews. Third, we consider the score of description and service. The former illustrates buyers’ degree of satisfaction to the product quality. The service score consists of customer service and logistics service and illustrates buyers’ satisfaction to services in the entire purchasing process. The logistics service is equally important for online shopping although it is offshored by the third-party company. This is caused by that the seller should consider a quality company to provide buyers with good purchasing experience. There tends to be more complaints about the logistics service than customer service. Thus, we add the service score to the logistics score, and use the sum as a new variable. To identify the effect of the seller preference from experienced buyers on future buyers, we add a dummy variable to indicate whether a seller is preferred by experienced buyers. We also use the number of experienced buyers to identify whether the number of experienced buyers has affected the behavior of future buyers. Finally, we use the number of natural logarithms of some independent variables to ensure a normal distribution and build the linear regression model. Table 1 provides the description of variables used in this study. Table 2 demonstrates correlations among variables used in this study.

Table 1
Variables in analysis

Variable Description

Sales Current sales—number of sales of the product in past one month

Price The price of the product

Number The number of experienced buyers who have made a transaction in a shop

D₁ A dummy variable, which indicates whether a seller is preferred by experienced buyers

Seller

Ratio_positive The ratio of experienced buyers’ positive ratings and reviews

Description(des) The score of the difference grade of between seller description and the real products got by customers

Service The score of customers’ satisfactory grade to the pre-/past-sales services and the logistics services

Variable	Description
Sales	Current sales—number of sales of the product in past one month
Price	The price of the product
Number	The number of experienced buyers who have made a transaction in a shop
D₁	A dummy variable, which indicates whether a seller is preferred by experienced buyers
Seller
Ratio_positive	The ratio of experienced buyers’ positive ratings and reviews
Description(des)	The score of the difference grade of between seller description and the real products got by customers
Service	The score of customers’ satisfactory grade to the pre-/past-sales services and the logistics services

Table 2

Correlation between variables

	Sales	Price	Ratio_positive	Description (des)	Service	D₁	Number
Sales	1
Price	0.414***	1
Ratio_positive	0.256***	0.138***	1	1
Des	0.540***	0.219***	0.162***	1
Service	0.423***	0.150***	0.246***	0.459***	1
D₁	0.738***	0.324***	0.267***	0.459***	0.349***	1
Number	0.342***	0.033***	0.201***	0.232***	0.166***	0.298***	1

***p < 0.01; **p < 0.05; *p < 0.1.

4 Research models

Our study model controls for price, rating scores and use ratio of positive reviews as the online feedback volume and valence. To reflect the influence of online experienced buyers’ reviews, we develop the Eq. (1) to identify whether the behavior of experienced buyers influences the behavior of future buyers. We use a dummy variable in this equation to indicate whether a seller is preferred by experienced buyers. $\begin{matrix} ln {(sales)}_{i, t} = α_{0} + α_{1} ln {(price)}_{i, t} + \\ α_{2} D_{1_{i, t}} + α_{3} ln {(number)}_{i, t} \\ + β \cdot {Seller}_{i, t - 1} + ɛ_{i, t} # \end{matrix},$ (1) where α₀ is the constant term, α₁ is the coefficient of price, α₂ is the coefficient of the dummy variable, α₃ is the coefficient of the number of experienced buyers, and β is a vector containing three coefficients of ratio_positive, description and services respectively.

5 Statistical results

Table 3 reveals our findings based on Eq. (1). Firstly, the variable ln (ratio _ positive), the positive ratio of the online reviews made by experienced buyers is consistently positive and significant. This result indicates that buyers pay attention to the reviews provided by experienced buyers. When the value of this variable increases, the intention of purchasing products from the seller can be stronger. Thus, the ratio of positive online reviews made by experienced buyers can be a new indicator for future buyers to consider when making decisions. To some extent, this can prevent buyers from the negative effects made by fake reviews in online transaction environment since it is difficult for sellers to fake experienced buyers’ positive online reviews. In Model V, the dummy variable D₁ is not included because experienced buyers do not merely buy products from those sellers which are signed on the website, they may also buy products from other sellers in daily operations, therefore they have accumulated experience in online purchasing. Accordingly, when we check the effect of the number of experienced buyers on the purchasing behavior of future buyers, we should consider this reality and select the variable separately. The coefficient of ln (number) demonstrates that the number of experienced buyers has positively and significantly influenced the transaction volume. That is, the more orders made by experienced buyers, the more willingness future buyers have to buy products from the seller, leading increased sales in volume. Therefore, the findings support hypothesis H1a.

Table 3
The effect of experienced buyers on the sales of products

Dependent variable ln(Sales)

Model I Model II Model III Model IV Model V Model VI

constant 7.041* 4.519* 66.45* -87.00* -78.27* -46.07*

(174.42) (33.75) (-46.67) (-48.71) (-45.09) (-29.62)

ln(Price) -0.446* -0.417* -0.319* -0.312* -0.340* -0.188*

(-40.21) (-38.19) (-32.86) (-32.84) (-37.18) (-24.01)

ln(ratio_Positive) 4.033* 2.826* 2.212* 1.389* 0.550***

(19.71) (15.73) (12.35) (8.00) (3.78)

ln(description) 45.39* 37.70* 32.82* 19.73*

(50.02) (38.39) (34.36) (23.76)

ln(service) 14.59* 13.71* 8.867***

(18.4) (18.09) (13.86)

ln(number) 0.304*

(27.37)

D₁ 1.362*

(66.35)

N 7800 7799 7799 7799 7798 7799

R-Square 0.172 0.211 0.403 0.427 0.478 0.634

Dependent variable	ln(Sales)
constant	7.041***	4.519***	66.45***	-87.00***	-78.27***	-46.07***
	(174.42)	(33.75)	(-46.67)	(-48.71)	(-45.09)	(-29.62)
ln(Price)	-0.446***	-0.417***	-0.319***	-0.312***	-0.340***	-0.188***
	(-40.21)	(-38.19)	(-32.86)	(-32.84)	(-37.18)	(-24.01)
ln(ratio_Positive)		4.033***	2.826***	2.212***	1.389***	0.550***
		(19.71)	(15.73)	(12.35)	(8.00)	(3.78)
ln(description)			45.39***	37.70***	32.82***	19.73***
			(50.02)	(38.39)	(34.36)	(23.76)
ln(service)				14.59***	13.71***	8.867***
				(18.4)	(18.09)	(13.86)
ln(number)					0.304***
					(27.37)
D₁						1.362***
						(66.35)
N	7800	7799	7799	7799	7798	7799
R-Square	0.172	0.211	0.403	0.427	0.478	0.634

***p < 0.01; **p < 0.05; *p < 0.1.

Secondly, the coefficient of the ratio of positive reviews made by experienced buyers is consistently positive and significant. This finding suggests that a high ratio of positive reviews made by experienced buyers will indeed increase the sales volume. As a result, our findings support H1b.

Finally, the statistical power increases with the addition of each variable into the model suggesting the variables we included significantly impact on the volume of sales. In Model VI, the R-Square is 0.634 and the coefficient of the dummy variable is significantly positive. This result suggests that, when buyers know that a seller is preferred by experienced buyers, they are more willing to buy products from this seller rather than from elsewhere. As a result, sellers preferred by experienced buyers will get increment on sales volume of their products, supporting H1c. Indeed, the effect of experienced buyers has positive impact on the operation.

6 Discussion and conclusion

6.1 Major findings

This study aims at investigating the of experienced buyers’ online feedback effects on future buyers’ behavior and sales from online sellers. Through the empirical analysis, we have the following major findings.

First, the coefficient of ln (number) in Equation (1) demonstrates that the number of experienced buyers has positively and significantly influenced the transaction volume. That is, the more orders made by experienced buyers, the more willingness future buyers have to buy products from the seller, leading increased sales in volume. Therefore, the findings support hypothesis H1a. The future buyers are affected by the positive reviews left by experienced buyers and their behaviors. Experienced buyers’ positive reviews can promote daily sales volume. As a result, these features may increase sales of their products in volume, which can support hypothesis H1b. This result also suggests that, when buyers know that a seller is preferred by experienced buyers, they are more willing to buy products from this seller rather than from elsewhere, thereby supporting H1c.

6.2 Theoretical contributions

This study contributes to the online feedback mechanism and new literature fake reviews in two ways.

First, it contributes to the online reputation research by introducing a new online feedback review assessment method. The method provides a way to build a good trust-building with online buyers’ feedback, thus realizing the trust and trustworthiness for survival of online markets. Previous studies have explored the relationship between online operation condition and feedback mechanism [15]. Some findings show that positive online reviews can bring a higher transaction volume [16]. By contrast, in this study, we distinguish buyers with distinct online purchasing experience into different group, which is not considered by previous studies on online feedback mechanism. We demonstrate that the transaction volume is positively correlated with the ratio of positive reviews given by experienced buyers for sellers.

Second, this study tries to model online review without involving those fake online reviews from a market structure perspective, thus improving its predictive power on sales. Previous works in the literature focus on the effect of more positive reviews and frequent reviews measurement [58], but have paid little attention to fake online review, which is a serious issue in current online market. Online feedback mechanism is undermined by the manipulation of fake online reviews [52 , 55–58]. Evidence shows that with the appearance of fake online reviews, the significance of prior findings decreases and gradually loses effectiveness [17]. We reduce the fake online review influence by evaluating the quality of online transactions only based on experienced buyers’ reviews which are hard to deliberately produce. Even if a fake online comment is posted, reviews made by experienced buyers might tell the truth. Hence, the work indicates a new decision support research stream for overcoming online fake reviews.

6.3 Managerial contributions

This study contributes to managerial strategies by providing tools and suggestions. First, the work yields a good practical tool for business intelligence, realizes the trust and trustworthiness of online markets, and helps sustain trust typically in a coherent product category section. Therefore, employing the proposed online feedback measure can help sellers and buyers get a more accurate evaluation on products, and overcoming information asymmetry between online sellers and buyers.

As for the operation of online transaction platform, feedbacks left by experienced buyers can be collected for analysis. The data can be used for discovering differences between true reviews and fake reviews. It can also help online transaction platform recommend outstanding shops and sellers to buyers. In the long run, the e-Commerce platform can also accordingly establish rules of product quality and services to constraint online sellers.

Footnotes

Acknowledgment

The research work is supported by the Innovation Program of Humanities and Social Science Youth Fund of Ministry of Education under Grant No.13YJC630210, National Natural Science Foundation of China No.71402094, and National Natural Science Foundation of China, Project NO. 71961031.

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Dependent variable	ln(Sales)
	Model I	Model II	Model III	Model IV	Model V	Model VI
constant	7.041***	4.519***	66.45***	-87.00***	-78.27***	-46.07***
	(174.42)	(33.75)	(-46.67)	(-48.71)	(-45.09)	(-29.62)
ln(Price)	-0.446***	-0.417***	-0.319***	-0.312***	-0.340***	-0.188***
	(-40.21)	(-38.19)	(-32.86)	(-32.84)	(-37.18)	(-24.01)
ln(ratio_Positive)		4.033***	2.826***	2.212***	1.389***	0.550***
		(19.71)	(15.73)	(12.35)	(8.00)	(3.78)
ln(description)			45.39***	37.70***	32.82***	19.73***
			(50.02)	(38.39)	(34.36)	(23.76)
ln(service)				14.59***	13.71***	8.867***
				(18.4)	(18.09)	(13.86)
ln(number)					0.304***
					(27.37)
D₁						1.362***
						(66.35)
N	7800	7799	7799	7799	7798	7799
R-Square	0.172	0.211	0.403	0.427	0.478	0.634