B2C E-Commerce Websites Evaluation System on Users Experience Basis

B2C E-Commerce Websites Evaluation System on Users’ Experience Basis

1

Li Xiuli,

2

Zhao Rui,

3

Xiao Yan

1, First Author

Hebei Normal University of Science&Technology, Fiance&Economics College,

Qinhuangdao, Hebei Province, 066004, E-mail:

[email protected]

*2,Corresponding Author

Hebei Normal University of Science&Technology,Fiance&Economics

College, Qinhuangdao, Hebei Province, 066004, E-mail:[email protected]

3,

Hebei Normal University of Science&Technology,Fiance&Economics College,

Qinhuangdao, Hebei Province, 066004

Abstract

With rapid development of China's internet which has entered a new era, honesty becomes more and more important and B2C website pattern reflects the inherent advantages.The emergence of numerous B2C websites has expanded the scope of consumers’ choice.But in the process of operating, these sites kind of ignore the user feeling and could not fully meet the various needs of users when they are shopping. This article from the users’ experience perspective,using content analysis,and process hierarchy analytic to analysis and research on the website evaluation of B2C e-commerce,and establish the evaluation index in order to communicate the requirement between consumers and website managers, and achieve the objective that consumers could find their appropriate website easily through the website evaluation of B2C e-commerce.

Keywords

: E-Commerce Website Evaluation,Users’Experience,B2C

1. Introduction

According to the 27th Internet Development Statistics Report of China,the numbers of China's Internet users reached 457 million at end of December 2011, aaccounted for 23.2% of the total global internet users and 55.4% of the total number of internet users in Asia.[1] Online shopping user scale bucked the trend of the context of the economic slowdown and reaching more than 100 million. E-business internet applications have become the fast growing main force of internet economies in China. The increase of 48.6% year-on-year number of users of online shopping and the annual growth rate of 45.8% and 48.2% online payment and online banking are more than other types of network applications. [2]

The first step of users’ online shopping is started by through the browse of the website the brand. Whether the website design (layout, color) is beautiful, products classification is clear, product images could attract the eye of users, the shopping process is reasonable and simple, these first time login experiences could directly affect whether users would make orders or not. After deciding to make orders, the experience will be reflected in the speed of logistics and product quality. So the user experience throughout the whole process of e-commerce.

With the development of online shopping,there has new opportunities for B2C,various comprehensive B2C mall have emerged, a typical examples are Dangdang, Jogo. Besides, other on-line shops such as Jingdong, Xindan China, Fanke Chengpin, Hong Haizi and No.1 shop are also developing rapidly.B2C(Business-to-Consumer)E-commerce mode is commonly referred to as commercial retail, companies sale products or services directly to consumers .These forms of e-commerce generally rely on network retail, mainly via internet to conduct online sales. [3] E-e-commerce website evaluation not only make rapid development of their own, and promote the overall level of e-commerce sites and improve the quality of evaluation activities, then promote the healthy development of e-commerce.

Dangdang and Joyo as a typical representative of B2C websites, occupies a significant market share in e-commerce B2C model industry and has a very strong appeal. By comparing the websites, Dangdang and Joyo these two websites both have similar size, a longer history, similar strength and operating content. For the research of this article, the above two websites are highly comparable, thus this article select Dangdang and Joyo to study the B2C evaluation system.

2. Determination of B2C e-commerce site evaluation model

This article chooses the typical domestic B2C websites, Dangdang and Joyo, to study the evaluation system of B2C from the point of view of users’ experience.

2.1. Sampling

2.1.1. Sample selection

This paper mainly study Dangdang and Joyo, use to the comments of users to Dangdang and Joyo that use B2C model. Through multi comprehensive comparison, finally select the content of a third-party website comment forum to study. To ensure the fairness and credibility of the data, the selected data information is derived from the three types of websites:1)Traditional professional comment websites, including Public comment, Word-of-mouth network, and I love shopping network, Beneficial supplier review Network, Brief Comment net ,Dele network, Point sites these seven sites.2)The rebates site reviews forum. This rebate model could make the site gathered members of online shopping, to receive comprehensive comments.This article selects Tesco, Rebate network, Xungou these three shopping networks comments forum as data sampling. 3)Consumers Network 12315.com.

2.1.2. Data collection

This study mainly focuses on the latest comments, collect users’ reviews from vicarious destination sites to Dangdang and Joyo from December 2011 to May 2012. After preliminary organization, select comprehensive comments rather than the reviews that not comply with the spirit of this study. Through this series of data filters, 817 customer reviews finally incorporated into subsequent coding and data analysis.

Table 1. Frequency distribution table

Dimension Index Frequency Proportion Sequence

Commodity indexes Types of goods 113 13.83% 6

Commodity price 287 35.13% 1

The quality of goods 178 21.79% 2

Packaging of goods 76 9.30% 8

Update rate 153 18.73% 3

Subtotal 807 98.78% —

Trading Indicators Navigation system 4 0.49% 20

Search function 13 1.59% 17

Payment method 142 17.38% 5

Online order tracking 45 5.51% 10

Order processing speed 98 11.99% 7

Delivery services 150 18.36% 4

Subtotal 497 60.83% —

Customized indicators Account Management 40 4.90% 11

Customized services 11 1.35% 18

After-sales service 59 7.22% 9

Returns 38 4.65% 12

Promotions 30 3.67% 13

Subtotal 178 21.79% —

Interaction indicators Consultation Feedback 22 2.69% 14

Live Support 16 1.96% 15

Friendly interface 8 0.98% 19

Privacy Security 15 1.84% 16

Subtotal 61 7.47% —

2.2. Data analysis and conclusions

2.2.1. Quantitative calculation

Users pay attention to different indicators, specific attentions are shown in Table 1 frequency distribution table. There has positive correlation between the proportion of indicators and users’ attention. This reflects the importance of the links of e-commerce sites on user's perspective. For example, 287 of 817 comments mentioned commodity prices, so the frequency of this indicator is 287, the proportion of 287/817 = 35.13%,which is the highest number of all mentioned indicators mentioned. It is worth noting that the contents of each one of the reviews may involve multiple indicators. The frequencies are unknown, that is, the sums of the frequencies does not equal to the total number 817.

2.2.2. Data analysis

There are 807 commodities indicators in the total of 817 comments, account for 98.78%;497 trading indicators, account for 60.83%; 178 customized indicators, account for 21.79%, 61 interaction indicators ", account for 7.47%. The difference between frequency distribution of the four dimensions are large, which means that the attention of the customers to B2C website is relatively high, while for some other aspects is lower.The specific analysis of the data is as follows:

1)Commodity indexes

As can be seen from the data in Table 1, the customer commodity indexes are the most concerned by customers and there are 807 comments reached 98.78% of the total. 35.13% of the customers concerned about commodity prices, and another 178 indicators involve the quality of goods, account for 21.79% of the total number of customers.The site update speed indicators just lower than his quality of the goods indicator, 153 comments, and the ratio of 18.73% to be the third. This shows that customers are very concerned about the speed of website news update, concerned about the speed of the update rate of new products; our frequently updated website would be more dynamic and more likely to attract the attention of customers. Besides, factors such as the types of goods and merchandise packaging indicators are also important that whether consumers choose the site to shopping or not.

2)Trading Indicators

From B2C commerce site survey for dimension frequency distribution point of view, more than half of the customers mentioned in the comments of this indicator. The proportions of distribution service and payment method are high. This indicate that customers are more concern about the various payment methods and standardized distribution services.Thus, businesses should be concerned about and actively improve these services. 11.99% of the customers are very concerned about order processing speed indicators, this indicator to a certain extent reflect the efficiency of the site, customers would complain if the order processing speed is slow. On the contrast, customers would be pleased if the speed is fast. Order processing speed would greatly influence the customers shopping experience. Online order tracking indicator is to be tenth, and 45 comments involve this indicator, account for 5.51% of the proportion of customers. Order website online tracking embodies the websites are professional and real-time. High transparency website shopping process would improve the psychological experience of the customer

3)Customized indicators

After-sales service indicator has 59 comments and a ratio of 7.22% to be the ninth. Returns indicator has 38 comments and the proportion of 4.65% to be the twelfth. Customers’ concern about these two indicators show that customers pay attention to the online product quality problems and not 100 percent trust after-sales, which is a current common problem for e-commerce. Preferential service indicators ratio of 3.67% which has 30 comments to be the thirteenth of the list, businesses could more effort in promotions to increase the types of concessions promotions, improve preferential practicality and customer loyalty, attract customers and retain customers. Customized services index is only involve 11 comments, account for 1.35% of the proportion and to be the eighteenth of the list, seems the customers do not care site customization services as well as custom effects in the subconscious.

4)Interaction indicators

This indicator involves a total of 61 comments, account for 7.47%, which is a lower rate. The indicator consultation feedback involves a total of 22 comments, accounting for 2.69%, ranked fourteen. Real-time support has 16 indicators, account for 1.96%. Part of the customers concern about

the advisory of B2C website feedback indicate that customers hope they could quickly contact customer service to solve problems they meet, and hope the website could reply their messages promptly. The website should do the consultation feedback; inquiry is a vital part of the process of the sale. Second, the Privacy security, interface friendly these two indicators are lower proportion of 1.84% and 0.98% respectively. It shows that customers generally do not worry about privacy and security, the existing interface does not affect their shopping experience, to some extent, the electronic commerce in China in terms of privacy protection and network property safety technology gradually mature after a few years of development and the IT industry has made great progress.

3. B2C e-commerce site evaluation systems

3.1. Use Analytic Hierarchy Process (AHP) to calculate the index weight.

Evaluation indexes are shown in Table 2. Following commodity indexes A1, for example, is a application that demonstrates the Analytic Hierarchy Process.

Table 2. B2C e-commerce websites evaluation system

Dimension Evaluation indexes

Commodity indexes(A1) Types of goods(B1)

Commodity price(B2) The quality of goods(B3)

Packing of goods(B4) Update rate(B5)

Trading Indicators(A2) Navigation system(B6)

Search function(B7) Payment method(B8) Order processing speed(B9) Order processing speed(B10)

Delivery services(B11)

Customized indicators(A3) Account Management(B12)

Customized services(B13) After-sales service(B14)

returns(B15) Promotions(B16)

Interaction indicators(A4) Consultation Feedback(B17)

Live Support(B18) Friendly interface(B19)

Privacy Security(B20)

The calculation procedure is as follows:

Calculate the product of each line element of judgment matrix B

M

_i:

M

_i= n j j=1

b



,i=1,2,3…n (4-1) Calculate each line n th root of

M

_i:

i

W

=n

i

M

,i=1,2,3…n (4-2) in the formula,n is theMatrix order;

Normalized Vector





1, 2, ,

T n

W W W ,the formula is as follows:

W

_i= n i j j=1

W



W

(4-3) i

3.1.1. Establish judgment matrix

In this paper, indirect conversion method of judgment matrix is used.Its features are: When compared importance of making the two elements, people most likely to give three scales determines instead of nine marks determine which is originally hard to be provided. For two levels of evaluation, the first dimension indicators, for example, the combination of the above content analysis research, we can see that the commodity indexes (A1) is more important than trading indicators (A2) , customized indicators (A3) is less important than the above two ,while interaction indicators (A4) is the least important.

And so on, to establish a the indicators judgment matrix, as shown in Table 3.

Table 3. An indicator matrix

1 B1 B2 B3 B4

B1 1 3 4 5

B2 1/3 1 3 4

B3 1/4 1/3 1 2

B4 1/5 1/4 1/2 1

This judgment matrix could be simplified: B=

1 3 4 5 1 / 3 1 3 4 1 / 4 1 / 3 1 2 1 / 5 1 / 4 1 / 2 1            

Through the formula 4-1 could calculate:

M

₁=60,

M

₂=4,

M

₃=1/6,

M

₄=1/40;

Through the formula 4-1 could calculate:W₁=2.783,W₂ =1.414,W₃=0.639,W₄=0.398; Through the above calculation and formula 4-3 could calculate the weight of each index were:

1

W =0.53,W₂=0.27,W₃=0.12,W₄=0.08. the biggest characteristic root



_max=4.1145.

3.1.2. Consistency test

Construct good judgment matrix consistency, according to the relative weight of the judgment matrix calculation for the various elements of a criterion level test.Although judgment matrix does not require the consistency when construct, but the judge deviated from consistency over large is also not allowed.Therefore, the judgment matrix consistency test is necessary. RI is the average random consistency index, which based on sufficient calculation of randomly judgment matrix. “n” is the order of bands judgment matrix. [5-7]

Table 4. RIvalue table

n 1 2 3 4 5 6 7 8 9 10

RI 0.00 0.00 0.58 0.90 1.12 1.24 1.32 1.41 1.45 1.49

Generally the smaller

CI

, the better consistency of judgment matrix, usually

CI

<0.1, the judgment matrix has satisfactory consistency. The consistency test calculation is shown below

Consistency index

CI

= max-n

n-1

 ₌4.1145 4 4 1



 =0.038

Proportion of Random Consistency

CR

=

CR

RI

=

0.038

0.9

=0.042<0.1

No logic error that the judgment matrix has satisfactory consistency, the right weight.

3.2. Conclusion

After a series of operations, the specific results are shown in Table 5. As a result, we get the B2C e-commerce websites evaluation system and index weights.

Table 5. B2C e-commerce websites evaluation system

First level indicators Second level indicators Combining weight

A1 Commodity indexes(0.53) B1 Types of goods (0.12) 0.064

B2 Commodity price (0.46) 0.244

B3 The quality of goods (0.18) 0.095

B4 Packaging of goods (0.07) 0.037

B5 Update rate (0.17) 0.090

A2 Trading Indicators(0.27) B6 Navigation system (0.08) 0.022

B7 Search function (0.07) 0.019

B8 Payment (0.28) 0.076

B9 Order online tracking (0.09) 0.024

B10 Processing speed (0.17) 0.046

B11 Distribution services (0.31) 0.084

A3 Customized indicators (0.12) B12 Account Management (0.20) 0.024

B13 Customized services (0.07) 0.008

B14 After-sales service (0.34) 0.041

B15 Returns (0.22) 0.026

B16 Promotions (0.17) 0.020

A4 Interaction indicators (0.08) B17 Consultation Feedback (0.36) 0.029

B18 Live Support (0.27) 0.022

B19 Friendly interface (0.11) 0.009

B20 Privacy Security (0.26) 0.021

Notes: synthesis weights for the corresponding index weights at all levels of the plot

4. Empirical Analysis of the evaluation system of e-commerce sites

4.1. Collect data

First select 30 users of Dangdang and Joyo respectively, to vote for the actual situation of the website. The result of the voting is as follows:

Table 6. Dangdang voting results table

Factor good OK medium Poor worse

Commodity indexes

Types of goods 15 8 5 2 0

Commodity price 7 14 6 3 0

The quality of goods 15 7 6 2 0

Commodity indexes Packaging of goods 6 10 10 2 2 Update rate 16 11 2 1 0 Trading Indicators Navigation system 6 11 9 4 0 Search function 3 18 7 2 0 Payment method 9 14 6 1 0

Online order tracking 2 16 12 0 0

Order processing speed 4 12 10 2 2

Distribution services 3 17 5 4 1 Customized indicators Account Management 1 21 6 2 0 After-sales service 0 6 19 5 0 returns 8 10 5 6 1 Promotions 5 17 3 5 0 Interaction indicators Consultation Feedback 4 16 9 1 0 Live Support 10 10 9 1 0 Friendly interface 16 8 6 0 0 Privacy Security 19 7 4 0 0

Table 7. Joyo voting results table

Factor Good OK Medium Poor Worse

Commodity indexes

Types of goods 13 9 5 3 0

Commodity price 17 8 4 1 0

The quality of goods 11 14 1 4 0

Packaging of goods 21 4 3 2 0 Update rate 6 14 6 4 0 Trading Indicators Navigation system 10 11 2 7 0 Search function 15 12 2 1 0 Payment method 16 10 2 2 0

Online order tracking 2 9 10 5 4

Order processing speed 5 18 4 3 0

Distribution services 7 13 4 6 0 Customized indicators Account Management 12 6 8 4 0 Customized services 3 15 11 1 0 After-sales service 1 5 19 5 0 Returns 3 8 13 4 2 Promotions 12 15 1 2 0 teraction indicators Consultation Feedback 13 5 8 4 0 Live Support 12 11 6 1 0 Friendly interface 7 12 8 3 0 Privacy Security 10 12 8 0 0

4.2. Empirical Analysis

The figures in the table represents voters’ number First, the results of the second layer indicators constitute the first level of fuzzy matrix and then carry out fuzzy comprehensive evaluation of the first layer. Ultimately could get much more level fuzzy comprehensive evaluation results. The detailed calculations are shown below[8-10].

The commodity indicators two factor, for example, according to the statistical results of Table 5 Dangdang combined B1, B2, B3, B4, B5 three secondary factor weights (0.12,0.46,0.18,0.07,0.17), can calculate the set of commodity indexes A1[4]



r r r r r

11, 12, 13, 14, 15



= (0.12,0.46,0.18,0.07,0.17) 15 / 30 8 / 30 5 / 30 2 / 30 0 7 / 30 14 / 30 6 / 30 3 / 30 0 15 / 30 7 / 30 6 / 30 2 / 30 0 6 / 30 10 / 30 10 / 30 2 / 30 2 / 30 16 / 30 11 / 30 2 / 30 1 / 30 0                 =(0.362,0.374,0.183,0.076,0.005)

Using the same method, you can calculate the remaining three an alternative set of indicators, the second layer indicators fuzzy relation matrix R:

R= -3 0.362 0.374 0.183 0.076 0.005 0.167 0.494 0.241 0.077 0.022 0.096 0.387 0.360 0.149 7.333 10 0.361 0.372 0.246 0.021 0.090             

According to Table 5, A1, A2, A3, and A4 of the right weight, we can get the final fuzzy evaluation results: B=A



R=(0.53,0.27,0.12,0.08) -3 0.362 0.374 0.183 0.076 0.005 0.167 0.494 0.241 0.077 0.022 0.096 0.387 0.360 0.149 7.333 10 0.361 0.372 0.246 0.021 0.090              =(0.277,0.408,0.225,0.081,0.016)

27.7% of users believe that Dangdang's competitiveness is "good"; 40.8% of users believe that Dangdang's competitiveness is "OK”; 22.5% of users think Dangdang competitiveness is "medium”; 8.1% of users believe that Dangdang's competitiveness is "poor"; 1.6% of users believe that Dangdang's competitiveness is "very poor". Dangdang’s good comment rate achieves 68.5%.

Joyo, the same results can be obtained for Table 5-2: (0.383,0.345,0.169,0.098,5.0 10 -3₎

The results could be interpreted as:

38.3% of users believe that Joyo competitiveness is "good"; 34.5% of users believe that Joyo competitiveness is "OK"; 16.9% of users believe that the Joyo competitiveness is "medium”; 9.8% of users believe that Joyo competitiveness is "poor";

0.005% of users think Joyo competitiveness is "worse". Joyo's praise rate reached 72.8%.

According to the data above, we can see that Joyo’s comprehensive competitiveness of the user experience is slightly above Dangdang.

With the popularity of the Internet, people transfer more and more business activities on the network. The influence of E-commerce businesses, service capabilities and further enhance. People visit the site is not just find information like they use traditional site, but to carry out trading and trading of capital and goods, however, the asymmetry of information bring the user's shopping potential risks. Because of this particular character, analysis of e-commerce site features and interactive behaviors is required, and make the following work of evaluation of e-commerce site more targeted.

5. References

[1] Patron, "Automatic Support for Usability Evaluation", IEEE Trans,S-oftware Energy, No.24,pp.863-887,2009

[2] Neuendorf K.A, "The Content Analysis Guidebook", Thousand Oaks:S-age Publications, No.28,2008.

[3] Helge Clausen, "Evaluation of Library Web Sites:the Danish Case",The Electronic Library, No.2,pp.83-87,2009

[4] Zhangliang, " Users’ experience of website optimize in e-commerce optimization ",Computer Knowledge and Technology,No.10,pp.8480-8484,2010

[5] Zhang Yangyang,Yuan Yuyu, "Analytic Hierarchy Process analysis of e-commerce software usability evaluation ",Research of Computers Application,No.4,pp.104-107, 118, 2009

[6] Dan Zhang, Xiaoqing Zeng, Wei He, "Research on Customer Satisfaction of E-Commerce Website with Uncertain Linguistic Variables", JCIT: Journal of Convergence Information Technology, Vol. 7, No. 1, pp. 165 -171, 2012

[7] Wenying Tian, Lihua Wang, Hengjie Zhang, "Using Cloud Computing to Build E-Commerce Recommendation Platform", JDCTA: International Journal of Digital Content Technology and its Applications, Vol. 6, No. 12, pp. 391- 398, 2012

[8] M. Riad, Q. F. Hassan, "Service-Oriented Architecture-A New Alternative to Traditional Integration Methods in B2B Applications", JCIT: Journal of Convergence Information Technology, Vol. 3, No. 1, pp.31-41, 2008

[9] Ashwin B.K, Kumaran K, Madhu Vishwanatham V, M Sumaithri, "A Secured Web Services Based E-Commerce Model for SMME Using Digital Identity", IJACT: International Journal of Advancements in Computing Technology, Vol. 2, No. 2, pp. 79-87, 2010

[10]Duolin Liu, "E-commerce System Security Assessment Based on Grey Relational Analysis Comprehensive Evaluation", JDCTA: International Journal of Digital Content Technology and its Applications, Vol. 5, No. 10, pp. 279 -284, 2011