B2C E-Commerce Websites Evaluation System on Users Experience Basis

Transcription

1 B2C E-Commerce Webstes Evaluaton System on Users Experence Bass 1 L Xul, 2 Zhao Ru, 3 Xao Yan 1, Frst Author Hebe Normal Unversty of Scence&Technology, Fance&Economcs College, Qnhuangdao, Hebe Provnce, , E-mal: lxull@yeah.net *2,Correspondng Author Hebe Normal Unversty of Scence&Technology,Fance&Economcs College, Qnhuangdao, Hebe Provnce, , E-mal:Zhaoru1631@126.com 3, Hebe Normal Unversty of Scence&Technology,Fance&Economcs College, Qnhuangdao, Hebe Provnce, Abstract Wth rapd development of Chna's nternet whch has entered a new era, honesty becomes more and more mportant and B2C webste pattern reflects the nherent advantages.the emergence of numerous B2C webstes has expanded the scope of consumers choce.but n the process of operatng, these stes knd of gnore the user feelng and could not fully meet the varous needs of users when they are shoppng. Ths artcle from the users experence perspectve,usng content analyss,and process herarchy analytc to analyss and research on the webste evaluaton of B2C e-commerce,and establsh the evaluaton ndex n order to communcate the requrement between consumers and webste managers, and acheve the objectve that consumers could fnd ther approprate webste easly through the webste evaluaton of B2C e-commerce. 1. Introducton Keywords: E-Commerce Webste Evaluaton,Users Experence,B2C Accordng to the 27th Internet Development Statstcs Report of Chna,the numbers of Chna's Internet users reached 457 mllon at end of December 2011, aaccounted for 23.2% of the total global nternet users and 55.4% of the total number of nternet users n Asa.[1] Onlne shoppng user scale bucked the trend of the context of the economc slowdown and reachng more than 100 mllon. E- busness nternet applcatons have become the fast growng man force of nternet economes n Chna. The ncrease of 48.6% year-on-year number of users of onlne shoppng and the annual growth rate of 45.8% and 48.2% onlne payment and onlne bankng are more than other types of network applcatons. [2] The frst step of users onlne shoppng s started by through the browse of the webste the brand. Whether the webste desgn (layout, color) s beautful, products classfcaton s clear, product mages could attract the eye of users, the shoppng process s reasonable and smple, these frst tme logn experences could drectly affect whether users would make orders or not. After decdng to make orders, the experence wll be reflected n the speed of logstcs and product qualty. So the user experence throughout the whole process of e-commerce. Wth the development of onlne shoppng,there has new opportuntes for B2C,varous comprehensve B2C mall have emerged, a typcal examples are Dangdang, Jogo. Besdes, other on-lne shops such as Jngdong, Xndan Chna, Fanke Chengpn, Hong Haz and No.1 shop are also developng rapdly.b2c(busness-to-consumer)e-commerce mode s commonly referred to as commercal retal, companes sale products or servces drectly to consumers.these forms of e- commerce generally rely on network retal, manly va nternet to conduct onlne sales. [3] E-commerce webste evaluaton not only make rapd development of ther own, and promote the overall level of e- commerce stes and mprove the qualty of evaluaton actvtes, then promote the healthy development of e-commerce. Dangdang and Joyo as a typcal representatve of B2C webstes, occupes a sgnfcant market share n e-commerce B2C model ndustry and has a very strong appeal. By comparng the webstes, Dangdang and Joyo these two webstes both have smlar sze, a longer hstory, smlar strength and operatng content. For the research of ths artcle, the above two webstes are hghly comparable, thus ths artcle select Dangdang and Joyo to study the B2C evaluaton system. Internatonal Journal of Advancements n Computng Technology(IJACT) Volume5,Number2,January 2013 do: /jact.vol5.ssue

2 2. Determnaton of B2C e-commerce ste evaluaton model Ths artcle chooses the typcal domestc B2C webstes, Dangdang and Joyo, to study the evaluaton system of B2C from the pont of vew of users experence Samplng Sample selecton Ths paper manly study Dangdang and Joyo, use to the comments of users to Dangdang and Joyo that use B2C model. Through mult comprehensve comparson, fnally select the content of a thrdparty webste comment forum to study. To ensure the farness and credblty of the data, the selected data nformaton s derved from the three types of webstes:1)tradtonal professonal comment webstes, ncludng Publc comment, Word-of-mouth network, and I love shoppng network, Benefcal suppler revew Network, Bref Comment net,dele network, Pont stes these seven stes.2)the rebates ste revews forum. Ths rebate model could make the ste gathered members of onlne shoppng, to receve comprehensve comments.ths artcle selects Tesco, Rebate network, Xungou these three shoppng networks comments forum as data samplng. 3)Consumers Network com Data collecton Ths study manly focuses on the latest comments, collect users revews from vcarous destnaton stes to Dangdang and Joyo from December 2011 to May After prelmnary organzaton, select comprehensve comments rather than the revews that not comply wth the sprt of ths study. Through ths seres of data flters, 817 customer revews fnally ncorporated nto subsequent codng and data analyss. Table 1. Frequency dstrbuton table Dmenson Index Frequency Proporton Sequence Commodty ndexes Types of goods % 6 Commodty prce % 1 The qualty of goods % 2 Packagng of goods % 8 Update rate % 3 Subtotal % Tradng Indcators Navgaton system % 20 Search functon % 17 Payment method % 5 Onlne order trackng % 10 Order processng speed % 7 Delvery servces % 4 Subtotal % Customzed ndcators Account Management % 11 Customzed servces % 18 After-sales servce % 9 Returns % 12 Promotons % 13 Subtotal % Interacton ndcators Consultaton Feedback % 14 Lve Support % 15 Frendly nterface % 19 Prvacy Securty % 16 Subtotal % Note: rato = frequency / total number of

3 2.2. Data analyss and conclusons Quanttatve calculaton Users pay attenton to dfferent ndcators, specfc attentons are shown n Table 1 frequency dstrbuton table. There has postve correlaton between the proporton of ndcators and users attenton. Ths reflects the mportance of the lnks of e-commerce stes on user's perspectve. For example, 287 of 817 comments mentoned commodty prces, so the frequency of ths ndcator s 287, the proporton of 287/817 = 35.13%,whch s the hghest number of all mentoned ndcators mentoned. It s worth notng that the contents of each one of the revews may nvolve multple ndcators. The frequences are unknown, that s, the sums of the frequences does not equal to the total number Data analyss There are 807 commodtes ndcators n the total of 817 comments, account for 98.78%;497 tradng ndcators, account for 60.83%; 178 customzed ndcators, account for 21.79%, 61 nteracton ndcators ", account for 7.47%. The dfference between frequency dstrbuton of the four dmensons are large, whch means that the attenton of the customers to B2C webste s relatvely hgh, whle for some other aspects s lower.the specfc analyss of the data s as follows: 1)Commodty ndexes As can be seen from the data n Table 1, the customer commodty ndexes are the most concerned by customers and there are 807 comments reached 98.78% of the total % of the customers concerned about commodty prces, and another 178 ndcators nvolve the qualty of goods, account for 21.79% of the total number of customers.the ste update speed ndcators just lower than hs qualty of the goods ndcator, 153 comments, and the rato of 18.73% to be the thrd. Ths shows that customers are very concerned about the speed of webste news update, concerned about the speed of the update rate of new products; our frequently updated webste would be more dynamc and more lkely to attract the attenton of customers. Besdes, factors such as the types of goods and merchandse packagng ndcators are also mportant that whether consumers choose the ste to shoppng or not. 2)Tradng Indcators From B2C commerce ste survey for dmenson frequency dstrbuton pont of vew, more than half of the customers mentoned n the comments of ths ndcator. The proportons of dstrbuton servce and payment method are hgh. Ths ndcate that customers are more concern about the varous payment methods and standardzed dstrbuton servces.thus, busnesses should be concerned about and actvely mprove these servces % of the customers are very concerned about order processng speed ndcators, ths ndcator to a certan extent reflect the effcency of the ste, customers would complan f the order processng speed s slow. On the contrast, customers would be pleased f the speed s fast. Order processng speed would greatly nfluence the customers shoppng experence. Onlne order trackng ndcator s to be tenth, and 45 comments nvolve ths ndcator, account for 5.51% of the proporton of customers. Order webste onlne trackng embodes the webstes are professonal and real-tme. Hgh transparency webste shoppng process would mprove the psychologcal experence of the customer 3)Customzed ndcators After-sales servce ndcator has 59 comments and a rato of 7.22% to be the nnth. Returns ndcator has 38 comments and the proporton of 4.65% to be the twelfth. Customers concern about these two ndcators show that customers pay attenton to the onlne product qualty problems and not 100 percent trust after-sales, whch s a current common problem for e-commerce. Preferental servce ndcators rato of 3.67% whch has 30 comments to be the thrteenth of the lst, busnesses could more effort n promotons to ncrease the types of concessons promotons, mprove preferental practcalty and customer loyalty, attract customers and retan customers. Customzed servces ndex s only nvolve 11 comments, account for 1.35% of the proporton and to be the eghteenth of the lst, seems the customers do not care ste customzaton servces as well as custom effects n the subconscous. 4)Interacton ndcators Ths ndcator nvolves a total of 61 comments, account for 7.47%, whch s a lower rate. The ndcator consultaton feedback nvolves a total of 22 comments, accountng for 2.69%, ranked fourteen. Real-tme support has 16 ndcators, account for 1.96%. Part of the customers concern about 565

4 the advsory of B2C webste feedback ndcate that customers hope they could quckly contact customer servce to solve problems they meet, and hope the webste could reply ther messages promptly. The webste should do the consultaton feedback; nqury s a vtal part of the process of the sale. Second, the Prvacy securty, nterface frendly these two ndcators are lower proporton of 1.84% and 0.98% respectvely. It shows that customers generally do not worry about prvacy and securty, the exstng nterface does not affect ther shoppng experence, to some extent, the electronc commerce n Chna n terms of prvacy protecton and network property safety technology gradually mature after a few years of development and the IT ndustry has made great progress. 3. B2C e-commerce ste evaluaton systems 3.1. Use Analytc Herarchy Process (AHP) to calculate the ndex weght. Evaluaton ndexes are shown n Table 2. Followng commodty ndexes A1, for example, s a applcaton that demonstrates the Analytc Herarchy Process. Table 2. B2C e-commerce webstes evaluaton system Dmenson Evaluaton ndexes Commodty ndexes(a1) Types of goods(b1) Commodty prce(b2) Tradng Indcators(A2) Customzed ndcators(a3) Interacton ndcators(a4) The calculaton procedure s as follows: Calculate the product of each lne element of judgment matrx B Calculate each lne n th root of M : M = W = n n n the formula,n s thematrx order; T Normalzed Vector W1, W2,, W n,the formula s as follows: W s Weght coeffcent of each ndexes. j=1 W = W The qualty of goods(b3) Packng of goods(b4) Update rate(b5) Navgaton system(b6) Search functon(b7) Payment method(b8) Order processng speed(b9) Order processng speed(b10) Delvery servces(b11) Account Management(B12) Customzed servces(b13) After-sales servce(b14) returns(b15) Promotons(B16) Consultaton Feedback(B17) Lve Support(B18) Frendly nterface(b19) Prvacy Securty(B20) b j M :,=1,2,3 n (4-1) M,=1,2,3 n (4-2) n W (4-3) j j=1 566

5 Establsh judgment matrx In ths paper, ndrect converson method of judgment matrx s used.its features are: When compared mportance of makng the two elements, people most lkely to gve three scales determnes nstead of nne marks determne whch s orgnally hard to be provded. For two levels of evaluaton, the frst dmenson ndcators, for example, the combnaton of the above content analyss research, we can see that the commodty ndexes (A1) s more mportant than tradng ndcators (A2), customzed ndcators (A3) s less mportant than the above two,whle nteracton ndcators (A4) s the least mportant. And so on, to establsh a the ndcators judgment matrx, as shown n Table 3. Table 3. An ndcator matrx 1 B1 B2 B3 B4 B B2 1/ B3 1/4 1/3 1 2 B4 1/5 1/4 1/ Ths judgment matrx could be smplfed: B= 1/ /4 1/ /5 1/4 1/2 1 Through the formula 4-1 could calculate: M =60, M =4, M =1/6, M =1/40; Through the formula 4-1 could calculate: W =2.783, W =1.414, W =0.639, W =0.398; Through the above calculaton and formula 4-3 could calculate the weght of each ndex were: W =0.53, W =0.27, W =0.12, W =0.08. the bggest characterstc root max = Consstency test Construct good judgment matrx consstency, accordng to the relatve weght of the judgment matrx calculaton for the varous elements of a crteron level test.although judgment matrx does not requre the consstency when construct, but the judge devated from consstency over large s also not allowed.therefore, the judgment matrx consstency test s necessary. RI s the average random consstency ndex, whch based on suffcent calculaton of randomly judgment matrx. n s the order of bands judgment matrx. [5-7] Table 4. RI value table n RI Generally the smaller CI, the better consstency of judgment matrx, usually CI <0.1, the judgment matrx has satsfactory consstency. The consstency test calculaton s shown below Consstency ndex CI -n = max = =0.038 n-1 41 Proporton of Random Consstency CR CR = RI = =0.042<0.1 No logc error that the judgment matrx has satsfactory consstency, the rght weght Concluson After a seres of operatons, the specfc results are shown n Table 5. As a result, we get the B2C e- commerce webstes evaluaton system and ndex weghts. 567

6 Table 5. B2C e-commerce webstes evaluaton system Frst level ndcators Second level ndcators Combnng weght A1 Commodty ndexes(0.53) B1 Types of goods (0.12) B2 Commodty prce (0.46) B3 The qualty of goods (0.18) B4 Packagng of goods (0.07) B5 Update rate (0.17) A2 Tradng Indcators(0.27) B6 Navgaton system (0.08) B7 Search functon (0.07) B8 Payment (0.28) B9 Order onlne trackng (0.09) B10 Processng speed (0.17) B11 Dstrbuton servces (0.31) A3 Customzed ndcators (0.12) B12 Account Management (0.20) B13 Customzed servces (0.07) B14 After-sales servce (0.34) B15 Returns (0.22) B16 Promotons (0.17) A4 Interacton ndcators (0.08) B17 Consultaton Feedback (0.36) B18 Lve Support (0.27) B19 Frendly nterface (0.11) B20 Prvacy Securty (0.26) Notes: synthess weghts for the correspondng ndex weghts at all levels of the plot 4. Emprcal Analyss of the evaluaton system of e-commerce stes 4.1. Collect data Frst select 30 users of Dangdang and Joyo respectvely, to vote for the actual stuaton of the webste. The result of the votng s as follows: Commodty ndexes Commodty ndexes Tradng Indcators Customzed ndcators Interacton ndcators Table 6. Dangdang votng results table Factor good OK medum Poor worse Types of goods Commodty prce The qualty of goods Packagng of goods Update rate Navgaton system Search functon Payment method Onlne order trackng Order processng speed Dstrbuton servces Account Management After-sales servce returns Promotons Consultaton Feedback Lve Support Frendly nterface Prvacy Securty

7 Commodty ndexes Tradng Indcators Customzed ndcators teracton ndcators Table 7. Joyo votng results table Factor Good OK Medum Poor Worse Types of goods Commodty prce The qualty of goods Packagng of goods Update rate Navgaton system Search functon Payment method Onlne order trackng Order processng speed Dstrbuton servces Account Management Customzed servces After-sales servce Returns Promotons Consultaton Feedback Lve Support Frendly nterface Prvacy Securty Emprcal Analyss The fgures n the table represents voters number Frst, the results of the second layer ndcators consttute the frst level of fuzzy matrx and then carry out fuzzy comprehensve evaluaton of the frst layer. Ultmately could get much more level fuzzy comprehensve evaluaton results. The detaled calculatons are shown below[8-10]. The commodty ndcators two factor, for example, accordng to the statstcal results of Table 5 Dangdang combned B1, B2, B3, B4, B5 three secondary factor weghts (0.12,0.46,0.18,0.07,0.17), can calculate the set of commodty ndexes A1[4] r, 11 r, 12 r,, 13 r14 r = 15 15/30 8/30 5/30 2/30 0 7/30 14/30 6/30 3/30 0 (0.12,0.46,0.18,0.07,0.17) 15/30 7/30 6/30 2/ / / / 30 2 / 30 2 / / 30 11/ 30 2 / 30 1/ 30 0 =(0.362,0.374,0.183,0.076,0.005) Usng the same method, you can calculate the remanng three an alternatve set of ndcators, the second layer ndcators fuzzy relaton matrx R: R= Accordng to Table 5, A1, A2, A3, and A4 of the rght weght, we can get the fnal fuzzy evaluaton results: B=A R=(0.53,0.27,0.12,0.08) =(0.277,0.408,0.225,0.081,0.016) In ths paper, the evaluaton results can be nterpreted as: 569

8 27.7% of users beleve that Dangdang's compettveness s "good"; 40.8% of users beleve that Dangdang's compettveness s "OK ; 22.5% of users thnk Dangdang compettveness s "medum ; 8.1% of users beleve that Dangdang's compettveness s "poor"; 1.6% of users beleve that Dangdang's compettveness s "very poor". Dangdang s good comment rate acheves 68.5%. Joyo, the same results can be obtaned for Table 5-2: -3 (0.383,0.345,0.169,0.098, ) The results could be nterpreted as: 38.3% of users beleve that Joyo compettveness s "good"; 34.5% of users beleve that Joyo compettveness s "OK"; 16.9% of users beleve that the Joyo compettveness s "medum ; 9.8% of users beleve that Joyo compettveness s "poor"; 0.005% of users thnk Joyo compettveness s "worse". Joyo's prase rate reached 72.8%. Accordng to the data above, we can see that Joyo s comprehensve compettveness of the user experence s slghtly above Dangdang. Wth the popularty of the Internet, people transfer more and more busness actvtes on the network. The nfluence of E-commerce busnesses, servce capabltes and further enhance. People vst the ste s not just fnd nformaton lke they use tradtonal ste, but to carry out tradng and tradng of captal and goods, however, the asymmetry of nformaton brng the user's shoppng potental rsks. Because of ths partcular character, analyss of e-commerce ste features and nteractve behavors s requred, and make the followng work of evaluaton of e-commerce ste more targeted. 5. References [1] Patron, "Automatc Support for Usablty Evaluaton", IEEE Trans,S-oftware Energy, No.24,pp ,2009 [2] Neuendorf K.A, "The Content Analyss Gudebook", Thousand Oaks:S-age Publcatons, No.28,2008. [3] Helge Clausen, "Evaluaton of Lbrary Web Stes:the Dansh Case",The Electronc Lbrary, No.2,pp.83-87,2009 [4] Zhanglang, " Users experence of webste optmze n e-commerce optmzaton ",Computer Knowledge and Technology,No.10,pp ,2010 [5] Zhang Yangyang,Yuan Yuyu, "Analytc Herarchy Process analyss of e-commerce software usablty evaluaton ",Research of Computers Applcaton,No.4,pp , 118, 2009 [6] Dan Zhang, Xaoqng Zeng, We He, "Research on Customer Satsfacton of E-Commerce Webste wth Uncertan Lngustc Varables", JCIT: Journal of Convergence Informaton Technology, Vol. 7, No. 1, pp , 2012 [7] Wenyng Tan, Lhua Wang, Hengje Zhang, "Usng Cloud Computng to Buld E-Commerce Recommendaton Platform", JDCTA: Internatonal Journal of Dgtal Content Technology and ts Applcatons, Vol. 6, No. 12, pp , 2012 [8] M. Rad, Q. F. Hassan, "Servce-Orented Archtecture-A New Alternatve to Tradtonal Integraton Methods n B2B Applcatons", JCIT: Journal of Convergence Informaton Technology, Vol. 3, No. 1, pp.31-41, 2008 [9] Ashwn B.K, Kumaran K, Madhu Vshwanatham V, M Sumathr, "A Secured Web Servces Based E-Commerce Model for SMME Usng Dgtal Identty", IJACT: Internatonal Journal of Advancements n Computng Technology, Vol. 2, No. 2, pp , 2010 [10] Duoln Lu, "E-commerce System Securty Assessment Based on Grey Relatonal Analyss Comprehensve Evaluaton", JDCTA: Internatonal Journal of Dgtal Content Technology and ts Applcatons, Vol. 5, No. 10, pp ,