Research on Evaluation of Customer Experience of B2C Ecommerce Logistics Enterprises

Transcription

1 3rd Internatonal Conference on Educaton, Management, Arts, Economcs and Socal Scence (ICEMAESS 2015) Research on Evaluaton of Customer Experence of B2C Ecommerce Logstcs Enterprses Yle Pe1, a, Wanxn Xue1, b, Dandan L1, c, Yong Su2, d 1 Department of Informaton Management and E-commerce, Bejng Unon Unversty, Bejng, , Chna Unt of PLA, Bejng, , Chna a emal: peyle@126.com, bemal:xuewanxn@126.com, cldandanhdj@163.com, d sudoo@163.com Keywords: ; customer experence; fuzzy comprehensve evaluaton Abstract. Wth the rapd development of E-commerce, more and more enterprses attach great mportance to customer experence, and B2C E-commerce logstcs enterprses make no excepton. The good customer experence can promote consumer s percepton of the servce level of ECommerce logstcs enterprses. Ths paper establshes ndex system customer experence of B2C E-commerce logstcs enterprses, uses analytc herarchy process to determne the weght of each level ndex, establshes the fuzzy comprehensve evaluaton model, fnally the case study shows that t s reasonable and credble to evaluate customer experence of B2C E-commerce logstcs enterprses wth analytc herarchy process and fuzzy comprehensve evaluaton. Introducton Wth the rapd development of Internet technology and e-commerce, more and more B2C Ecommerce logstcs enterprses attach great mportance to onlne customer experence, because more customers complete the purchasng, orderng, and even after-sales servce of goods through the network. But now Chna's e-commerce logstcs servces are manly labor-ntensve servces and the utlzaton of basc facltes s low, so t s dffcult to meet the growng demand for onlne consumers. Therefore, t s urgent for Chna's e-commerce logstcs enterprses to mprove the servce level of the dstrbuton lnk of physcal commodtes. Customers attach mportance to obtanng the specal feelng n accordance wth the personalty, taste preferences and values n the process of nformaton collecton and evaluaton, when they make the purchase decson, namely the experence. The good customer experence can promote consumers' percepton of the servce level of e-commerce logstcs enterprses, and then promote servce purchase ntenton and servce purchase behavor of consumers. Hsuan (2011) proposes customer experence s the mportant factor to promote purchase behavors, but under the envronment of E-Commerce customer experence has not been fully studed [1]. At present, most of researches on customer experence focus on the lmted types of servce ndustry, whch s only concerned wth the phenomenon of customer experence n the physcal servce envronment The phenomenon of the customer experence of logstcs servce n the vrtual envronment s lack of scentfc research. In addton, conductng research on the dmensons of customer experence of B2C e-commerce logstcs enterprse and evaluatng customer experence of B2C E-commerce logstcs enterprses are ssues n the lack of research n the current theoretcal research and management practces, dong that wll evaluate the customer experence of objectvely, understand and predct customer servce purchase ntenton of B2C e-commerce logstcs enterprse better, and promote the healthy and rapd growth of B2C E-commerce logstcs enterprses The authors - Publshed by Atlants Press 747

2 The Index System of Customer Experence of B2C E-commerce Logstcs Enterprses Lterature Revew Wenfe We (2013) proposes E-commerce logstcs s a seres of transportaton, storage, handlng, packagng, crculaton processng, nformaton processng and dstrbuton actvtes carred out under the envronment of network and nformaton technology support, whch requres actvtes match wth the nformaton flow and captal flow of electronc and network level under the envronment of E-commerce, and the logstcs objects nclude vrtual goods (or servces) and physcal goods. Kohler (2011), Rose (2012) propose n vew of the nature of Web2.0 technology, n the vrtual envronment, customers and retalers co-create experence. Accordng to the researches above, ths paper defnes customer experence of B2C E- commerce logstcs enterprses as the process n whch customers obtan the specal feelng, form cognton and evaluaton, thus affect the meetng of psychologcal demands and decson makng through a set of logstcs servce envronments and nteractve logstcs servce that E-Commerce logstcs webstes and operators provde. Establshment of Evaluaton Index System In 1988, PZB propose the SERVQUAL model, and developed a set of servce qualty scale whch s composed of tangblty, relablty, assurance, responsveness and empathy fve dmensons, based on the background of bankng and retalng servces ndustres. For B2C e- commerce logstcs enterprse customers, servce qualty experence s an mportant dmenson of customer experence. Hongl Guo and Jng Wang (2013) uses webste usefulness and webste ease of use two dmensons, when buldng B2C customer experence model based on the TAM model; webste usefulness s descrbed by nformaton qualty, servce level and the degree of attenton to personal needs three varables; webste ease of use s descrbed by learnng cost, the degree of relatve convenence and webste system qualty three varables. Yuanyuan Cao (2013) dentfes webste navgaton functon, search functon, operatonal flexblty, smplcty of operatonal process and other functons, and aesthetcs, professonalty, characterstcs of webste desgn affect customer experence[2]. Guoqng Guo s (2012) emprcal research shows that the communcaton dmenson of webste nteractvty s the key to mprove consumer experence value and satsfacton. Therefore, customer experence of contans the usefulness, ease of use and nteractve experence of the webste. Datan B(2014) measures customer experence of B2C e-commerce enterprses from perceptual experence, emotonal experence and trust experence three dmensons. Danyang Huang(2014) measure customer shoppng experence of B2C webstes from ntal product user experence, nformaton product user experence and product platform user experence three dmensons. Accordng to the researches above, ths paper desgn evaluaton ndex system, as shown n Table

3 Table 1. Evaluaton ndex system of Customer Experence of B2C E-commerce Logstcs Enterprses One-level ndexes Two-level ndexes Interpretaton of ndcators usefulness webste servce qualty and nformaton ease of use webste experence servce experence nformaton experence qualty webste functon qualty, webste desgn qualty, the degree of relatve convenence nteractvty mutual communcaton and mutual nfluence between webstes and customers servce tangblty servce relablty servce assurance servce responsveness servce empathy nformaton tmelness nformaton relablty nformaton securty The actual facltes, equpment and servce personnel s appearance of B2C e- commerce logstcs enterprse can perform the servce commtment relably and accurately employees knowledge, etquette and the ablty to express confdence and credblty n B2C e-commerce logstcs enterprses help customers and mprove servce levels rapdly care about customers and provde customers wth personalzed servce provde customers wth tmely and convenent logstcs nformaton provde customers wth accurate and relable logstcs nformaton provde customers wth safe and relable logstcs nformaton Fuzzy Comprehensve Evaluaton Establshment of Factor Set and Evaluaton Set Factor set s made up of elements that affect the judgment objects. It can be commonly expressed as follows: U U1, U2,..., Un. Accordng the analyss above, there are 15 sngle factors affectng B2C e-commerce logstcs enterprses, and they can be dvded nto two ters. The factor set can be establshed U U, U, U U u, u, u U u, u, u, u, u, as 1 2 3, and the sngle-factor sets are , U u, u, u Evaluaton set s made of all knds of total judgment results gven by judges as elements. It can be expressed as V, namely, V V1, V2,..., Vm. The evaluaton set V of can be establshed wth fve evaluaton results: excellent, good, moderate, common and bad. 749

4 Establshment of Weght Set Every factor has dfferent mportance degree. To reflect the dfferences, every factor U s endowed wth correspondng weght w. And the set W ( w 1, w 2,, w n ) whch conssts of weghts s called factor weght set. Establshment of the mult-level evaluaton model Frst, complex problems break down nto several elements and dfferent elements are dvded nto several groups. Then we establsh a multlevel evaluaton model based on the group status. Establshment of the comparson judgment matrx Membershp between the up-down herarchy members s determned after we establsh the mult-level evaluaton model. We draw the parwse comparson between elements n each herarchy of the mult-level model for the correlatve up-level element, and then establsh a seres of judgment matrxes as follows: b11 b12 b1 n b21 b22 b 2n A B bn1 bn2 bnn In the formulaton, bj 0, bj 1/ bj, b 1. bj stands for the mportance proporton scale of B and B j for the correlatve up-level element A. When drawng the parwse comparson between elements, one-to-nne scale method s usually adopted as shown n Table 2. Table 2: The defnton of scale method Scale Defnton descrpton 1 The equal mportance of two elements comparson. 3 The former s a lttle more mportant than the latter. 5 The former s obvously more mportant than the latter. 7 The former s mghtly more mportant than the latter. 9 The former s extremely more mportant than the latter. 2,4,6,8 The ntermedate values of adjacent judgments above. Ths paper uses characterstc root method to compute collatng weght vector. We suppose that the max characterstc root of judgment matrx s max, and the correspondng characterstc vector s W. The methods of W and max are as follows: ) Multply elements of A B accordng to lne; ) Extract ganed products for n tmes; ) Normalze the root vector and get the collatng weght vector W; n n n n n j j j1 1 j1 n (1) w b / b ( 1,, n) max bw n j 1 ( 1,, n) (2) 1 nw Consstency check To make sure that the decson-makng process s scentfc, consstency check of max s necessary. Checkng process s as followng: ) The calculaton of concdence ndex CI CI ( max n)/( n 1) (3) ) The calculaton of concdence rate CR CR CI / RI (4) RI s random concdence ndex. When CR<0.1, we consder that judgment matrx has a good consstency, or else we should adjust the values of elements n judgment matrx. The calculaton of combnaton weght of each level element To get the weghts of all elements of each level for the overall objectve, t s necessary to judge the value of CR. If CR 0.1, we should assemble the calculaton results of the thrd step properly and check the total judgment consstency. 750

5 We do ths step baspetally. The fnal results ndcate the relatve weght of decson-makng prorty sequence and the judgment consstency check of the whole herarchcal model. Fuzzy Evaluaton Frst, experts evaluate from the sngle element of factor set U and determne the degree of membershp that the evaluaton objects rely on the elements of factor set. Then, we establsh the total evaluaton matrx consstng of evaluaton sets of n elements. It s usually expressed as R. After we get values of W and R, we can do fuzzy mappng to have a comprehensve judgment. The mathematcal model of fuzzy comprehensve evaluaton s shown as: B W R (5) Applcaton Example Analyss We have a fuzzy comprehensve evaluaton of customer experence of B2C e-commerce logstcs enterprses, based on theoretcal study and combnng the practcal needs of customers n certan B2C e-commerce logstcs enterprse. The model s as shown n Fg 1. B 1 B 2 B 3 nformaton securty nformaton relablty nformaton tmelness servce empathy servce responsveness servce assurance servce relablty servce tangblty Interactvty ease of use usefulness C11 C12 C13 C21 C22 C23 C24 C25 C 31 C 32 C 33 Fg.1. Step-down herarchcal model of network growth capabltes of hgh-tech small and mcroszed enterprses Accordng to Fg.1 above, we structure the judgment matrx A B. Smlarly, we can establsh the judgment matrx of C-level elements for correlatve B-level elements. Based on Formulaton (1) and (2), we calculate and get: max =5.0680, W B =(0.1365,0.2385,0.6250). W s the weght set of B-level elements for the general goal. Based on Formulaton (3), we calculate and get: CI= When n=3, RI=0.58. Based on Formulaton (4), we calculate and get: CR=0.0158<0.10. Ths ndcates that the judgment matrx has a satsfyng consstency. Smlarly, we can also calculate all weghts of evaluaton ndexes of customer experence of ths B2C e-commerce logstcs enterprse. Accordng to all weghts of evaluaton ndexes, we can calculate and get combnaton weght: W=(0.0959, , , , , , , , , , ). Evaluatng factor set U s made up of eleven factors nfluencng customer experence of ths B2C e-commerce logstcs enterprse. Evaluaton set V s establshed wth fve evaluaton results for the factors: excellent, good, moderate, common and bad. Accordng to experts test data of customer experence of ths B2C e-commerce logstcs enterprse, we establsh estmaton matrx. Based on Formulaton (5), we can calculate: B=(0.1288,0.4274,0.3773,0.0665,0). 751

6 Accordng to the prncple of maxmum degree of membershp, the maxmum degree max( b )=0.4274, whch shows that customer experence of ths B2C e-commerce logstcs enterprse s the second level, namely, that s good. Conclusons Ths paper adopts the method of analytc herarchy process and fuzzy evaluaton to establsh the fuzzy comprehensve evaluaton model, n order to avod the effect of ndvdual subjectve judgment and favortsm on the result of customer experence of B2C e-commerce logstcs enterprses. Accordng to the results, fuzzy comprehensve evaluaton s a reasonable and feasble method to evaluate customer experence of, and t can be used wdely n customer experence of. Acknowledgement In ths paper, the research was sponsored by Bejng Hgher Educaton Young Elte Teacher Project(No.YETP1761). References [1] Hsuan Yu Hsua. Understandng Customer Experences n Onlne Blog Envronments [J]. Internatonal Journal of Informaton Management, 2011, (31): [2] F. Lemke, M. Clark, H. Wlson, Customer Experence Qualty: An Exploraton n Busness and Consumer Contexts Usng Repertory Grd Technque [J], J. Acad. Mark. Sc, 2011, 39(6): [3] S. Rose, M. Clarck, P. Samouel, N. Har, Onlne Customer Experence n E-retalng: An Emprcal Model of Antecedents and Outcomes [J], J. Retal, 2012, 88(2): [4] Guo Hongl, Wang Jng. The Study of B2C Customer Experence Model Based on Tam Model, Scence and Technology Management Research [J]h, 2013, (19): [5] Cao Yuanyuan, Zhang Jantong. E-commerce Customer Experence Evaluaton Research Based on Factor Analyss and Synthetc Fuzzy Method [J]. Shangha Management Scence, 2012, 35(2): [6]Guo Guo-qng, L Guang-mng, The Influence of Interactvty of Onlne Shoppng on Consumers Experental Value and Satsfacton [J], Chna Busness and Market, 2012, (2):