Hindawi Publishing CorporationMathematical Problems in EngineeringVolume 2013 Article ID 128678 9 pageshttpdxdoiorg1011552013128678

Research ArticleService Quality of Online Shopping PlatformsA Case-Based Empirical and Analytical Study

Tsan-Ming Choi1 Pui-Sze Chow1 Bowood Kwok1 Shuk-Ching Liu1 and Bin Shen2

1 Business Division Institute of Textiles and Clothing The Hong Kong Polytechnic University Hung Hom Kowloon Hong Kong2 School of Glorious Sun of Business and Management Donghua University Shanghai 200051 China

Correspondence should be addressed to Bin Shen binshenjerrygmailcom

Received 29 July 2013 Accepted 2 September 2013

Academic Editor Kannan Govindan

Copyright copy 2013 Tsan-Ming Choi et alThis is an open access article distributed under theCreativeCommonsAttribution Licensewhich permits unrestricted use distribution and reproduction in any medium provided the original work is properly cited

Customer service is crucially important for online shopping platforms (OSPs) such as eBay and Taobao Based on the well-established service quality instruments and the scenario of the specific case on Taobao this paper focuses on exploring the servicequality of an OSP with an aim of revealing customer perceptions of the service quality associated with the provided functions andinvestigating their impacts on customer loyalty By an empirical study this paper finds that the ldquofulfillment and responsivenessrdquofunction is significantly related to the customer loyalty Further analytical study is conducted to reveal that the optimal service levelon the ldquofulfillment and responsivenessrdquo function for the risk averse OSP uniquely exists Moreover the analytical results prove that(i) if the customer loyalty is more positively correlated to the service level it will lead to a larger optimal service level and (ii) theoptimal service level is independent of the profit target the source of uncertainty and the risk preference of the OSP

1 Introduction

Observing from the popular and growing trend of electroniccommerce (e-commerce) there is no doubt that businesssuch as those selling fashion-related products can now usethe Internet to interact with customers and gain the com-petitive edge The critical determinants of success in e-com-merce cover not only the low price strategy but also itsservice quality (ie e-service quality) [1 2] In the literatureZeithaml [2] defines e-service quality as an overall customerassessment and judgment of e-service delivery in the virtualmarketplace To measure the customer perceptions of servicequality the SERVQUAL model which was first developedby Parasuraman et al [3] has been widely adopted TheSERVQUAL model includes five dimensions namely tan-gibles reliability responsiveness assurance and empathyPrevious studies on the measurement of e-service qualityfocus on the ldquorewordingrdquo of the original scale items of theapplication of the SERVQUALmodel Yet service researchersought to pay extra attention to e-services in the field of servicequality because assessing service quality in e-commerce

might be different from that in physical marketplace service[4] As such it is necessary to reformulate the SERVQUALscale items in e-commerce context [5]

With the prevailing trend of online shopping in HongKong and China httptaobaocom becomes one of thehottest online shopping platforms (OSPs) despite its keencompetition with internationally renowned companies suchas yahoo auction and eBay as well as domestic companiessuch as Jingdong and Dangdang Yahoo auction and eBayenable consumers to search for products globally and hencehave their niche However it is interesting to observe thathttptaobaocom has successfully penetrated into the HongKong and Macau e-tailing markets by its price leadershipspecial features and a wide product range More import-antly httptaobaocom also enjoys the geographic advant-age of pooling local sellers together with a low local deliverycost and convenient and unobstructed communication withseller The exclusive functions featured in httptaobaocominclude (i) Searching Functions (ii) Guarantee of TradeSafety (iii) Aliwangwang (iv) Alipay and (v) Taodot Allthese features of httptaobaocom are then able to

2 Mathematical Problems in Engineering

Table 1 Featured functions of httptaobaocom

Featured functions Descriptions

Search functionSearch by keywords show items in icons or lists or by default arrangement sort searching product accordingto price credibility transaction volume and store location and lastly sort the production categories intobrand new hot items product from Taobao mall second handed or auction

Guarantee of trade safety

Each seller of httptaobaocom is required to sign the Guarantee of Trade Safety Agreement which ensuresthat she commits to provide guaranteed transaction service to customers Customers are able to request forrefund if they receive any incorrect goods The terms of guarantee include 7 days of unconditional return nocounterfeits 3 times refund for counterfeits maintenance within 30 days of purchase and express delivery

AliwangwangIt is the pervasive communication between buyer and seller prior to the purchase through its embeddedproprietary instant chat program Customers can enquire about products engage in bargaining display ofproduct image in the messenger window and make queries of the delivery status and logistic message

Alipay It is an escrow-based online payment platform It aims to enable fast money transaction with higher securityand privacy

Taodot It is an online platform that provides the value adding service for Alipay users This convenient tool enablesTaobao users who do not have an RMB account to pay for their online transactions

differentiate it from other competing OSPs and obtain itsniche Table 1 summarizes the major featured functions ofhttptaobaocom

For the research goal and contribution first by modi-fying the SERVQUAL model this paper attempts to derivethe instrument dimensions of e-service quality from thefunctions featured on httptaobaocom and to develop aresearch model to examine how its e-service quality dimen-sions affect the customer loyalty To achieve this researchobjective empirical data is collected from 195 online con-sumers (who have purchased fashion products (as a remarkin this paper in order to be more concrete we focus onexamining the consumersrsquo experience in purchasing fashionproducts) on httptaobaocom) Second based on theempirical finding this paper further analytically develops andexplores the optimal service decision on the function whichis critical to the OSP We believe that the research findingsderived from this paper can contribute to the literatureon service management and they also provide importantmanagerial insights on OSPrsquos operations

2 Literature Review

21 Review of Related Empirical Studies and ModelsSERVQUAL is a multiple-item scale for measuring customerperception of service quality [3] According to those fivedimensions of the SERVQUAL model we note that thetangibles dimension means the physical facilities and theappearance of personnel the reliability dimension refers tothe companyrsquos ability to perform the promised servicedependably and accurately the responsiveness dimensionrelates to the willingness to help customers and provideprompt service the assurance dimension means the employ-ee knowledge base which induces customer trust and con-fidence and finally the empathy dimension is about caringand individualized attention provided to customers by theservice provider

Recently SERVQUAL scale has been employed tomeasure the corresponding system service quality in e-commerce [6 7] Most existing research on e-service quality

measurement focuses on rewording the SERVQUAL scaleitems (eg [8ndash10]) The challenges in measuring web-basedservice quality mainly come from the differences betweenweb-based and traditional customer service Parasuramanand Grewal [4] suggest that revisions of the classicalSERVQUALdimensions are necessary because for web-basedservice quality customers interact with technology ratherthan the traditional service personnel Moreover severalstudies have proposed that the SERVQUAL scale items shouldbe reformulated in the online shopping context [5 11]

As a remark there are criticisms over the applicabilityof the SERVQUAL model Among them one important cri-tique is on the fact that SERVQUALrsquos five dimensions are notuniversally applicable and that the model fails to draw onestablished economic statistical and psychological theories[12] In fact the most significant criticism is the stability ofdimensions and items across different industries [13] Othercriticisms include (i) its focus on the process of servicedelivery rather than the outcomes of the service encounterand (ii) the probable respondent error from the reversedpolarity of items in the scale in the consumer survey [12 14]Parasuraman et al [15] also comment that the SERVQUAL isfar fromperfect as it is not general enough but they argue thatit does provide the basic skeleton for others to study servicequality even though some degree of customization is pro-bably needed (eg adding context-specific items) AlthoughSERVQUALrsquos construct validity is being challenged it is stillthe most widely used model employed to measure customerexpectations and perceptions of service quality

E-service quality is defined as how customers judge andevaluate the e-service being delivered to them [11] On theevaluation of websites service quality Parasuraman et al [16]introduce the E-S-QUAL and E-RecS-QUAL scales whichextend the SERVQUALmodel in evaluating the service qual-ity in the ldquoonline shopping contextrdquo E-S-QUAL is a multiple-item scale consisting of efficiency (8 items) system avail-ability (4 items) fulfillment (7 items) and privacy (3 items)dimensions E-S-QUAL provides the first formal definition ofWeb site service quality and it is a framework for consumerevaluation of electronic services Notice that Zeithaml [2]

Mathematical Problems in Engineering 3

Customer loyalty

Reliability (guarantee of trade safety)

Fulfillment and responsiveness (aliwangwang)

Flexibility (taodot)

Privacy (alipay)

H1

H2

H3

H4

H5

Efficiency (searchingfunctions)

Figure 1 The proposed ESQ-OSP e-service quality model (Customized for httptaobaocomrsquos case)

suggests that e-service quality is a multidimensional con-struct and the electronic service recovery construct involvesdimensions different from the core e-service quality From e-service perspective Parasuraman et al [16] develop E-RecS-QUAL specifically to measure e-service quality and this scalebrings out a new perspective by utilizing valuable consumerinformation on service recovery issues Actually E-RevS-QUAL is a subscale that contains items focusing on handlingservice problems and inquiries and being effective to thosewho used to have nonroutine behaviors on the online storeThe basic E-S-QUAL Scale (relevant for a Web sitersquos entirecustomer base) is a four-dimensional 22-item scale whereasE-RecS-QUAL (relevant for the portion of the customer basewith recovery service experience) is a three-dimensional scalewith 7 items E-RevS-QUAL is used to stimulate and facilitateadditional scholarly research on e-service quality and alsoassist practitioners in systematically assessing and improvinge-service quality together with E-S-QUAL [16]

Notice that Parasuraman et al [16] cannot evaluate thevalidity and psychometric properties of the scale in theirstudy due to the insufficient sample size [17] The mainreason of the limited sample size is that the number of con-sumers who encounter service problems is relatively smallerthan those who do not Hence their study is neither fullygeneric nor absolutely reliable Moreover E-S-QUAL is stilla recent model (2005) which requires more time to verifyAs a remark observe that there are different versions ofthe modified E-S-QUAL for identifying the best e-servicequality dimensions with the goal of examining differentonline businesses [17] Given the limitation of the existingmodels this paper proposes and explores an empirical model

which incorporates the service quality dimensions extractedfrom SERVQUAL E-S-QUAL and E-RevS-QUAL which webelieve can appropriately represent and study the servicequality in anOSP such as httptaobaocom (For somemorerecent literature on service quality related to online shoppingsee Rossitier [18] Ding et al [19] Ha and Stoel [20] and Huand Qiang [21])

Regarding the research motivation notice that priorstudies in the literature have suggested that perceived ser-vice quality positively influences customer satisfaction andpurchase intentions [22 23] However to the best of ourknowledge in the context of online shopping the impacts ofhow service quality dimensions affect overall e-service qualityand the customer loyalty are still under-explored This paperhence contributes to the literature by partially filling thisresearch gap Moreover by building the empirical data-basedanalytical model we explore the optimal service decision forthe OSP and derive various important insights

22 Empirical Hypotheses Development After combining andsynthesizing the existing construct of both service quality ande-service quality based on the reviewed literature we proposea perceived e-service quality construct for an OSP (ESQ-OSP) with httptaobaocom taken as the specific case Theproposed e-service quality model comprises 5 dimensionsextracted from SERVQUAL E-S-QUAL and E-RevS-QUALmodels namely efficiency reliability fulfillment and respon-siveness flexibility and privacy as shown in Figure 1

We now describe each dimension under the proposedESQ-OSPmodel First in Parasuraman et al [16] efficiency isreflected by whether the web site is simple to use structured

4 Mathematical Problems in Engineering

properly and requires a minimum amount of informationto be inputted by the customer As a result the onlineshipping platform is efficient if (i) it is easy for customersto find what they need (ii) it enables customers to completea transaction quickly and (iii) the information providedby the web site is well organized Thus service quality ofthe searching functions of httptaobaocom can reflect itsefficiency Therefore we have the following hypothesis

H1 efficiency positively influences the customer loyaltyof httptaobaocom

Reliability here refers to the consistency of performanceand dependability of companies [3] Reliability can makecustomers recognize the consistency and credibility of thecompany In e-commerce it is vital to make customers trustthe promises that the OSP made Obviously ldquoThe Guaranteeof Trade Safetyrdquo function of httptaobaocom is relatedto ensuring reliability and it is hypothesized to influencecustomer loyalty

H2 reliability positively influences the customer loyaltyof httptaobaocom

Fulfillment refers to the extent in which the websitersquospromises on order delivery and items availability are fulfilledTo be specific fulfillment is reflected by whether the OSP canensure items being available to delivery within an acceptabletime frame and guarantee quick delivery as promised [16] Forhttptaobaocom the functions of Aliwangwang are ableto represent fulfillment Responsiveness means the effectivehandling of problems and returns through the site It providescustomers with convenient options for returning items Thewebsite promises to handle product returns and offers ameaningful guarantee so that customers would feel morecomfortable [16] As one of the service quality dimen-sions contacting the seller through Aliwangwang reflectsthe responsiveness of httptaobaocom Therefore it ishypothesized that

H3 ldquofulfillment and responsivenessrdquo positively influ-ence the customer loyalty of httptaobaocom

Flexibility refers to the choice of payment methods Forexample when purchasing products in httptaobaocomTaodot is an innovative way for the users to pay onlinesafely Consumers can buy Taodot to depositadd values intheir Alipay account It is safer and shows its flexibility ifconsumers are in Hong Kong or Macau Then the followinghypothesis is proposed

H4 flexibility positively influences the customer loyaltyof httptaobaocom

Privacy refers to the degree to which the website is safeand customer information is well-protected This dimensionholds an important position in e-commerce and is critical ine-service Customers perceive significant risks in the virtualenvironment of e-commerce stemming from the possibilityof improper use of their personal financial data The privacyfunction in httptaobaocom is mainly associated withAlipay Accordingly it is hypothesized that

H5 privacy positively influences the customer loyalty ofhttptaobaocom

23 Review of Related Analytical Models In the literature onanalytical research there are many recent studies related tothe optimal service decision For example Xiao et al [24]explore the optimal service strategy with optimal pricingdecisions in a retail supply chain with risk averse playersThey consider the situation inwhich the retailer has to choosewhether to provide a service guarantee (SG) or to provideno service guarantee (NSG) They relate the optimal choiceto the supplierrsquos degree of risk aversion and the consumerrsquossensitivity to service reliabilityThey find that endogenizationof wholesale pricing decision will raise the retailerrsquos moti-vation to adopt SG strategy if the consumer is sufficientlyrisk averse Choi [25] examines the optimal service chargefor consumer product returns under mass customizationoperations He explores two cases which cover the scenariowhen the mass customization company is risk-neutral andrisk-averse He derives the closed-form expression of theoptimal return service charge for each case He analyticallyshows how the mass customization companyrsquos degree of riskaversion affects the optimal return service charge policy Inaddition he derives the analytical conditions under which itis optimal for the mass customization company to offer a freereturn with a full refund policy Hu andQiang [21] investigatean online supply chain analytical model which is consistedof manufacturers express service providers and electronicretailers They consider the situation in which consumerswho purchase products from the electronic retailers canchoose to adopt the express delivery service or simply pick upthe products themselves At the same time the express servicecompanies can invest on service quality improvement Theydevelop the variational inequality formulation and generateinsights via exploring the equilibrium As reviewed above itis evidenced that analytical modeling research is commonlyadopted to explore optimal service decisions and strategiesin the literature However to the best of our knowledgenone of them explore the problem associated with theempirical service quality framework Thus we believe thatthis paper is the first onewhich investigates the service qualityproblem forOSPs by combining both empirical and analyticalapproaches

3 Data Collection and Empirical Data Analysis

A consumer questionnaire survey is conducted 250 ques-tionnaires are distributed via social network websites undera convenience sampling approach Receivers are invited tocomplete the questionnaire if they know httptaobaocomand have visited it 190 valid questionnaires are received andemployed for statistical analysis finally

The questionnaire is divided into 8 parts The first part isused to collect the demographic data of the respondents Thesecond part is employed to examine the purchase behaviorof the respondents on httptaobaocom The rest of thequestionnaire is divided into 6 subparts to investigate theperceptions of httptaobaocom users on the 5 functionsprovided by httptaobaocom and their loyalty towards the

Mathematical Problems in Engineering 5

OSP The 7-point Likert scale is used in which 1 representsldquostrongly disagreerdquo and 7 represents ldquostrongly agreerdquo

31 Descriptive Statistics Among all the completed ques-tionnaires received 64 of the respondents have purchasedfashion products on httptaobaocom before while 36have not Thus the remaining analysis will be based on theresponses of these 64 of the collected sample in order toget valid data To be specific these consumers are consideredas httptaobaocomrsquos customers (for fashion products)The percentage of the httptaobaocom customers whobrowse products on httptaobaocom daily occupies 813 11 and 26 of httptaobaocomrsquos customers browseproducts on httptaobaocom 2-3 times a week once aweek and 2-3 times per month respectivelyThe percentagesof the httptaobaocomrsquos customers who buy products viahttptaobaocom daily 2-3 times a week once a week and2-3 times a month are 3 2 2 and 11 respectively

Regarding spending 75 of the httptaobaocomusers spend less than $500 on httptaobaocom monthly19 of them spend $500-1000 on Tobaocom monthly2 1 and 3 of the users spend $1000ndash1500 $1500ndash2000 and more than $2000 on httptaobaocom monthlyrespectively Regarding the users shopping experiences onhttptaobaocom ranking their enjoyment (in a 1ndash7 scalewith 7 being the best) more than half of the respondentsscore their enjoyment level strictly above 4 (64) 36 ofthem score as score 5 which represents the greatest group23 of them score as score 6 and 5 of them rank it 7The percentage of score 4 which means neutral excitementoccupies 15The percentages of low scores 1 2 and 3 occupy5 5 and 11 respectively

32 Hypotheses Testing Before testing hypotheses we firstconduct the reliability test to assess the internal consistencyof the variables Reliability test is measured by the Cronbachrsquosalpha in which the alpha value of 07 or above is widelyrecognized as an acceptable level of reliability [27] TheCronbach alpha values for Searching functions Guaranteeof Trade Safety Aliwangwang Alipay and Taodot are foundto be 0933 0919 0956 0958 and 0921 respectively All ofthem are higher than the acceptable level Besides the sevenquestions of customer loyalty are tested in this part TheCronbach alpha value of the customer loyalty is 0905 whichalso exceeds the acceptable level Thus we conclude that allthese dimensions are sufficiently reliable Next we proceedto test the relationships of the five featured functions andcustomer loyalty by performing multiple linear regressionanalysis It is shown that there is a statistically significantrelationship between customer loyalty and the Aliwangwangfunction The related statistical results of hypotheses aresummarized in Table 2 As such hypothesis H3 is supported(more details on the statistical analysis can be found in thesisresearch of Kwok [26])

4 Empirical Findings and Insights

From the above empirical analysis first of all we know thatthe factor of ldquofulfillment and responsiveness (Aliwangwang

function)rdquo is the most critical one as it most strongly affectscustomer loyalty of httptaobaocomrsquos customers (whopurchased fashion products before) Observe that this resultis consistent with the findings in the traditional servicecontext which suggests that customers expect quick feedbackon their requests and suggestions for improvements [2]For httptaobaocomrsquos case it can be explained by thefunction of Aliwangwang which provides instant messagingfor httptaobaocomrsquos users to send messages to the sellersThis enhances the fulfillment of httptaobaocom userson queries and needs related to product delivery and itemavailability This function also can reduce the customersrsquodoubt on the product that they want to buy and furtherconfirm their purchase decision In addition the customerscan contact the sellers whenever the sellers are online witha simple click This function extends the accessibility of thesellersonline stores without the control of the store operatinghours With Aliwangwang httptaobaocom users are ableto obtain adequate product information even if it is not givenon the product web page It increases the convenience ofhttptaobaocom Since it is a unique feature provided byhttptaobaocom but not its close competitors it becomes amajor niche for httptaobaocom

Second observe that only one hypothesis among theproposed five is proven to be true statistically A possiblereason may be given as follows Unlike the traditionalphysical store or other kinds of online electronic retailerssuch as httpwwwamazoncom etc httptaobaocomis an online marketplace in which there are thousands ofindependent retailers As a result customersrsquo perception onthe service quality and their loyalty towards the OSP maybe affected by their buying experience with the individ-ual retailers Owing to the fundamental differences manyhypotheses which apply for the more traditional bricks-and-mortar retailingelectronic retailing do not hold for theOSPs such as httptaobaocom In addition notice that inhttptaobaocom communication with individual sellers isespecially important In fact customers would like to gathermore information so that they can make better choice from alarge variety of independent retailers selling similar productson the platformThis accounts for the support ofHypothesis 3(which is about Aliwangwang and related to communicationaspect)

Third we know that a higher level of ldquofulfillment andresponsiveness (Aliwangwang function)rdquo will lead to a higherlevel of customer loyalty According to Edvardsson et al[28] customer loyalty will positively affect the revenue ofthe OSP it will be thus interesting to analytically examinewhether there exists an optimal level of ldquofulfillment andresponsivenessrdquo for the OSP We hence further conduct ananalytical study in the next section

5 Analytical Service OptimizationModel and Findings

Based on the empirical findings from Section 4 we proceedto build an analytical model for further analysis First fromthe linear regression analysis conducted in Section 4 (seeTable 2) we notice that the level of customer loyalty 119897 is related

6 Mathematical Problems in Engineering

Table 2 Summary of regression analysis

Corresponding hypothesis Independent variable Standardized coefficient t-value SigHypothesis 1 Efficiency minus0057 minus0575 0573Hypothesis 2 Reliability 0087 0896 0383Hypothesis 3 Fulfillment amp responsiveness 0582 2255 0038lowastlowast

Hypothesis 4 Flexibility 0062 0406 0690Hypothesis 5 Privacy 0307 1391 0182Remarksdependent variable customer loyaltylowastlowasthypothesis is supported

to the level of ldquofulfillment and responsivenessrdquo 120582 in a linearfunction as follows

119897 = 119886 + 119887120582 (1)

where 119886 119887 gt 0Second since it is intuitive that a higher level of customer

loyalty 119897will ldquostochasticallyrdquo (itmeans expectedly the revenueshould be higher but the exactmagnitude is unknown) lead toa higher revenue 119877(119897) for the OSP such as httptaobaocomwe have the following

119877 (119897) = 119866 (119897) + 120576 (2)

where (i) 119866(119897) is an increasing function of 119897 For analyticaltractability we further assume 119866(119897) as a concave function(ii) 120576 is a random variable which captures the noise (ieuncertainty) associated with 119877(119897) Following the literature[29] we model it to follow a symmetric distribution suchas the normal distribution with a zero mean and constantvariance 1205902

Third providing a higher level of ldquofulfillment and respon-sivenessrdquo120582 requires resourceWe hence define the respectivecost as 119862(120582) which is an increasing function of 120582 Similarto 119866(119897) we assume 119862(120582) as a convex function for analyticaltractability Notice that the above functional definitions areall intuitive and rather general

With the above model we can derive the profit 120587(120582) theexpected profit 119864[120587(120582)] and the variance of profit119881[120587(120582)] asfunctions of 120582 as follows

120587 (120582) = 119877 (119897) minus 119862 (120582)

= 119866 (119886 + 119887120582) minus 119862 (120582) + 120576

119864 [120587 (120582)] = 119866 (119886 + 119887120582) minus 119862 (120582)

119881 [120587 (120582)] = 1205902

(3)

Because e-business is known to be a risky operation weargue that the OSP has concern on risk and possesses a riskaverse attitude [24] As such we employ the following mean-variance safety first measure (SFM(120582)) as the objective func-tion (for the modeling of risk averse optimization objectivessee Xiao and Choi [30] Chiu and Choi [31] Chiu et al [32]Choi and Chiu [33] Choi et al [34] Choi [35] and Shen etal [36]) for the OSP as follows

SFM (120582) =(119864 [120587 (120582)] minus 120573)

radic119881 [120587 (120582)] (4)

where 120573 is the expected profit target that the OSP wants toachieve

For the meaning behind SFM(120582) please notice thatby Bienayme-Tchebycheff inequality we have the followinginequality

119875 (|120587 (120582) minus 119864 [120587 (120582)]| ge 119864 [120587 (120582)] minus 120573) le119881 [120587 (120582)]

(119864 [120587 (120582)] minus 120573)2

(5)

997904rArr 119875 (120587 (120582) le 120573) le119881 [120587 (120582)]

(119864 [120587 (120582)] minus 120573)2equiv

1

SFM(120582)2 (6)

Thus the RHS of (6) gives the analytical upper boundfor 119875(120587(120582) le 120573) Obviously maximizing SFM(120582) yields aminimized upper bound for 119875(120587(120582) le 120573) and hence SFM(120582)

is called the safety first measure (see [37])With the above model we define (7) and then have

Proposition 1 Notice that 119878119866(120582) and 119878

119862(120582) are the first order

derivatives of 119866(119886 + 119887120582) and 119862(120582) respectively and they alsorepresent the corresponding slopes as follows

120582lowast

SFM = argmax120582

SFM (120582)

120582lowast

EP = argmax120582

119864 [120587 (120582)]

119878119866(120582) =

119889119866 (119886 + 119887120582)

119889120582

119878119862(120582) =

119889119862 (120582)

119889120582

(7)

Proposition 1 120582lowast119878119865119872

uniquely exists and it is equal toarg120582119878119866(120582)119878119862(120582) = 1

Proof of Proposition 1 All proofs are placed in the Appendix

Proposition 1 first presents the uniqueness feature of theoptimal level of fulfillment and responsiveness (ie optimalservice level) which maximizes the safety first objectiveIt then reveals that this optimal service level is the onewhich equalizes 119878

119866(120582) and 119878

119862(120582) In other words when the

rates of change of 119866(119886 + 119887120582) and 119862(120582) are the same thecorresponding service level 120582 is optimal From Proposition 1we have Proposition 2

Mathematical Problems in Engineering 7

Proposition 2 (a) A larger 119887 implies a larger 120582lowastSFM (b) 120582lowast

SFMis independent of 120573 and 120590

Observe that a larger 119887 means that the customer loy-alty is more positively correlated to the service level 120582Proposition 2(a) hence indicates that it will lead to a biggeroptimal service level because the optimal solution refersto the one which balances the increase of revenue 119878

119866(120582)

and rise of cost 119878119862(120582) It is also interesting to note from

Proposition 2(b) that 120582lowastSFM is independent of 120573 and 120590 Thisfinding can be explained by the fact that the variance of profit119881[120587(120582)] is a constant which is independent of the service level120582Thus the corresponding optimal service level is not relatedto 120590 as well as the expected profit target 120573 and will in factbehave like the risk neutral case as shown in Proposition 3below

Proposition 3 120582lowast119864119875

uniquely exists and 120582lowastSFM = 120582lowast

119864119875

Proposition 3 shows a rather surprising result that the riskaverse and risk neutral OSPs will have the same optimal ser-vice levels for this particular case In other words irrespectiveof the risk preference the optimal service level is the sameThis finding is important because it shows the fundamentaldifference between different kinds of operations manage-ment problems For example for inventory planning underuncertainty the optimal inventory decision for the risk averseand risk neutral cases will be different However in serviceoptimization the situation can be different as shown here

6 Concluding Remarks

We conclude by discussing some research implicationsderived from the discussions above First the empiricalfindings suggest that in order to enhance customer loyaltyonline stores should develop marketing strategies to enhanceldquofulfillment and responsivenessrdquo since they strongly affectcustomer loyalty httptaobaocom has devoted valuablecorporate resources to it andwe argue that httptaobaocomcan further improve the functions of Aliwangwang toachieve even better communication between the sellersand httptaobaocom users Some probable enhancementsincluding inserting flash games and converting to smart-phone apps can all help to attract the httptaobaocom usersto use Aliwangwang Furthermore httptaobaocom canmake use of Aliwangwang to obtain personalized data relatedto the customersrsquo preferences As a result a higher degreeof web site customization (eg based on past purchases andother customersrsquo information collected) can be provided toindividual customers

Second for the empirical result driven analytical studieswe find that the optimal service level on ldquofulfillment andresponsivenessrdquo function for the risk averse OSP uniquelyexists and can be expressed as the service level which balancesthe increase of revenue 119878

119866(120582) and the rise of cost 119878

119862(120582) We

further show that if the customerrsquos loyalty is more positivelycorrelated to the service level 120582 it will lead to a larger optimalservice level Moreover the optimal service level under thesafety first objective is independent of profit target 120573 and

uncertainty threshold120590 Finally we analytically prove that theoptimal service level does not depend on the risk preferenceof the OSP

For future research there are two directions Empiricallyit is useful to examine more functions of OSP such ashttptaobaocom This paper only focuses on the severalcore functions specifically adopted by httptaobaocom andthere are some more which deserve further explorationsBesides unlike the traditional bricks-and-mortar and elec-tronic retailers the operation of httptaobaocom involvesthousands of independent sellers As a result the customersrsquoperceived service quality of the platform would be consid-erably affected by their buying experience with individualsellers In the future it is worth investigating themodificationof the existing service quality-scale measurement to cater forthis specific type of newbusinessmodelThe second directionfor further studies relates to the theoretical analysis basedon more comprehensive economics analytical models Webelieve that the findings revealed by this paper can lay thefoundation for many further empirical and analytical studies

Appendix

All Proofs of Propositions

Proof of Proposition 1 From (4) we have

SFM (120582) =(119864 [120587 (120582)] minus 120573)

radic119881 [120587 (120582)] (A1)

Since 119881[120587(120582)] = 1205902 (A2) becomes

SFM (120582) =(119864 [120587 (120582)] minus 120573)

120590 (A2)

119889SFM (120582)

119889120582=119889119864 [120587 (120582)] 119889120582

120590

=119889 [119866 (119886 + 119887120582) minus 119862 (120582)] 119889120582

120590

(A3)

1198892SFM (120582)

1198891205822=1

120590(1198892

119866 (119886 + 119887120582)

1198891205822minus1198892

119862 (120582)

1198891205822) (A4)

Since 119866(119886 + 119887120582) is concave and 119862(120582) is convex1198892SFM(120582)119889120582

2

lt 0 which means SFM(120582) is concave and120582lowast

SFM uniquely exists In addition notice that 120582lowast

SFM =

arg120582[119889SFM(120582)119889120582 = 0] Thus we have 120582lowastSFM = arg

120582119878119866(120582)

119878119862(120582) = 1

Proof of Proposition 2 Part (a) notice that 120582lowast

SFM =

arg120582119878119866(120582)119878119862(120582) = 1 and

119878119866(120582) =

119889 [119866 (119886 + 119887120582)]

119889120582

=119889 (119886 + 119887120582)

119889120582

119889119866 (119886 + 119887120582)

119889 (119886 + 119887120582)= 119887

119889119866 (119886 + 119887120582)

119889 (119886 + 119887120582)

(A5)

Thus 120582lowastSFM = arg120582119878119866(120582)119878119862(120582) = 1 = arg119878

119862(120582) = 119887(119889119866(119886+

119887120582)119889(119886 + 119887120582))

8 Mathematical Problems in Engineering

Define 119891(120582) = 119878119862(120582)(119889119866(119897)119889119897)|

119897=119886+119887120582 Consider

1198911015840

(120582) =1

( (119889119866 (119897) 119889119897)|119897=119886+119887120582

)2

times ((119889119866 (119897)

119889119897

10038161003816100381610038161003816100381610038161003816119897=119886+119887120582)119889119878119862(120582)

119889120582

minus119878119862(120582)

119889

119889120582(119889119866 (119897)

119889119897

10038161003816100381610038161003816100381610038161003816119897=119886+119887120582))

(A6)

We have

(a) 119889119878119862(120582)119889120582 = 119889

2

119862(120582)1198891205822

gt 0 [∵ 119862(120582) is convex in120582]

(b) (119889119866(119897)119889119897)|119897=119886+119887120582

gt 0 [∵ 119866(119897) is increasing in 119897](c) 119878119862(120582) = 119889119862(120582)119889120582 gt 0 [∵ 119862(120582) is increasing in 120582]

(d) (119889119889120582)((119889119866(119897)119889119897)|119897=119886+119887120582

) = [1198892

119866(119897)1198891198972

sdot 119889(119897 = 119886 +

119887120582)119889120582]119897=119886+119887120582

= 119887(1198892

119866(119897)1198891198972

)119897=119886+119887120582

lt 0 [∵ 119866(119897) isconcave in 119897]

Therefore we have 1198911015840(120582) gt 0 for all 120582 In other words 119891(120582) isincreasing in 120582 Therefore a larger 119887 implies a larger120582lowastSFM

Part (b) since both 119878119866(120582) and 119878

119862(120582) are independent of 120573

and 120590 it is obvious that 120582lowastSFM = arg120582119878119866(120582)119878119862(120582) = 1 is also

independent of 120573 and 120590

Proof of Proposition 3 First notice that 119864[120587(120582)] is a concavefunction Thus 120582lowastEP uniquely exists Second from (A3) wehave

119889SFM (120582)

119889120582=119889119864 [120587 (120582)] 119889120582

120590=1

120590sdot119889119864 [120587 (120582)]

119889120582 (A7)

Obviously from (A7) we have 120582lowastSFM = arg120582[119889SFM(120582)119889120582 =

0] = arg120582[119889119864[120587(120582)]119889120582 = 0] which is the same as 120582lowastEP Thus

120582lowast

SFM = 120582lowast

EP

Authorsrsquo Contribution

All authors have good contribution to the paper and thelisting follows their surnames

Acknowledgments

The authors thank the editor and the two reviewers for theirkind comments on this paperThey declare that none of themis related to the target case company ldquohttptaobaocomrdquo andother companies named in the paper which may constitute aconflict of interest This paper is partially supported by theRGC(HK)-GRF under grant ac of PolyU 542010H

References

[1] Z Yang ldquoCustomer perceptions of service quality in internet-based electronic commercerdquo inProceedings of the 30th EuropeanMarketing Academy Conference (EMAC rsquo01) pp 8ndash11 BergenNorway 2001

[2] V A Zeithaml ldquoService excellent in electronic channelsrdquoMan-aging Service Quality vol 12 no 3 pp 135ndash139 2002

[3] Parasuraman A L Berry and V A Zeithaml ldquoSERVQUALa multiple-item scale for measuring consumer perceptions ofservice qualityrdquo Journal of Retailing vol 64 no 1 pp 12ndash401988

[4] A Parasuraman and D Grewal ldquoThe impact of technology onthe quality-value-loyalty chain a research agendardquo Journal ofthe Academy of Marketing Science vol 28 no 1 pp 168ndash1742000

[5] A C R Van Riel V Liljander and P Jurriens ldquoExploringconsumer evaluations of e-services a portal siterdquo InternationalJournal of Service Industry Management vol 12 no 4 pp 359ndash377 2001

[6] S Devaraj M Fan and R Kohli ldquoAntecedents of B2C channelsatisfaction and preference validating e-commerce metricsrdquoInformation Systems Research vol 13 no 3 pp 316ndash333 2002

[7] J Kim and J Lee ldquoCritical design factors for successful e-commerce systemsrdquoBehaviour and Information Technology vol21 no 3 pp 185ndash199 2002

[8] S Negash T Ryan and M Igbaria ldquoQuality and effectivenessin Web-based customer support systemsrdquo Information andManagement vol 40 no 8 pp 757ndash768 2003

[9] Y-F Kuo ldquoA study on service quality of virtual communitywebsitesrdquo Total Quality Management and Business Excellencevol 14 no 4 pp 461ndash473 2003

[10] F X Zhu W Wymer and I Chen ldquoIT-based services and ser-vice quality in consumer bankingrdquo International Journal of Ser-vice Industry Management vol 13 no 1 pp 69ndash90 2002

[11] J Santos ldquoE-service quality a model of virtual service qualitydimensionsrdquo Managing Service Quality vol 13 no 3 pp 233ndash246 2003

[12] F Buttle ldquoSERVQUAL review critique research agendardquo Euro-pean Journal of Marketing vol 30 no 1 pp 8ndash32 1996

[13] H Stewart C Hope and A Muhlemann ldquoProfessional servicequality a step beyond other servicesrdquo Journal of Retailing andConsumer Services vol 5 no 4 pp 209ndash222 1998

[14] J J Cronin and S A Taylor ldquoMeasuring service quality a re-examination and extensionrdquo Journal of Marketing vol 56 no 3pp 55ndash68 1992

[15] A Parasuraman L L Berry and V A Zeithaml ldquoMore onimproving service quality measurementrdquo Journal of Retailingvol 69 no 1 pp 140ndash147 1993

[16] A Parasuraman V A Zeithaml and A Malhotra ldquoE-S-QUALa multiple-item scale for assessing electronic service qualityrdquoJournal of Service Research vol 7 no 3 pp 213ndash233 2005

[17] S Akinci E Atilgan-Inan and S Aksoy ldquoRe-assessment of E-S-Qual and E-RecS-Qual in a pure service settingrdquo Journal ofBusiness Research vol 63 no 3 pp 232ndash240 2010

[18] J R Rossitier ldquoER-SERVCOMPSQUAL a measure of e-retail-ing service components qualityrdquo Service Science vol 1 no 4 pp212ndash2224 2009

[19] D X Ding P J-H Hu and O R L Sheng ldquoE-SELFQUALa scale for measuring online self-service qualityrdquo Journal ofBusiness Research vol 64 no 5 pp 508ndash515 2011

[20] S Ha and L Stoel ldquoOnline apparel retailing roles of e-shoppingquality and experiential e-shopping motivesrdquo Journal of ServiceManagement vol 23 no 2 pp 197ndash215 2012

[21] Y Hu and Q Qiang ldquoAn equilibriummodel of online shoppingsupply chain networks with service capacity investmentrdquo Ser-vice Science 2013

[22] R T Rust and A J Zahorik ldquoCustomer satisfaction customerretention and market sharerdquo Journal of Retailing vol 69 no 2pp 193ndash215 1993

Mathematical Problems in Engineering 9

[23] L Groslashnholdt A Martensen and K Kristensen ldquoThe relation-ship between customer satisfaction and loyalty cross-industrydifferencesrdquo Total Quality Management vol 11 no 4ndash6 ppS509ndashS514 2000

[24] T Xiao T M Choi D Yang and T C E Cheng ldquoService com-mitment strategy and pricing decisions in retail supply chainswith risk-averse playersrdquo Service Science vol 4 no 3 pp 236ndash252 2012

[25] T M Choi ldquoOptimal return service charging policy for afashionmass customization programrdquo Service Science vol 5 no1 pp 56ndash68 2013

[26] B Kwok The service quality of the features of Taobaocom andtheir impacts on customer loyalty [BA FYP thesis] The HongKong Polytechnic University 2011

[27] J P Robinson P R Shaver and L S Wrightsman Measures ofPersonality and Social Psychological Attitudes Academic PressSan Diego Calif USA 1991

[28] B Edvardsson M D Johnson A Gustafsson and T StrandvikldquoThe effects of satisfaction and loyalty on profits and growthproducts versus servicesrdquoTotal QualityManagement vol 11 no7 pp S917ndashS927 2000

[29] C-HChiu T-MChoi andD Li ldquoPricewall orwar the pricingstrategies for retailersrdquo IEEE Transactions on Systems Man andCybernetics A vol 39 no 2 pp 331ndash343 2009

[30] T Xiao and T-M Choi ldquoPurchasing choices and channelstructure strategies for a two-echelon system with risk-averseplayersrdquo International Journal of Production Economics vol 120no 1 pp 54ndash65 2009

[31] C-H Chiu and T-M Choi ldquoOptimal pricing and stockingdecisions for newsvendor problem with value-at-risk consider-ationrdquo IEEE Transactions on Systems Man and Cybernetics Avol 40 no 5 pp 1116ndash1119 2010

[32] C-H Chiu T-M Choi and X Li ldquoSupply chain coordinationwith risk sensitive retailer under target sales rebaterdquo Automat-ica vol 47 no 8 pp 1617ndash1625 2011

[33] T-M Choi and C-H Chiu ldquoMean-downside-risk and mean-variance newsvendor models implications for sustainable fash-ion retailingrdquo International Journal of Production Economicsvol 135 no 2 pp 552ndash560 2012

[34] T M Choi P S Chow and T Xiao ldquoElectronic price-testingscheme for fashion retailing with information updatingrdquo Inter-national Journal of Production Economics vol 140 pp 396ndash4062012

[35] TMChoi ldquoMulti-period riskminimization purchasingmodelsfor fashion products with interest rate budget and profit targetconsiderationsrdquo Annals of Operations Research 2013

[36] B Shen TM Choi YWang andC K Y Lo ldquoThe coordinationof fashion supply chains with a risk averse supplier by themarkdown money policyrdquo IEEE Transactions on Systems Manand CyberneticsmdashSystems vol 43 pp 266ndash276 2013

[37] A D Roy ldquoSafety first and the holding of assetsrdquo Econometricavol 20 no 3 pp 431ndash449 1952

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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Mathematical Problems in Engineering

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Differential EquationsInternational Journal of

Volume 2014

Applied MathematicsJournal of

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Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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Mathematical PhysicsAdvances in

Complex AnalysisJournal of

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CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

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Operations ResearchAdvances in

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Function Spaces

Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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Discrete Dynamics in Nature and Society

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Decision SciencesAdvances in

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Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Stochastic AnalysisInternational Journal of

2 Mathematical Problems in Engineering

Table 1 Featured functions of httptaobaocom

Featured functions Descriptions

Search functionSearch by keywords show items in icons or lists or by default arrangement sort searching product accordingto price credibility transaction volume and store location and lastly sort the production categories intobrand new hot items product from Taobao mall second handed or auction

Guarantee of trade safety

Each seller of httptaobaocom is required to sign the Guarantee of Trade Safety Agreement which ensuresthat she commits to provide guaranteed transaction service to customers Customers are able to request forrefund if they receive any incorrect goods The terms of guarantee include 7 days of unconditional return nocounterfeits 3 times refund for counterfeits maintenance within 30 days of purchase and express delivery

AliwangwangIt is the pervasive communication between buyer and seller prior to the purchase through its embeddedproprietary instant chat program Customers can enquire about products engage in bargaining display ofproduct image in the messenger window and make queries of the delivery status and logistic message

Alipay It is an escrow-based online payment platform It aims to enable fast money transaction with higher securityand privacy

Taodot It is an online platform that provides the value adding service for Alipay users This convenient tool enablesTaobao users who do not have an RMB account to pay for their online transactions

differentiate it from other competing OSPs and obtain itsniche Table 1 summarizes the major featured functions ofhttptaobaocom

For the research goal and contribution first by modi-fying the SERVQUAL model this paper attempts to derivethe instrument dimensions of e-service quality from thefunctions featured on httptaobaocom and to develop aresearch model to examine how its e-service quality dimen-sions affect the customer loyalty To achieve this researchobjective empirical data is collected from 195 online con-sumers (who have purchased fashion products (as a remarkin this paper in order to be more concrete we focus onexamining the consumersrsquo experience in purchasing fashionproducts) on httptaobaocom) Second based on theempirical finding this paper further analytically develops andexplores the optimal service decision on the function whichis critical to the OSP We believe that the research findingsderived from this paper can contribute to the literatureon service management and they also provide importantmanagerial insights on OSPrsquos operations

2 Literature Review

21 Review of Related Empirical Studies and ModelsSERVQUAL is a multiple-item scale for measuring customerperception of service quality [3] According to those fivedimensions of the SERVQUAL model we note that thetangibles dimension means the physical facilities and theappearance of personnel the reliability dimension refers tothe companyrsquos ability to perform the promised servicedependably and accurately the responsiveness dimensionrelates to the willingness to help customers and provideprompt service the assurance dimension means the employ-ee knowledge base which induces customer trust and con-fidence and finally the empathy dimension is about caringand individualized attention provided to customers by theservice provider

Recently SERVQUAL scale has been employed tomeasure the corresponding system service quality in e-commerce [6 7] Most existing research on e-service quality

measurement focuses on rewording the SERVQUAL scaleitems (eg [8ndash10]) The challenges in measuring web-basedservice quality mainly come from the differences betweenweb-based and traditional customer service Parasuramanand Grewal [4] suggest that revisions of the classicalSERVQUALdimensions are necessary because for web-basedservice quality customers interact with technology ratherthan the traditional service personnel Moreover severalstudies have proposed that the SERVQUAL scale items shouldbe reformulated in the online shopping context [5 11]

As a remark there are criticisms over the applicabilityof the SERVQUAL model Among them one important cri-tique is on the fact that SERVQUALrsquos five dimensions are notuniversally applicable and that the model fails to draw onestablished economic statistical and psychological theories[12] In fact the most significant criticism is the stability ofdimensions and items across different industries [13] Othercriticisms include (i) its focus on the process of servicedelivery rather than the outcomes of the service encounterand (ii) the probable respondent error from the reversedpolarity of items in the scale in the consumer survey [12 14]Parasuraman et al [15] also comment that the SERVQUAL isfar fromperfect as it is not general enough but they argue thatit does provide the basic skeleton for others to study servicequality even though some degree of customization is pro-bably needed (eg adding context-specific items) AlthoughSERVQUALrsquos construct validity is being challenged it is stillthe most widely used model employed to measure customerexpectations and perceptions of service quality

E-service quality is defined as how customers judge andevaluate the e-service being delivered to them [11] On theevaluation of websites service quality Parasuraman et al [16]introduce the E-S-QUAL and E-RecS-QUAL scales whichextend the SERVQUALmodel in evaluating the service qual-ity in the ldquoonline shopping contextrdquo E-S-QUAL is a multiple-item scale consisting of efficiency (8 items) system avail-ability (4 items) fulfillment (7 items) and privacy (3 items)dimensions E-S-QUAL provides the first formal definition ofWeb site service quality and it is a framework for consumerevaluation of electronic services Notice that Zeithaml [2]

Mathematical Problems in Engineering 3

Customer loyalty

Reliability (guarantee of trade safety)

Fulfillment and responsiveness (aliwangwang)

Flexibility (taodot)

Privacy (alipay)

H1

H2

H3

H4

H5

Efficiency (searchingfunctions)

Figure 1 The proposed ESQ-OSP e-service quality model (Customized for httptaobaocomrsquos case)

suggests that e-service quality is a multidimensional con-struct and the electronic service recovery construct involvesdimensions different from the core e-service quality From e-service perspective Parasuraman et al [16] develop E-RecS-QUAL specifically to measure e-service quality and this scalebrings out a new perspective by utilizing valuable consumerinformation on service recovery issues Actually E-RevS-QUAL is a subscale that contains items focusing on handlingservice problems and inquiries and being effective to thosewho used to have nonroutine behaviors on the online storeThe basic E-S-QUAL Scale (relevant for a Web sitersquos entirecustomer base) is a four-dimensional 22-item scale whereasE-RecS-QUAL (relevant for the portion of the customer basewith recovery service experience) is a three-dimensional scalewith 7 items E-RevS-QUAL is used to stimulate and facilitateadditional scholarly research on e-service quality and alsoassist practitioners in systematically assessing and improvinge-service quality together with E-S-QUAL [16]

Notice that Parasuraman et al [16] cannot evaluate thevalidity and psychometric properties of the scale in theirstudy due to the insufficient sample size [17] The mainreason of the limited sample size is that the number of con-sumers who encounter service problems is relatively smallerthan those who do not Hence their study is neither fullygeneric nor absolutely reliable Moreover E-S-QUAL is stilla recent model (2005) which requires more time to verifyAs a remark observe that there are different versions ofthe modified E-S-QUAL for identifying the best e-servicequality dimensions with the goal of examining differentonline businesses [17] Given the limitation of the existingmodels this paper proposes and explores an empirical model

which incorporates the service quality dimensions extractedfrom SERVQUAL E-S-QUAL and E-RevS-QUAL which webelieve can appropriately represent and study the servicequality in anOSP such as httptaobaocom (For somemorerecent literature on service quality related to online shoppingsee Rossitier [18] Ding et al [19] Ha and Stoel [20] and Huand Qiang [21])

Regarding the research motivation notice that priorstudies in the literature have suggested that perceived ser-vice quality positively influences customer satisfaction andpurchase intentions [22 23] However to the best of ourknowledge in the context of online shopping the impacts ofhow service quality dimensions affect overall e-service qualityand the customer loyalty are still under-explored This paperhence contributes to the literature by partially filling thisresearch gap Moreover by building the empirical data-basedanalytical model we explore the optimal service decision forthe OSP and derive various important insights

22 Empirical Hypotheses Development After combining andsynthesizing the existing construct of both service quality ande-service quality based on the reviewed literature we proposea perceived e-service quality construct for an OSP (ESQ-OSP) with httptaobaocom taken as the specific case Theproposed e-service quality model comprises 5 dimensionsextracted from SERVQUAL E-S-QUAL and E-RevS-QUALmodels namely efficiency reliability fulfillment and respon-siveness flexibility and privacy as shown in Figure 1

We now describe each dimension under the proposedESQ-OSPmodel First in Parasuraman et al [16] efficiency isreflected by whether the web site is simple to use structured

4 Mathematical Problems in Engineering

properly and requires a minimum amount of informationto be inputted by the customer As a result the onlineshipping platform is efficient if (i) it is easy for customersto find what they need (ii) it enables customers to completea transaction quickly and (iii) the information providedby the web site is well organized Thus service quality ofthe searching functions of httptaobaocom can reflect itsefficiency Therefore we have the following hypothesis

H1 efficiency positively influences the customer loyaltyof httptaobaocom

Reliability here refers to the consistency of performanceand dependability of companies [3] Reliability can makecustomers recognize the consistency and credibility of thecompany In e-commerce it is vital to make customers trustthe promises that the OSP made Obviously ldquoThe Guaranteeof Trade Safetyrdquo function of httptaobaocom is relatedto ensuring reliability and it is hypothesized to influencecustomer loyalty

H2 reliability positively influences the customer loyaltyof httptaobaocom

Fulfillment refers to the extent in which the websitersquospromises on order delivery and items availability are fulfilledTo be specific fulfillment is reflected by whether the OSP canensure items being available to delivery within an acceptabletime frame and guarantee quick delivery as promised [16] Forhttptaobaocom the functions of Aliwangwang are ableto represent fulfillment Responsiveness means the effectivehandling of problems and returns through the site It providescustomers with convenient options for returning items Thewebsite promises to handle product returns and offers ameaningful guarantee so that customers would feel morecomfortable [16] As one of the service quality dimen-sions contacting the seller through Aliwangwang reflectsthe responsiveness of httptaobaocom Therefore it ishypothesized that

H3 ldquofulfillment and responsivenessrdquo positively influ-ence the customer loyalty of httptaobaocom

Flexibility refers to the choice of payment methods Forexample when purchasing products in httptaobaocomTaodot is an innovative way for the users to pay onlinesafely Consumers can buy Taodot to depositadd values intheir Alipay account It is safer and shows its flexibility ifconsumers are in Hong Kong or Macau Then the followinghypothesis is proposed

H4 flexibility positively influences the customer loyaltyof httptaobaocom

Privacy refers to the degree to which the website is safeand customer information is well-protected This dimensionholds an important position in e-commerce and is critical ine-service Customers perceive significant risks in the virtualenvironment of e-commerce stemming from the possibilityof improper use of their personal financial data The privacyfunction in httptaobaocom is mainly associated withAlipay Accordingly it is hypothesized that

H5 privacy positively influences the customer loyalty ofhttptaobaocom

23 Review of Related Analytical Models In the literature onanalytical research there are many recent studies related tothe optimal service decision For example Xiao et al [24]explore the optimal service strategy with optimal pricingdecisions in a retail supply chain with risk averse playersThey consider the situation inwhich the retailer has to choosewhether to provide a service guarantee (SG) or to provideno service guarantee (NSG) They relate the optimal choiceto the supplierrsquos degree of risk aversion and the consumerrsquossensitivity to service reliabilityThey find that endogenizationof wholesale pricing decision will raise the retailerrsquos moti-vation to adopt SG strategy if the consumer is sufficientlyrisk averse Choi [25] examines the optimal service chargefor consumer product returns under mass customizationoperations He explores two cases which cover the scenariowhen the mass customization company is risk-neutral andrisk-averse He derives the closed-form expression of theoptimal return service charge for each case He analyticallyshows how the mass customization companyrsquos degree of riskaversion affects the optimal return service charge policy Inaddition he derives the analytical conditions under which itis optimal for the mass customization company to offer a freereturn with a full refund policy Hu andQiang [21] investigatean online supply chain analytical model which is consistedof manufacturers express service providers and electronicretailers They consider the situation in which consumerswho purchase products from the electronic retailers canchoose to adopt the express delivery service or simply pick upthe products themselves At the same time the express servicecompanies can invest on service quality improvement Theydevelop the variational inequality formulation and generateinsights via exploring the equilibrium As reviewed above itis evidenced that analytical modeling research is commonlyadopted to explore optimal service decisions and strategiesin the literature However to the best of our knowledgenone of them explore the problem associated with theempirical service quality framework Thus we believe thatthis paper is the first onewhich investigates the service qualityproblem forOSPs by combining both empirical and analyticalapproaches

3 Data Collection and Empirical Data Analysis

A consumer questionnaire survey is conducted 250 ques-tionnaires are distributed via social network websites undera convenience sampling approach Receivers are invited tocomplete the questionnaire if they know httptaobaocomand have visited it 190 valid questionnaires are received andemployed for statistical analysis finally

The questionnaire is divided into 8 parts The first part isused to collect the demographic data of the respondents Thesecond part is employed to examine the purchase behaviorof the respondents on httptaobaocom The rest of thequestionnaire is divided into 6 subparts to investigate theperceptions of httptaobaocom users on the 5 functionsprovided by httptaobaocom and their loyalty towards the

Mathematical Problems in Engineering 5

OSP The 7-point Likert scale is used in which 1 representsldquostrongly disagreerdquo and 7 represents ldquostrongly agreerdquo

31 Descriptive Statistics Among all the completed ques-tionnaires received 64 of the respondents have purchasedfashion products on httptaobaocom before while 36have not Thus the remaining analysis will be based on theresponses of these 64 of the collected sample in order toget valid data To be specific these consumers are consideredas httptaobaocomrsquos customers (for fashion products)The percentage of the httptaobaocom customers whobrowse products on httptaobaocom daily occupies 813 11 and 26 of httptaobaocomrsquos customers browseproducts on httptaobaocom 2-3 times a week once aweek and 2-3 times per month respectivelyThe percentagesof the httptaobaocomrsquos customers who buy products viahttptaobaocom daily 2-3 times a week once a week and2-3 times a month are 3 2 2 and 11 respectively

Regarding spending 75 of the httptaobaocomusers spend less than $500 on httptaobaocom monthly19 of them spend $500-1000 on Tobaocom monthly2 1 and 3 of the users spend $1000ndash1500 $1500ndash2000 and more than $2000 on httptaobaocom monthlyrespectively Regarding the users shopping experiences onhttptaobaocom ranking their enjoyment (in a 1ndash7 scalewith 7 being the best) more than half of the respondentsscore their enjoyment level strictly above 4 (64) 36 ofthem score as score 5 which represents the greatest group23 of them score as score 6 and 5 of them rank it 7The percentage of score 4 which means neutral excitementoccupies 15The percentages of low scores 1 2 and 3 occupy5 5 and 11 respectively

32 Hypotheses Testing Before testing hypotheses we firstconduct the reliability test to assess the internal consistencyof the variables Reliability test is measured by the Cronbachrsquosalpha in which the alpha value of 07 or above is widelyrecognized as an acceptable level of reliability [27] TheCronbach alpha values for Searching functions Guaranteeof Trade Safety Aliwangwang Alipay and Taodot are foundto be 0933 0919 0956 0958 and 0921 respectively All ofthem are higher than the acceptable level Besides the sevenquestions of customer loyalty are tested in this part TheCronbach alpha value of the customer loyalty is 0905 whichalso exceeds the acceptable level Thus we conclude that allthese dimensions are sufficiently reliable Next we proceedto test the relationships of the five featured functions andcustomer loyalty by performing multiple linear regressionanalysis It is shown that there is a statistically significantrelationship between customer loyalty and the Aliwangwangfunction The related statistical results of hypotheses aresummarized in Table 2 As such hypothesis H3 is supported(more details on the statistical analysis can be found in thesisresearch of Kwok [26])

4 Empirical Findings and Insights

From the above empirical analysis first of all we know thatthe factor of ldquofulfillment and responsiveness (Aliwangwang

function)rdquo is the most critical one as it most strongly affectscustomer loyalty of httptaobaocomrsquos customers (whopurchased fashion products before) Observe that this resultis consistent with the findings in the traditional servicecontext which suggests that customers expect quick feedbackon their requests and suggestions for improvements [2]For httptaobaocomrsquos case it can be explained by thefunction of Aliwangwang which provides instant messagingfor httptaobaocomrsquos users to send messages to the sellersThis enhances the fulfillment of httptaobaocom userson queries and needs related to product delivery and itemavailability This function also can reduce the customersrsquodoubt on the product that they want to buy and furtherconfirm their purchase decision In addition the customerscan contact the sellers whenever the sellers are online witha simple click This function extends the accessibility of thesellersonline stores without the control of the store operatinghours With Aliwangwang httptaobaocom users are ableto obtain adequate product information even if it is not givenon the product web page It increases the convenience ofhttptaobaocom Since it is a unique feature provided byhttptaobaocom but not its close competitors it becomes amajor niche for httptaobaocom

Second observe that only one hypothesis among theproposed five is proven to be true statistically A possiblereason may be given as follows Unlike the traditionalphysical store or other kinds of online electronic retailerssuch as httpwwwamazoncom etc httptaobaocomis an online marketplace in which there are thousands ofindependent retailers As a result customersrsquo perception onthe service quality and their loyalty towards the OSP maybe affected by their buying experience with the individ-ual retailers Owing to the fundamental differences manyhypotheses which apply for the more traditional bricks-and-mortar retailingelectronic retailing do not hold for theOSPs such as httptaobaocom In addition notice that inhttptaobaocom communication with individual sellers isespecially important In fact customers would like to gathermore information so that they can make better choice from alarge variety of independent retailers selling similar productson the platformThis accounts for the support ofHypothesis 3(which is about Aliwangwang and related to communicationaspect)

Third we know that a higher level of ldquofulfillment andresponsiveness (Aliwangwang function)rdquo will lead to a higherlevel of customer loyalty According to Edvardsson et al[28] customer loyalty will positively affect the revenue ofthe OSP it will be thus interesting to analytically examinewhether there exists an optimal level of ldquofulfillment andresponsivenessrdquo for the OSP We hence further conduct ananalytical study in the next section

5 Analytical Service OptimizationModel and Findings

Based on the empirical findings from Section 4 we proceedto build an analytical model for further analysis First fromthe linear regression analysis conducted in Section 4 (seeTable 2) we notice that the level of customer loyalty 119897 is related

6 Mathematical Problems in Engineering

Table 2 Summary of regression analysis

Corresponding hypothesis Independent variable Standardized coefficient t-value SigHypothesis 1 Efficiency minus0057 minus0575 0573Hypothesis 2 Reliability 0087 0896 0383Hypothesis 3 Fulfillment amp responsiveness 0582 2255 0038lowastlowast

Hypothesis 4 Flexibility 0062 0406 0690Hypothesis 5 Privacy 0307 1391 0182Remarksdependent variable customer loyaltylowastlowasthypothesis is supported

to the level of ldquofulfillment and responsivenessrdquo 120582 in a linearfunction as follows

119897 = 119886 + 119887120582 (1)

where 119886 119887 gt 0Second since it is intuitive that a higher level of customer

loyalty 119897will ldquostochasticallyrdquo (itmeans expectedly the revenueshould be higher but the exactmagnitude is unknown) lead toa higher revenue 119877(119897) for the OSP such as httptaobaocomwe have the following

119877 (119897) = 119866 (119897) + 120576 (2)

where (i) 119866(119897) is an increasing function of 119897 For analyticaltractability we further assume 119866(119897) as a concave function(ii) 120576 is a random variable which captures the noise (ieuncertainty) associated with 119877(119897) Following the literature[29] we model it to follow a symmetric distribution suchas the normal distribution with a zero mean and constantvariance 1205902

Third providing a higher level of ldquofulfillment and respon-sivenessrdquo120582 requires resourceWe hence define the respectivecost as 119862(120582) which is an increasing function of 120582 Similarto 119866(119897) we assume 119862(120582) as a convex function for analyticaltractability Notice that the above functional definitions areall intuitive and rather general

With the above model we can derive the profit 120587(120582) theexpected profit 119864[120587(120582)] and the variance of profit119881[120587(120582)] asfunctions of 120582 as follows

120587 (120582) = 119877 (119897) minus 119862 (120582)

= 119866 (119886 + 119887120582) minus 119862 (120582) + 120576

119864 [120587 (120582)] = 119866 (119886 + 119887120582) minus 119862 (120582)

119881 [120587 (120582)] = 1205902

(3)

Because e-business is known to be a risky operation weargue that the OSP has concern on risk and possesses a riskaverse attitude [24] As such we employ the following mean-variance safety first measure (SFM(120582)) as the objective func-tion (for the modeling of risk averse optimization objectivessee Xiao and Choi [30] Chiu and Choi [31] Chiu et al [32]Choi and Chiu [33] Choi et al [34] Choi [35] and Shen etal [36]) for the OSP as follows

SFM (120582) =(119864 [120587 (120582)] minus 120573)

radic119881 [120587 (120582)] (4)

where 120573 is the expected profit target that the OSP wants toachieve

For the meaning behind SFM(120582) please notice thatby Bienayme-Tchebycheff inequality we have the followinginequality

119875 (|120587 (120582) minus 119864 [120587 (120582)]| ge 119864 [120587 (120582)] minus 120573) le119881 [120587 (120582)]

(119864 [120587 (120582)] minus 120573)2

(5)

997904rArr 119875 (120587 (120582) le 120573) le119881 [120587 (120582)]

(119864 [120587 (120582)] minus 120573)2equiv

1

SFM(120582)2 (6)

Thus the RHS of (6) gives the analytical upper boundfor 119875(120587(120582) le 120573) Obviously maximizing SFM(120582) yields aminimized upper bound for 119875(120587(120582) le 120573) and hence SFM(120582)

is called the safety first measure (see [37])With the above model we define (7) and then have

Proposition 1 Notice that 119878119866(120582) and 119878

119862(120582) are the first order

derivatives of 119866(119886 + 119887120582) and 119862(120582) respectively and they alsorepresent the corresponding slopes as follows

120582lowast

SFM = argmax120582

SFM (120582)

120582lowast

EP = argmax120582

119864 [120587 (120582)]

119878119866(120582) =

119889119866 (119886 + 119887120582)

119889120582

119878119862(120582) =

119889119862 (120582)

119889120582

(7)

Proposition 1 120582lowast119878119865119872

uniquely exists and it is equal toarg120582119878119866(120582)119878119862(120582) = 1

Proof of Proposition 1 All proofs are placed in the Appendix

Proposition 1 first presents the uniqueness feature of theoptimal level of fulfillment and responsiveness (ie optimalservice level) which maximizes the safety first objectiveIt then reveals that this optimal service level is the onewhich equalizes 119878

119866(120582) and 119878

119862(120582) In other words when the

rates of change of 119866(119886 + 119887120582) and 119862(120582) are the same thecorresponding service level 120582 is optimal From Proposition 1we have Proposition 2

Mathematical Problems in Engineering 7

Proposition 2 (a) A larger 119887 implies a larger 120582lowastSFM (b) 120582lowast

SFMis independent of 120573 and 120590

Observe that a larger 119887 means that the customer loy-alty is more positively correlated to the service level 120582Proposition 2(a) hence indicates that it will lead to a biggeroptimal service level because the optimal solution refersto the one which balances the increase of revenue 119878

119866(120582)

and rise of cost 119878119862(120582) It is also interesting to note from

Proposition 2(b) that 120582lowastSFM is independent of 120573 and 120590 Thisfinding can be explained by the fact that the variance of profit119881[120587(120582)] is a constant which is independent of the service level120582Thus the corresponding optimal service level is not relatedto 120590 as well as the expected profit target 120573 and will in factbehave like the risk neutral case as shown in Proposition 3below

Proposition 3 120582lowast119864119875

uniquely exists and 120582lowastSFM = 120582lowast

119864119875

Proposition 3 shows a rather surprising result that the riskaverse and risk neutral OSPs will have the same optimal ser-vice levels for this particular case In other words irrespectiveof the risk preference the optimal service level is the sameThis finding is important because it shows the fundamentaldifference between different kinds of operations manage-ment problems For example for inventory planning underuncertainty the optimal inventory decision for the risk averseand risk neutral cases will be different However in serviceoptimization the situation can be different as shown here

6 Concluding Remarks

We conclude by discussing some research implicationsderived from the discussions above First the empiricalfindings suggest that in order to enhance customer loyaltyonline stores should develop marketing strategies to enhanceldquofulfillment and responsivenessrdquo since they strongly affectcustomer loyalty httptaobaocom has devoted valuablecorporate resources to it andwe argue that httptaobaocomcan further improve the functions of Aliwangwang toachieve even better communication between the sellersand httptaobaocom users Some probable enhancementsincluding inserting flash games and converting to smart-phone apps can all help to attract the httptaobaocom usersto use Aliwangwang Furthermore httptaobaocom canmake use of Aliwangwang to obtain personalized data relatedto the customersrsquo preferences As a result a higher degreeof web site customization (eg based on past purchases andother customersrsquo information collected) can be provided toindividual customers

Second for the empirical result driven analytical studieswe find that the optimal service level on ldquofulfillment andresponsivenessrdquo function for the risk averse OSP uniquelyexists and can be expressed as the service level which balancesthe increase of revenue 119878

119866(120582) and the rise of cost 119878

119862(120582) We

further show that if the customerrsquos loyalty is more positivelycorrelated to the service level 120582 it will lead to a larger optimalservice level Moreover the optimal service level under thesafety first objective is independent of profit target 120573 and

uncertainty threshold120590 Finally we analytically prove that theoptimal service level does not depend on the risk preferenceof the OSP

For future research there are two directions Empiricallyit is useful to examine more functions of OSP such ashttptaobaocom This paper only focuses on the severalcore functions specifically adopted by httptaobaocom andthere are some more which deserve further explorationsBesides unlike the traditional bricks-and-mortar and elec-tronic retailers the operation of httptaobaocom involvesthousands of independent sellers As a result the customersrsquoperceived service quality of the platform would be consid-erably affected by their buying experience with individualsellers In the future it is worth investigating themodificationof the existing service quality-scale measurement to cater forthis specific type of newbusinessmodelThe second directionfor further studies relates to the theoretical analysis basedon more comprehensive economics analytical models Webelieve that the findings revealed by this paper can lay thefoundation for many further empirical and analytical studies

Appendix

All Proofs of Propositions

Proof of Proposition 1 From (4) we have

SFM (120582) =(119864 [120587 (120582)] minus 120573)

radic119881 [120587 (120582)] (A1)

Since 119881[120587(120582)] = 1205902 (A2) becomes

SFM (120582) =(119864 [120587 (120582)] minus 120573)

120590 (A2)

119889SFM (120582)

119889120582=119889119864 [120587 (120582)] 119889120582

120590

=119889 [119866 (119886 + 119887120582) minus 119862 (120582)] 119889120582

120590

(A3)

1198892SFM (120582)

1198891205822=1

120590(1198892

119866 (119886 + 119887120582)

1198891205822minus1198892

119862 (120582)

1198891205822) (A4)

Since 119866(119886 + 119887120582) is concave and 119862(120582) is convex1198892SFM(120582)119889120582

2

lt 0 which means SFM(120582) is concave and120582lowast

SFM uniquely exists In addition notice that 120582lowast

SFM =

arg120582[119889SFM(120582)119889120582 = 0] Thus we have 120582lowastSFM = arg

120582119878119866(120582)

119878119862(120582) = 1

Proof of Proposition 2 Part (a) notice that 120582lowast

SFM =

arg120582119878119866(120582)119878119862(120582) = 1 and

119878119866(120582) =

119889 [119866 (119886 + 119887120582)]

119889120582

=119889 (119886 + 119887120582)

119889120582

119889119866 (119886 + 119887120582)

119889 (119886 + 119887120582)= 119887

119889119866 (119886 + 119887120582)

119889 (119886 + 119887120582)

(A5)

Thus 120582lowastSFM = arg120582119878119866(120582)119878119862(120582) = 1 = arg119878

119862(120582) = 119887(119889119866(119886+

119887120582)119889(119886 + 119887120582))

8 Mathematical Problems in Engineering

Define 119891(120582) = 119878119862(120582)(119889119866(119897)119889119897)|

119897=119886+119887120582 Consider

1198911015840

(120582) =1

( (119889119866 (119897) 119889119897)|119897=119886+119887120582

)2

times ((119889119866 (119897)

119889119897

10038161003816100381610038161003816100381610038161003816119897=119886+119887120582)119889119878119862(120582)

119889120582

minus119878119862(120582)

119889

119889120582(119889119866 (119897)

119889119897

10038161003816100381610038161003816100381610038161003816119897=119886+119887120582))

(A6)

We have

(a) 119889119878119862(120582)119889120582 = 119889

2

119862(120582)1198891205822

gt 0 [∵ 119862(120582) is convex in120582]

(b) (119889119866(119897)119889119897)|119897=119886+119887120582

gt 0 [∵ 119866(119897) is increasing in 119897](c) 119878119862(120582) = 119889119862(120582)119889120582 gt 0 [∵ 119862(120582) is increasing in 120582]

(d) (119889119889120582)((119889119866(119897)119889119897)|119897=119886+119887120582

) = [1198892

119866(119897)1198891198972

sdot 119889(119897 = 119886 +

119887120582)119889120582]119897=119886+119887120582

= 119887(1198892

119866(119897)1198891198972

)119897=119886+119887120582

lt 0 [∵ 119866(119897) isconcave in 119897]

Therefore we have 1198911015840(120582) gt 0 for all 120582 In other words 119891(120582) isincreasing in 120582 Therefore a larger 119887 implies a larger120582lowastSFM

Part (b) since both 119878119866(120582) and 119878

119862(120582) are independent of 120573

and 120590 it is obvious that 120582lowastSFM = arg120582119878119866(120582)119878119862(120582) = 1 is also

independent of 120573 and 120590

Proof of Proposition 3 First notice that 119864[120587(120582)] is a concavefunction Thus 120582lowastEP uniquely exists Second from (A3) wehave

119889SFM (120582)

119889120582=119889119864 [120587 (120582)] 119889120582

120590=1

120590sdot119889119864 [120587 (120582)]

119889120582 (A7)

Obviously from (A7) we have 120582lowastSFM = arg120582[119889SFM(120582)119889120582 =

0] = arg120582[119889119864[120587(120582)]119889120582 = 0] which is the same as 120582lowastEP Thus

120582lowast

SFM = 120582lowast

EP

Authorsrsquo Contribution

All authors have good contribution to the paper and thelisting follows their surnames

Acknowledgments

The authors thank the editor and the two reviewers for theirkind comments on this paperThey declare that none of themis related to the target case company ldquohttptaobaocomrdquo andother companies named in the paper which may constitute aconflict of interest This paper is partially supported by theRGC(HK)-GRF under grant ac of PolyU 542010H

References

[1] Z Yang ldquoCustomer perceptions of service quality in internet-based electronic commercerdquo inProceedings of the 30th EuropeanMarketing Academy Conference (EMAC rsquo01) pp 8ndash11 BergenNorway 2001

[2] V A Zeithaml ldquoService excellent in electronic channelsrdquoMan-aging Service Quality vol 12 no 3 pp 135ndash139 2002

[3] Parasuraman A L Berry and V A Zeithaml ldquoSERVQUALa multiple-item scale for measuring consumer perceptions ofservice qualityrdquo Journal of Retailing vol 64 no 1 pp 12ndash401988

[4] A Parasuraman and D Grewal ldquoThe impact of technology onthe quality-value-loyalty chain a research agendardquo Journal ofthe Academy of Marketing Science vol 28 no 1 pp 168ndash1742000

[5] A C R Van Riel V Liljander and P Jurriens ldquoExploringconsumer evaluations of e-services a portal siterdquo InternationalJournal of Service Industry Management vol 12 no 4 pp 359ndash377 2001

[6] S Devaraj M Fan and R Kohli ldquoAntecedents of B2C channelsatisfaction and preference validating e-commerce metricsrdquoInformation Systems Research vol 13 no 3 pp 316ndash333 2002

[7] J Kim and J Lee ldquoCritical design factors for successful e-commerce systemsrdquoBehaviour and Information Technology vol21 no 3 pp 185ndash199 2002

[8] S Negash T Ryan and M Igbaria ldquoQuality and effectivenessin Web-based customer support systemsrdquo Information andManagement vol 40 no 8 pp 757ndash768 2003

[9] Y-F Kuo ldquoA study on service quality of virtual communitywebsitesrdquo Total Quality Management and Business Excellencevol 14 no 4 pp 461ndash473 2003

[10] F X Zhu W Wymer and I Chen ldquoIT-based services and ser-vice quality in consumer bankingrdquo International Journal of Ser-vice Industry Management vol 13 no 1 pp 69ndash90 2002

[11] J Santos ldquoE-service quality a model of virtual service qualitydimensionsrdquo Managing Service Quality vol 13 no 3 pp 233ndash246 2003

[12] F Buttle ldquoSERVQUAL review critique research agendardquo Euro-pean Journal of Marketing vol 30 no 1 pp 8ndash32 1996

[13] H Stewart C Hope and A Muhlemann ldquoProfessional servicequality a step beyond other servicesrdquo Journal of Retailing andConsumer Services vol 5 no 4 pp 209ndash222 1998

[14] J J Cronin and S A Taylor ldquoMeasuring service quality a re-examination and extensionrdquo Journal of Marketing vol 56 no 3pp 55ndash68 1992

[15] A Parasuraman L L Berry and V A Zeithaml ldquoMore onimproving service quality measurementrdquo Journal of Retailingvol 69 no 1 pp 140ndash147 1993

[16] A Parasuraman V A Zeithaml and A Malhotra ldquoE-S-QUALa multiple-item scale for assessing electronic service qualityrdquoJournal of Service Research vol 7 no 3 pp 213ndash233 2005

[17] S Akinci E Atilgan-Inan and S Aksoy ldquoRe-assessment of E-S-Qual and E-RecS-Qual in a pure service settingrdquo Journal ofBusiness Research vol 63 no 3 pp 232ndash240 2010

[18] J R Rossitier ldquoER-SERVCOMPSQUAL a measure of e-retail-ing service components qualityrdquo Service Science vol 1 no 4 pp212ndash2224 2009

[19] D X Ding P J-H Hu and O R L Sheng ldquoE-SELFQUALa scale for measuring online self-service qualityrdquo Journal ofBusiness Research vol 64 no 5 pp 508ndash515 2011

[20] S Ha and L Stoel ldquoOnline apparel retailing roles of e-shoppingquality and experiential e-shopping motivesrdquo Journal of ServiceManagement vol 23 no 2 pp 197ndash215 2012

[21] Y Hu and Q Qiang ldquoAn equilibriummodel of online shoppingsupply chain networks with service capacity investmentrdquo Ser-vice Science 2013

[22] R T Rust and A J Zahorik ldquoCustomer satisfaction customerretention and market sharerdquo Journal of Retailing vol 69 no 2pp 193ndash215 1993

Mathematical Problems in Engineering 9

[23] L Groslashnholdt A Martensen and K Kristensen ldquoThe relation-ship between customer satisfaction and loyalty cross-industrydifferencesrdquo Total Quality Management vol 11 no 4ndash6 ppS509ndashS514 2000

[24] T Xiao T M Choi D Yang and T C E Cheng ldquoService com-mitment strategy and pricing decisions in retail supply chainswith risk-averse playersrdquo Service Science vol 4 no 3 pp 236ndash252 2012

[25] T M Choi ldquoOptimal return service charging policy for afashionmass customization programrdquo Service Science vol 5 no1 pp 56ndash68 2013

[26] B Kwok The service quality of the features of Taobaocom andtheir impacts on customer loyalty [BA FYP thesis] The HongKong Polytechnic University 2011

[27] J P Robinson P R Shaver and L S Wrightsman Measures ofPersonality and Social Psychological Attitudes Academic PressSan Diego Calif USA 1991

[28] B Edvardsson M D Johnson A Gustafsson and T StrandvikldquoThe effects of satisfaction and loyalty on profits and growthproducts versus servicesrdquoTotal QualityManagement vol 11 no7 pp S917ndashS927 2000

[29] C-HChiu T-MChoi andD Li ldquoPricewall orwar the pricingstrategies for retailersrdquo IEEE Transactions on Systems Man andCybernetics A vol 39 no 2 pp 331ndash343 2009

[30] T Xiao and T-M Choi ldquoPurchasing choices and channelstructure strategies for a two-echelon system with risk-averseplayersrdquo International Journal of Production Economics vol 120no 1 pp 54ndash65 2009

[31] C-H Chiu and T-M Choi ldquoOptimal pricing and stockingdecisions for newsvendor problem with value-at-risk consider-ationrdquo IEEE Transactions on Systems Man and Cybernetics Avol 40 no 5 pp 1116ndash1119 2010

[32] C-H Chiu T-M Choi and X Li ldquoSupply chain coordinationwith risk sensitive retailer under target sales rebaterdquo Automat-ica vol 47 no 8 pp 1617ndash1625 2011

[33] T-M Choi and C-H Chiu ldquoMean-downside-risk and mean-variance newsvendor models implications for sustainable fash-ion retailingrdquo International Journal of Production Economicsvol 135 no 2 pp 552ndash560 2012

[34] T M Choi P S Chow and T Xiao ldquoElectronic price-testingscheme for fashion retailing with information updatingrdquo Inter-national Journal of Production Economics vol 140 pp 396ndash4062012

[35] TMChoi ldquoMulti-period riskminimization purchasingmodelsfor fashion products with interest rate budget and profit targetconsiderationsrdquo Annals of Operations Research 2013

[36] B Shen TM Choi YWang andC K Y Lo ldquoThe coordinationof fashion supply chains with a risk averse supplier by themarkdown money policyrdquo IEEE Transactions on Systems Manand CyberneticsmdashSystems vol 43 pp 266ndash276 2013

[37] A D Roy ldquoSafety first and the holding of assetsrdquo Econometricavol 20 no 3 pp 431ndash449 1952

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical Problems in Engineering

Hindawi Publishing Corporationhttpwwwhindawicom

Differential EquationsInternational Journal of

Volume 2014

Applied MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical PhysicsAdvances in

Complex AnalysisJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

OptimizationJournal of

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CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Operations ResearchAdvances in

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Function Spaces

Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of Mathematics and Mathematical Sciences

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The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

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Algebra

Discrete Dynamics in Nature and Society

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Decision SciencesAdvances in

Discrete MathematicsJournal of

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Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Stochastic AnalysisInternational Journal of

Mathematical Problems in Engineering 3

Customer loyalty

Reliability (guarantee of trade safety)

Fulfillment and responsiveness (aliwangwang)

Flexibility (taodot)

Privacy (alipay)

H1

H2

H3

H4

H5

Efficiency (searchingfunctions)

Figure 1 The proposed ESQ-OSP e-service quality model (Customized for httptaobaocomrsquos case)

suggests that e-service quality is a multidimensional con-struct and the electronic service recovery construct involvesdimensions different from the core e-service quality From e-service perspective Parasuraman et al [16] develop E-RecS-QUAL specifically to measure e-service quality and this scalebrings out a new perspective by utilizing valuable consumerinformation on service recovery issues Actually E-RevS-QUAL is a subscale that contains items focusing on handlingservice problems and inquiries and being effective to thosewho used to have nonroutine behaviors on the online storeThe basic E-S-QUAL Scale (relevant for a Web sitersquos entirecustomer base) is a four-dimensional 22-item scale whereasE-RecS-QUAL (relevant for the portion of the customer basewith recovery service experience) is a three-dimensional scalewith 7 items E-RevS-QUAL is used to stimulate and facilitateadditional scholarly research on e-service quality and alsoassist practitioners in systematically assessing and improvinge-service quality together with E-S-QUAL [16]

Notice that Parasuraman et al [16] cannot evaluate thevalidity and psychometric properties of the scale in theirstudy due to the insufficient sample size [17] The mainreason of the limited sample size is that the number of con-sumers who encounter service problems is relatively smallerthan those who do not Hence their study is neither fullygeneric nor absolutely reliable Moreover E-S-QUAL is stilla recent model (2005) which requires more time to verifyAs a remark observe that there are different versions ofthe modified E-S-QUAL for identifying the best e-servicequality dimensions with the goal of examining differentonline businesses [17] Given the limitation of the existingmodels this paper proposes and explores an empirical model

which incorporates the service quality dimensions extractedfrom SERVQUAL E-S-QUAL and E-RevS-QUAL which webelieve can appropriately represent and study the servicequality in anOSP such as httptaobaocom (For somemorerecent literature on service quality related to online shoppingsee Rossitier [18] Ding et al [19] Ha and Stoel [20] and Huand Qiang [21])

Regarding the research motivation notice that priorstudies in the literature have suggested that perceived ser-vice quality positively influences customer satisfaction andpurchase intentions [22 23] However to the best of ourknowledge in the context of online shopping the impacts ofhow service quality dimensions affect overall e-service qualityand the customer loyalty are still under-explored This paperhence contributes to the literature by partially filling thisresearch gap Moreover by building the empirical data-basedanalytical model we explore the optimal service decision forthe OSP and derive various important insights

22 Empirical Hypotheses Development After combining andsynthesizing the existing construct of both service quality ande-service quality based on the reviewed literature we proposea perceived e-service quality construct for an OSP (ESQ-OSP) with httptaobaocom taken as the specific case Theproposed e-service quality model comprises 5 dimensionsextracted from SERVQUAL E-S-QUAL and E-RevS-QUALmodels namely efficiency reliability fulfillment and respon-siveness flexibility and privacy as shown in Figure 1

We now describe each dimension under the proposedESQ-OSPmodel First in Parasuraman et al [16] efficiency isreflected by whether the web site is simple to use structured

4 Mathematical Problems in Engineering

properly and requires a minimum amount of informationto be inputted by the customer As a result the onlineshipping platform is efficient if (i) it is easy for customersto find what they need (ii) it enables customers to completea transaction quickly and (iii) the information providedby the web site is well organized Thus service quality ofthe searching functions of httptaobaocom can reflect itsefficiency Therefore we have the following hypothesis

H1 efficiency positively influences the customer loyaltyof httptaobaocom

Reliability here refers to the consistency of performanceand dependability of companies [3] Reliability can makecustomers recognize the consistency and credibility of thecompany In e-commerce it is vital to make customers trustthe promises that the OSP made Obviously ldquoThe Guaranteeof Trade Safetyrdquo function of httptaobaocom is relatedto ensuring reliability and it is hypothesized to influencecustomer loyalty

H2 reliability positively influences the customer loyaltyof httptaobaocom

Fulfillment refers to the extent in which the websitersquospromises on order delivery and items availability are fulfilledTo be specific fulfillment is reflected by whether the OSP canensure items being available to delivery within an acceptabletime frame and guarantee quick delivery as promised [16] Forhttptaobaocom the functions of Aliwangwang are ableto represent fulfillment Responsiveness means the effectivehandling of problems and returns through the site It providescustomers with convenient options for returning items Thewebsite promises to handle product returns and offers ameaningful guarantee so that customers would feel morecomfortable [16] As one of the service quality dimen-sions contacting the seller through Aliwangwang reflectsthe responsiveness of httptaobaocom Therefore it ishypothesized that

H3 ldquofulfillment and responsivenessrdquo positively influ-ence the customer loyalty of httptaobaocom

Flexibility refers to the choice of payment methods Forexample when purchasing products in httptaobaocomTaodot is an innovative way for the users to pay onlinesafely Consumers can buy Taodot to depositadd values intheir Alipay account It is safer and shows its flexibility ifconsumers are in Hong Kong or Macau Then the followinghypothesis is proposed

H4 flexibility positively influences the customer loyaltyof httptaobaocom

Privacy refers to the degree to which the website is safeand customer information is well-protected This dimensionholds an important position in e-commerce and is critical ine-service Customers perceive significant risks in the virtualenvironment of e-commerce stemming from the possibilityof improper use of their personal financial data The privacyfunction in httptaobaocom is mainly associated withAlipay Accordingly it is hypothesized that

H5 privacy positively influences the customer loyalty ofhttptaobaocom

23 Review of Related Analytical Models In the literature onanalytical research there are many recent studies related tothe optimal service decision For example Xiao et al [24]explore the optimal service strategy with optimal pricingdecisions in a retail supply chain with risk averse playersThey consider the situation inwhich the retailer has to choosewhether to provide a service guarantee (SG) or to provideno service guarantee (NSG) They relate the optimal choiceto the supplierrsquos degree of risk aversion and the consumerrsquossensitivity to service reliabilityThey find that endogenizationof wholesale pricing decision will raise the retailerrsquos moti-vation to adopt SG strategy if the consumer is sufficientlyrisk averse Choi [25] examines the optimal service chargefor consumer product returns under mass customizationoperations He explores two cases which cover the scenariowhen the mass customization company is risk-neutral andrisk-averse He derives the closed-form expression of theoptimal return service charge for each case He analyticallyshows how the mass customization companyrsquos degree of riskaversion affects the optimal return service charge policy Inaddition he derives the analytical conditions under which itis optimal for the mass customization company to offer a freereturn with a full refund policy Hu andQiang [21] investigatean online supply chain analytical model which is consistedof manufacturers express service providers and electronicretailers They consider the situation in which consumerswho purchase products from the electronic retailers canchoose to adopt the express delivery service or simply pick upthe products themselves At the same time the express servicecompanies can invest on service quality improvement Theydevelop the variational inequality formulation and generateinsights via exploring the equilibrium As reviewed above itis evidenced that analytical modeling research is commonlyadopted to explore optimal service decisions and strategiesin the literature However to the best of our knowledgenone of them explore the problem associated with theempirical service quality framework Thus we believe thatthis paper is the first onewhich investigates the service qualityproblem forOSPs by combining both empirical and analyticalapproaches

3 Data Collection and Empirical Data Analysis

A consumer questionnaire survey is conducted 250 ques-tionnaires are distributed via social network websites undera convenience sampling approach Receivers are invited tocomplete the questionnaire if they know httptaobaocomand have visited it 190 valid questionnaires are received andemployed for statistical analysis finally

The questionnaire is divided into 8 parts The first part isused to collect the demographic data of the respondents Thesecond part is employed to examine the purchase behaviorof the respondents on httptaobaocom The rest of thequestionnaire is divided into 6 subparts to investigate theperceptions of httptaobaocom users on the 5 functionsprovided by httptaobaocom and their loyalty towards the

Mathematical Problems in Engineering 5

OSP The 7-point Likert scale is used in which 1 representsldquostrongly disagreerdquo and 7 represents ldquostrongly agreerdquo

31 Descriptive Statistics Among all the completed ques-tionnaires received 64 of the respondents have purchasedfashion products on httptaobaocom before while 36have not Thus the remaining analysis will be based on theresponses of these 64 of the collected sample in order toget valid data To be specific these consumers are consideredas httptaobaocomrsquos customers (for fashion products)The percentage of the httptaobaocom customers whobrowse products on httptaobaocom daily occupies 813 11 and 26 of httptaobaocomrsquos customers browseproducts on httptaobaocom 2-3 times a week once aweek and 2-3 times per month respectivelyThe percentagesof the httptaobaocomrsquos customers who buy products viahttptaobaocom daily 2-3 times a week once a week and2-3 times a month are 3 2 2 and 11 respectively

Regarding spending 75 of the httptaobaocomusers spend less than $500 on httptaobaocom monthly19 of them spend $500-1000 on Tobaocom monthly2 1 and 3 of the users spend $1000ndash1500 $1500ndash2000 and more than $2000 on httptaobaocom monthlyrespectively Regarding the users shopping experiences onhttptaobaocom ranking their enjoyment (in a 1ndash7 scalewith 7 being the best) more than half of the respondentsscore their enjoyment level strictly above 4 (64) 36 ofthem score as score 5 which represents the greatest group23 of them score as score 6 and 5 of them rank it 7The percentage of score 4 which means neutral excitementoccupies 15The percentages of low scores 1 2 and 3 occupy5 5 and 11 respectively

32 Hypotheses Testing Before testing hypotheses we firstconduct the reliability test to assess the internal consistencyof the variables Reliability test is measured by the Cronbachrsquosalpha in which the alpha value of 07 or above is widelyrecognized as an acceptable level of reliability [27] TheCronbach alpha values for Searching functions Guaranteeof Trade Safety Aliwangwang Alipay and Taodot are foundto be 0933 0919 0956 0958 and 0921 respectively All ofthem are higher than the acceptable level Besides the sevenquestions of customer loyalty are tested in this part TheCronbach alpha value of the customer loyalty is 0905 whichalso exceeds the acceptable level Thus we conclude that allthese dimensions are sufficiently reliable Next we proceedto test the relationships of the five featured functions andcustomer loyalty by performing multiple linear regressionanalysis It is shown that there is a statistically significantrelationship between customer loyalty and the Aliwangwangfunction The related statistical results of hypotheses aresummarized in Table 2 As such hypothesis H3 is supported(more details on the statistical analysis can be found in thesisresearch of Kwok [26])

4 Empirical Findings and Insights

From the above empirical analysis first of all we know thatthe factor of ldquofulfillment and responsiveness (Aliwangwang

function)rdquo is the most critical one as it most strongly affectscustomer loyalty of httptaobaocomrsquos customers (whopurchased fashion products before) Observe that this resultis consistent with the findings in the traditional servicecontext which suggests that customers expect quick feedbackon their requests and suggestions for improvements [2]For httptaobaocomrsquos case it can be explained by thefunction of Aliwangwang which provides instant messagingfor httptaobaocomrsquos users to send messages to the sellersThis enhances the fulfillment of httptaobaocom userson queries and needs related to product delivery and itemavailability This function also can reduce the customersrsquodoubt on the product that they want to buy and furtherconfirm their purchase decision In addition the customerscan contact the sellers whenever the sellers are online witha simple click This function extends the accessibility of thesellersonline stores without the control of the store operatinghours With Aliwangwang httptaobaocom users are ableto obtain adequate product information even if it is not givenon the product web page It increases the convenience ofhttptaobaocom Since it is a unique feature provided byhttptaobaocom but not its close competitors it becomes amajor niche for httptaobaocom

Second observe that only one hypothesis among theproposed five is proven to be true statistically A possiblereason may be given as follows Unlike the traditionalphysical store or other kinds of online electronic retailerssuch as httpwwwamazoncom etc httptaobaocomis an online marketplace in which there are thousands ofindependent retailers As a result customersrsquo perception onthe service quality and their loyalty towards the OSP maybe affected by their buying experience with the individ-ual retailers Owing to the fundamental differences manyhypotheses which apply for the more traditional bricks-and-mortar retailingelectronic retailing do not hold for theOSPs such as httptaobaocom In addition notice that inhttptaobaocom communication with individual sellers isespecially important In fact customers would like to gathermore information so that they can make better choice from alarge variety of independent retailers selling similar productson the platformThis accounts for the support ofHypothesis 3(which is about Aliwangwang and related to communicationaspect)

Third we know that a higher level of ldquofulfillment andresponsiveness (Aliwangwang function)rdquo will lead to a higherlevel of customer loyalty According to Edvardsson et al[28] customer loyalty will positively affect the revenue ofthe OSP it will be thus interesting to analytically examinewhether there exists an optimal level of ldquofulfillment andresponsivenessrdquo for the OSP We hence further conduct ananalytical study in the next section

5 Analytical Service OptimizationModel and Findings

Based on the empirical findings from Section 4 we proceedto build an analytical model for further analysis First fromthe linear regression analysis conducted in Section 4 (seeTable 2) we notice that the level of customer loyalty 119897 is related

6 Mathematical Problems in Engineering

Table 2 Summary of regression analysis

Corresponding hypothesis Independent variable Standardized coefficient t-value SigHypothesis 1 Efficiency minus0057 minus0575 0573Hypothesis 2 Reliability 0087 0896 0383Hypothesis 3 Fulfillment amp responsiveness 0582 2255 0038lowastlowast

Hypothesis 4 Flexibility 0062 0406 0690Hypothesis 5 Privacy 0307 1391 0182Remarksdependent variable customer loyaltylowastlowasthypothesis is supported

to the level of ldquofulfillment and responsivenessrdquo 120582 in a linearfunction as follows

119897 = 119886 + 119887120582 (1)

where 119886 119887 gt 0Second since it is intuitive that a higher level of customer

loyalty 119897will ldquostochasticallyrdquo (itmeans expectedly the revenueshould be higher but the exactmagnitude is unknown) lead toa higher revenue 119877(119897) for the OSP such as httptaobaocomwe have the following

119877 (119897) = 119866 (119897) + 120576 (2)

where (i) 119866(119897) is an increasing function of 119897 For analyticaltractability we further assume 119866(119897) as a concave function(ii) 120576 is a random variable which captures the noise (ieuncertainty) associated with 119877(119897) Following the literature[29] we model it to follow a symmetric distribution suchas the normal distribution with a zero mean and constantvariance 1205902

Third providing a higher level of ldquofulfillment and respon-sivenessrdquo120582 requires resourceWe hence define the respectivecost as 119862(120582) which is an increasing function of 120582 Similarto 119866(119897) we assume 119862(120582) as a convex function for analyticaltractability Notice that the above functional definitions areall intuitive and rather general

With the above model we can derive the profit 120587(120582) theexpected profit 119864[120587(120582)] and the variance of profit119881[120587(120582)] asfunctions of 120582 as follows

120587 (120582) = 119877 (119897) minus 119862 (120582)

= 119866 (119886 + 119887120582) minus 119862 (120582) + 120576

119864 [120587 (120582)] = 119866 (119886 + 119887120582) minus 119862 (120582)

119881 [120587 (120582)] = 1205902

(3)

Because e-business is known to be a risky operation weargue that the OSP has concern on risk and possesses a riskaverse attitude [24] As such we employ the following mean-variance safety first measure (SFM(120582)) as the objective func-tion (for the modeling of risk averse optimization objectivessee Xiao and Choi [30] Chiu and Choi [31] Chiu et al [32]Choi and Chiu [33] Choi et al [34] Choi [35] and Shen etal [36]) for the OSP as follows

SFM (120582) =(119864 [120587 (120582)] minus 120573)

radic119881 [120587 (120582)] (4)

where 120573 is the expected profit target that the OSP wants toachieve

For the meaning behind SFM(120582) please notice thatby Bienayme-Tchebycheff inequality we have the followinginequality

119875 (|120587 (120582) minus 119864 [120587 (120582)]| ge 119864 [120587 (120582)] minus 120573) le119881 [120587 (120582)]

(119864 [120587 (120582)] minus 120573)2

(5)

997904rArr 119875 (120587 (120582) le 120573) le119881 [120587 (120582)]

(119864 [120587 (120582)] minus 120573)2equiv

1

SFM(120582)2 (6)

Thus the RHS of (6) gives the analytical upper boundfor 119875(120587(120582) le 120573) Obviously maximizing SFM(120582) yields aminimized upper bound for 119875(120587(120582) le 120573) and hence SFM(120582)

is called the safety first measure (see [37])With the above model we define (7) and then have

Proposition 1 Notice that 119878119866(120582) and 119878

119862(120582) are the first order

derivatives of 119866(119886 + 119887120582) and 119862(120582) respectively and they alsorepresent the corresponding slopes as follows

120582lowast

SFM = argmax120582

SFM (120582)

120582lowast

EP = argmax120582

119864 [120587 (120582)]

119878119866(120582) =

119889119866 (119886 + 119887120582)

119889120582

119878119862(120582) =

119889119862 (120582)

119889120582

(7)

Proposition 1 120582lowast119878119865119872

uniquely exists and it is equal toarg120582119878119866(120582)119878119862(120582) = 1

Proof of Proposition 1 All proofs are placed in the Appendix

Proposition 1 first presents the uniqueness feature of theoptimal level of fulfillment and responsiveness (ie optimalservice level) which maximizes the safety first objectiveIt then reveals that this optimal service level is the onewhich equalizes 119878

119866(120582) and 119878

119862(120582) In other words when the

rates of change of 119866(119886 + 119887120582) and 119862(120582) are the same thecorresponding service level 120582 is optimal From Proposition 1we have Proposition 2

Mathematical Problems in Engineering 7

Proposition 2 (a) A larger 119887 implies a larger 120582lowastSFM (b) 120582lowast

SFMis independent of 120573 and 120590

Observe that a larger 119887 means that the customer loy-alty is more positively correlated to the service level 120582Proposition 2(a) hence indicates that it will lead to a biggeroptimal service level because the optimal solution refersto the one which balances the increase of revenue 119878

119866(120582)

and rise of cost 119878119862(120582) It is also interesting to note from

Proposition 2(b) that 120582lowastSFM is independent of 120573 and 120590 Thisfinding can be explained by the fact that the variance of profit119881[120587(120582)] is a constant which is independent of the service level120582Thus the corresponding optimal service level is not relatedto 120590 as well as the expected profit target 120573 and will in factbehave like the risk neutral case as shown in Proposition 3below

Proposition 3 120582lowast119864119875

uniquely exists and 120582lowastSFM = 120582lowast

119864119875

Proposition 3 shows a rather surprising result that the riskaverse and risk neutral OSPs will have the same optimal ser-vice levels for this particular case In other words irrespectiveof the risk preference the optimal service level is the sameThis finding is important because it shows the fundamentaldifference between different kinds of operations manage-ment problems For example for inventory planning underuncertainty the optimal inventory decision for the risk averseand risk neutral cases will be different However in serviceoptimization the situation can be different as shown here

6 Concluding Remarks

We conclude by discussing some research implicationsderived from the discussions above First the empiricalfindings suggest that in order to enhance customer loyaltyonline stores should develop marketing strategies to enhanceldquofulfillment and responsivenessrdquo since they strongly affectcustomer loyalty httptaobaocom has devoted valuablecorporate resources to it andwe argue that httptaobaocomcan further improve the functions of Aliwangwang toachieve even better communication between the sellersand httptaobaocom users Some probable enhancementsincluding inserting flash games and converting to smart-phone apps can all help to attract the httptaobaocom usersto use Aliwangwang Furthermore httptaobaocom canmake use of Aliwangwang to obtain personalized data relatedto the customersrsquo preferences As a result a higher degreeof web site customization (eg based on past purchases andother customersrsquo information collected) can be provided toindividual customers

Second for the empirical result driven analytical studieswe find that the optimal service level on ldquofulfillment andresponsivenessrdquo function for the risk averse OSP uniquelyexists and can be expressed as the service level which balancesthe increase of revenue 119878

119866(120582) and the rise of cost 119878

119862(120582) We

further show that if the customerrsquos loyalty is more positivelycorrelated to the service level 120582 it will lead to a larger optimalservice level Moreover the optimal service level under thesafety first objective is independent of profit target 120573 and

uncertainty threshold120590 Finally we analytically prove that theoptimal service level does not depend on the risk preferenceof the OSP

For future research there are two directions Empiricallyit is useful to examine more functions of OSP such ashttptaobaocom This paper only focuses on the severalcore functions specifically adopted by httptaobaocom andthere are some more which deserve further explorationsBesides unlike the traditional bricks-and-mortar and elec-tronic retailers the operation of httptaobaocom involvesthousands of independent sellers As a result the customersrsquoperceived service quality of the platform would be consid-erably affected by their buying experience with individualsellers In the future it is worth investigating themodificationof the existing service quality-scale measurement to cater forthis specific type of newbusinessmodelThe second directionfor further studies relates to the theoretical analysis basedon more comprehensive economics analytical models Webelieve that the findings revealed by this paper can lay thefoundation for many further empirical and analytical studies

Appendix

All Proofs of Propositions

Proof of Proposition 1 From (4) we have

SFM (120582) =(119864 [120587 (120582)] minus 120573)

radic119881 [120587 (120582)] (A1)

Since 119881[120587(120582)] = 1205902 (A2) becomes

SFM (120582) =(119864 [120587 (120582)] minus 120573)

120590 (A2)

119889SFM (120582)

119889120582=119889119864 [120587 (120582)] 119889120582

120590

=119889 [119866 (119886 + 119887120582) minus 119862 (120582)] 119889120582

120590

(A3)

1198892SFM (120582)

1198891205822=1

120590(1198892

119866 (119886 + 119887120582)

1198891205822minus1198892

119862 (120582)

1198891205822) (A4)

Since 119866(119886 + 119887120582) is concave and 119862(120582) is convex1198892SFM(120582)119889120582

2

lt 0 which means SFM(120582) is concave and120582lowast

SFM uniquely exists In addition notice that 120582lowast

SFM =

arg120582[119889SFM(120582)119889120582 = 0] Thus we have 120582lowastSFM = arg

120582119878119866(120582)

119878119862(120582) = 1

Proof of Proposition 2 Part (a) notice that 120582lowast

SFM =

arg120582119878119866(120582)119878119862(120582) = 1 and

119878119866(120582) =

119889 [119866 (119886 + 119887120582)]

119889120582

=119889 (119886 + 119887120582)

119889120582

119889119866 (119886 + 119887120582)

119889 (119886 + 119887120582)= 119887

119889119866 (119886 + 119887120582)

119889 (119886 + 119887120582)

(A5)

Thus 120582lowastSFM = arg120582119878119866(120582)119878119862(120582) = 1 = arg119878

119862(120582) = 119887(119889119866(119886+

119887120582)119889(119886 + 119887120582))

8 Mathematical Problems in Engineering

Define 119891(120582) = 119878119862(120582)(119889119866(119897)119889119897)|

119897=119886+119887120582 Consider

1198911015840

(120582) =1

( (119889119866 (119897) 119889119897)|119897=119886+119887120582

)2

times ((119889119866 (119897)

119889119897

10038161003816100381610038161003816100381610038161003816119897=119886+119887120582)119889119878119862(120582)

119889120582

minus119878119862(120582)

119889

119889120582(119889119866 (119897)

119889119897

10038161003816100381610038161003816100381610038161003816119897=119886+119887120582))

(A6)

We have

(a) 119889119878119862(120582)119889120582 = 119889

2

119862(120582)1198891205822

gt 0 [∵ 119862(120582) is convex in120582]

(b) (119889119866(119897)119889119897)|119897=119886+119887120582

gt 0 [∵ 119866(119897) is increasing in 119897](c) 119878119862(120582) = 119889119862(120582)119889120582 gt 0 [∵ 119862(120582) is increasing in 120582]

(d) (119889119889120582)((119889119866(119897)119889119897)|119897=119886+119887120582

) = [1198892

119866(119897)1198891198972

sdot 119889(119897 = 119886 +

119887120582)119889120582]119897=119886+119887120582

= 119887(1198892

119866(119897)1198891198972

)119897=119886+119887120582

lt 0 [∵ 119866(119897) isconcave in 119897]

Therefore we have 1198911015840(120582) gt 0 for all 120582 In other words 119891(120582) isincreasing in 120582 Therefore a larger 119887 implies a larger120582lowastSFM

Part (b) since both 119878119866(120582) and 119878

119862(120582) are independent of 120573

and 120590 it is obvious that 120582lowastSFM = arg120582119878119866(120582)119878119862(120582) = 1 is also

independent of 120573 and 120590

Proof of Proposition 3 First notice that 119864[120587(120582)] is a concavefunction Thus 120582lowastEP uniquely exists Second from (A3) wehave

119889SFM (120582)

119889120582=119889119864 [120587 (120582)] 119889120582

120590=1

120590sdot119889119864 [120587 (120582)]

119889120582 (A7)

Obviously from (A7) we have 120582lowastSFM = arg120582[119889SFM(120582)119889120582 =

0] = arg120582[119889119864[120587(120582)]119889120582 = 0] which is the same as 120582lowastEP Thus

120582lowast

SFM = 120582lowast

EP

Authorsrsquo Contribution

All authors have good contribution to the paper and thelisting follows their surnames

Acknowledgments

The authors thank the editor and the two reviewers for theirkind comments on this paperThey declare that none of themis related to the target case company ldquohttptaobaocomrdquo andother companies named in the paper which may constitute aconflict of interest This paper is partially supported by theRGC(HK)-GRF under grant ac of PolyU 542010H

References

[1] Z Yang ldquoCustomer perceptions of service quality in internet-based electronic commercerdquo inProceedings of the 30th EuropeanMarketing Academy Conference (EMAC rsquo01) pp 8ndash11 BergenNorway 2001

[2] V A Zeithaml ldquoService excellent in electronic channelsrdquoMan-aging Service Quality vol 12 no 3 pp 135ndash139 2002

[3] Parasuraman A L Berry and V A Zeithaml ldquoSERVQUALa multiple-item scale for measuring consumer perceptions ofservice qualityrdquo Journal of Retailing vol 64 no 1 pp 12ndash401988

[4] A Parasuraman and D Grewal ldquoThe impact of technology onthe quality-value-loyalty chain a research agendardquo Journal ofthe Academy of Marketing Science vol 28 no 1 pp 168ndash1742000

[5] A C R Van Riel V Liljander and P Jurriens ldquoExploringconsumer evaluations of e-services a portal siterdquo InternationalJournal of Service Industry Management vol 12 no 4 pp 359ndash377 2001

[6] S Devaraj M Fan and R Kohli ldquoAntecedents of B2C channelsatisfaction and preference validating e-commerce metricsrdquoInformation Systems Research vol 13 no 3 pp 316ndash333 2002

[7] J Kim and J Lee ldquoCritical design factors for successful e-commerce systemsrdquoBehaviour and Information Technology vol21 no 3 pp 185ndash199 2002

[8] S Negash T Ryan and M Igbaria ldquoQuality and effectivenessin Web-based customer support systemsrdquo Information andManagement vol 40 no 8 pp 757ndash768 2003

[9] Y-F Kuo ldquoA study on service quality of virtual communitywebsitesrdquo Total Quality Management and Business Excellencevol 14 no 4 pp 461ndash473 2003

[10] F X Zhu W Wymer and I Chen ldquoIT-based services and ser-vice quality in consumer bankingrdquo International Journal of Ser-vice Industry Management vol 13 no 1 pp 69ndash90 2002

[11] J Santos ldquoE-service quality a model of virtual service qualitydimensionsrdquo Managing Service Quality vol 13 no 3 pp 233ndash246 2003

[12] F Buttle ldquoSERVQUAL review critique research agendardquo Euro-pean Journal of Marketing vol 30 no 1 pp 8ndash32 1996

[13] H Stewart C Hope and A Muhlemann ldquoProfessional servicequality a step beyond other servicesrdquo Journal of Retailing andConsumer Services vol 5 no 4 pp 209ndash222 1998

[14] J J Cronin and S A Taylor ldquoMeasuring service quality a re-examination and extensionrdquo Journal of Marketing vol 56 no 3pp 55ndash68 1992

[15] A Parasuraman L L Berry and V A Zeithaml ldquoMore onimproving service quality measurementrdquo Journal of Retailingvol 69 no 1 pp 140ndash147 1993

[16] A Parasuraman V A Zeithaml and A Malhotra ldquoE-S-QUALa multiple-item scale for assessing electronic service qualityrdquoJournal of Service Research vol 7 no 3 pp 213ndash233 2005

[17] S Akinci E Atilgan-Inan and S Aksoy ldquoRe-assessment of E-S-Qual and E-RecS-Qual in a pure service settingrdquo Journal ofBusiness Research vol 63 no 3 pp 232ndash240 2010

[18] J R Rossitier ldquoER-SERVCOMPSQUAL a measure of e-retail-ing service components qualityrdquo Service Science vol 1 no 4 pp212ndash2224 2009

[19] D X Ding P J-H Hu and O R L Sheng ldquoE-SELFQUALa scale for measuring online self-service qualityrdquo Journal ofBusiness Research vol 64 no 5 pp 508ndash515 2011

[20] S Ha and L Stoel ldquoOnline apparel retailing roles of e-shoppingquality and experiential e-shopping motivesrdquo Journal of ServiceManagement vol 23 no 2 pp 197ndash215 2012

[21] Y Hu and Q Qiang ldquoAn equilibriummodel of online shoppingsupply chain networks with service capacity investmentrdquo Ser-vice Science 2013

[22] R T Rust and A J Zahorik ldquoCustomer satisfaction customerretention and market sharerdquo Journal of Retailing vol 69 no 2pp 193ndash215 1993

Mathematical Problems in Engineering 9

[23] L Groslashnholdt A Martensen and K Kristensen ldquoThe relation-ship between customer satisfaction and loyalty cross-industrydifferencesrdquo Total Quality Management vol 11 no 4ndash6 ppS509ndashS514 2000

[24] T Xiao T M Choi D Yang and T C E Cheng ldquoService com-mitment strategy and pricing decisions in retail supply chainswith risk-averse playersrdquo Service Science vol 4 no 3 pp 236ndash252 2012

[25] T M Choi ldquoOptimal return service charging policy for afashionmass customization programrdquo Service Science vol 5 no1 pp 56ndash68 2013

[26] B Kwok The service quality of the features of Taobaocom andtheir impacts on customer loyalty [BA FYP thesis] The HongKong Polytechnic University 2011

[27] J P Robinson P R Shaver and L S Wrightsman Measures ofPersonality and Social Psychological Attitudes Academic PressSan Diego Calif USA 1991

[28] B Edvardsson M D Johnson A Gustafsson and T StrandvikldquoThe effects of satisfaction and loyalty on profits and growthproducts versus servicesrdquoTotal QualityManagement vol 11 no7 pp S917ndashS927 2000

[29] C-HChiu T-MChoi andD Li ldquoPricewall orwar the pricingstrategies for retailersrdquo IEEE Transactions on Systems Man andCybernetics A vol 39 no 2 pp 331ndash343 2009

[30] T Xiao and T-M Choi ldquoPurchasing choices and channelstructure strategies for a two-echelon system with risk-averseplayersrdquo International Journal of Production Economics vol 120no 1 pp 54ndash65 2009

[31] C-H Chiu and T-M Choi ldquoOptimal pricing and stockingdecisions for newsvendor problem with value-at-risk consider-ationrdquo IEEE Transactions on Systems Man and Cybernetics Avol 40 no 5 pp 1116ndash1119 2010

[32] C-H Chiu T-M Choi and X Li ldquoSupply chain coordinationwith risk sensitive retailer under target sales rebaterdquo Automat-ica vol 47 no 8 pp 1617ndash1625 2011

[33] T-M Choi and C-H Chiu ldquoMean-downside-risk and mean-variance newsvendor models implications for sustainable fash-ion retailingrdquo International Journal of Production Economicsvol 135 no 2 pp 552ndash560 2012

[34] T M Choi P S Chow and T Xiao ldquoElectronic price-testingscheme for fashion retailing with information updatingrdquo Inter-national Journal of Production Economics vol 140 pp 396ndash4062012

[35] TMChoi ldquoMulti-period riskminimization purchasingmodelsfor fashion products with interest rate budget and profit targetconsiderationsrdquo Annals of Operations Research 2013

[36] B Shen TM Choi YWang andC K Y Lo ldquoThe coordinationof fashion supply chains with a risk averse supplier by themarkdown money policyrdquo IEEE Transactions on Systems Manand CyberneticsmdashSystems vol 43 pp 266ndash276 2013

[37] A D Roy ldquoSafety first and the holding of assetsrdquo Econometricavol 20 no 3 pp 431ndash449 1952

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Mathematical Problems in Engineering

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Differential EquationsInternational Journal of

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Stochastic AnalysisInternational Journal of

4 Mathematical Problems in Engineering

properly and requires a minimum amount of informationto be inputted by the customer As a result the onlineshipping platform is efficient if (i) it is easy for customersto find what they need (ii) it enables customers to completea transaction quickly and (iii) the information providedby the web site is well organized Thus service quality ofthe searching functions of httptaobaocom can reflect itsefficiency Therefore we have the following hypothesis

H1 efficiency positively influences the customer loyaltyof httptaobaocom

Reliability here refers to the consistency of performanceand dependability of companies [3] Reliability can makecustomers recognize the consistency and credibility of thecompany In e-commerce it is vital to make customers trustthe promises that the OSP made Obviously ldquoThe Guaranteeof Trade Safetyrdquo function of httptaobaocom is relatedto ensuring reliability and it is hypothesized to influencecustomer loyalty

H2 reliability positively influences the customer loyaltyof httptaobaocom

Fulfillment refers to the extent in which the websitersquospromises on order delivery and items availability are fulfilledTo be specific fulfillment is reflected by whether the OSP canensure items being available to delivery within an acceptabletime frame and guarantee quick delivery as promised [16] Forhttptaobaocom the functions of Aliwangwang are ableto represent fulfillment Responsiveness means the effectivehandling of problems and returns through the site It providescustomers with convenient options for returning items Thewebsite promises to handle product returns and offers ameaningful guarantee so that customers would feel morecomfortable [16] As one of the service quality dimen-sions contacting the seller through Aliwangwang reflectsthe responsiveness of httptaobaocom Therefore it ishypothesized that

H3 ldquofulfillment and responsivenessrdquo positively influ-ence the customer loyalty of httptaobaocom

Flexibility refers to the choice of payment methods Forexample when purchasing products in httptaobaocomTaodot is an innovative way for the users to pay onlinesafely Consumers can buy Taodot to depositadd values intheir Alipay account It is safer and shows its flexibility ifconsumers are in Hong Kong or Macau Then the followinghypothesis is proposed

H4 flexibility positively influences the customer loyaltyof httptaobaocom

Privacy refers to the degree to which the website is safeand customer information is well-protected This dimensionholds an important position in e-commerce and is critical ine-service Customers perceive significant risks in the virtualenvironment of e-commerce stemming from the possibilityof improper use of their personal financial data The privacyfunction in httptaobaocom is mainly associated withAlipay Accordingly it is hypothesized that

H5 privacy positively influences the customer loyalty ofhttptaobaocom

23 Review of Related Analytical Models In the literature onanalytical research there are many recent studies related tothe optimal service decision For example Xiao et al [24]explore the optimal service strategy with optimal pricingdecisions in a retail supply chain with risk averse playersThey consider the situation inwhich the retailer has to choosewhether to provide a service guarantee (SG) or to provideno service guarantee (NSG) They relate the optimal choiceto the supplierrsquos degree of risk aversion and the consumerrsquossensitivity to service reliabilityThey find that endogenizationof wholesale pricing decision will raise the retailerrsquos moti-vation to adopt SG strategy if the consumer is sufficientlyrisk averse Choi [25] examines the optimal service chargefor consumer product returns under mass customizationoperations He explores two cases which cover the scenariowhen the mass customization company is risk-neutral andrisk-averse He derives the closed-form expression of theoptimal return service charge for each case He analyticallyshows how the mass customization companyrsquos degree of riskaversion affects the optimal return service charge policy Inaddition he derives the analytical conditions under which itis optimal for the mass customization company to offer a freereturn with a full refund policy Hu andQiang [21] investigatean online supply chain analytical model which is consistedof manufacturers express service providers and electronicretailers They consider the situation in which consumerswho purchase products from the electronic retailers canchoose to adopt the express delivery service or simply pick upthe products themselves At the same time the express servicecompanies can invest on service quality improvement Theydevelop the variational inequality formulation and generateinsights via exploring the equilibrium As reviewed above itis evidenced that analytical modeling research is commonlyadopted to explore optimal service decisions and strategiesin the literature However to the best of our knowledgenone of them explore the problem associated with theempirical service quality framework Thus we believe thatthis paper is the first onewhich investigates the service qualityproblem forOSPs by combining both empirical and analyticalapproaches

3 Data Collection and Empirical Data Analysis

A consumer questionnaire survey is conducted 250 ques-tionnaires are distributed via social network websites undera convenience sampling approach Receivers are invited tocomplete the questionnaire if they know httptaobaocomand have visited it 190 valid questionnaires are received andemployed for statistical analysis finally

The questionnaire is divided into 8 parts The first part isused to collect the demographic data of the respondents Thesecond part is employed to examine the purchase behaviorof the respondents on httptaobaocom The rest of thequestionnaire is divided into 6 subparts to investigate theperceptions of httptaobaocom users on the 5 functionsprovided by httptaobaocom and their loyalty towards the

Mathematical Problems in Engineering 5

OSP The 7-point Likert scale is used in which 1 representsldquostrongly disagreerdquo and 7 represents ldquostrongly agreerdquo

31 Descriptive Statistics Among all the completed ques-tionnaires received 64 of the respondents have purchasedfashion products on httptaobaocom before while 36have not Thus the remaining analysis will be based on theresponses of these 64 of the collected sample in order toget valid data To be specific these consumers are consideredas httptaobaocomrsquos customers (for fashion products)The percentage of the httptaobaocom customers whobrowse products on httptaobaocom daily occupies 813 11 and 26 of httptaobaocomrsquos customers browseproducts on httptaobaocom 2-3 times a week once aweek and 2-3 times per month respectivelyThe percentagesof the httptaobaocomrsquos customers who buy products viahttptaobaocom daily 2-3 times a week once a week and2-3 times a month are 3 2 2 and 11 respectively

Regarding spending 75 of the httptaobaocomusers spend less than $500 on httptaobaocom monthly19 of them spend $500-1000 on Tobaocom monthly2 1 and 3 of the users spend $1000ndash1500 $1500ndash2000 and more than $2000 on httptaobaocom monthlyrespectively Regarding the users shopping experiences onhttptaobaocom ranking their enjoyment (in a 1ndash7 scalewith 7 being the best) more than half of the respondentsscore their enjoyment level strictly above 4 (64) 36 ofthem score as score 5 which represents the greatest group23 of them score as score 6 and 5 of them rank it 7The percentage of score 4 which means neutral excitementoccupies 15The percentages of low scores 1 2 and 3 occupy5 5 and 11 respectively

32 Hypotheses Testing Before testing hypotheses we firstconduct the reliability test to assess the internal consistencyof the variables Reliability test is measured by the Cronbachrsquosalpha in which the alpha value of 07 or above is widelyrecognized as an acceptable level of reliability [27] TheCronbach alpha values for Searching functions Guaranteeof Trade Safety Aliwangwang Alipay and Taodot are foundto be 0933 0919 0956 0958 and 0921 respectively All ofthem are higher than the acceptable level Besides the sevenquestions of customer loyalty are tested in this part TheCronbach alpha value of the customer loyalty is 0905 whichalso exceeds the acceptable level Thus we conclude that allthese dimensions are sufficiently reliable Next we proceedto test the relationships of the five featured functions andcustomer loyalty by performing multiple linear regressionanalysis It is shown that there is a statistically significantrelationship between customer loyalty and the Aliwangwangfunction The related statistical results of hypotheses aresummarized in Table 2 As such hypothesis H3 is supported(more details on the statistical analysis can be found in thesisresearch of Kwok [26])

4 Empirical Findings and Insights

From the above empirical analysis first of all we know thatthe factor of ldquofulfillment and responsiveness (Aliwangwang

function)rdquo is the most critical one as it most strongly affectscustomer loyalty of httptaobaocomrsquos customers (whopurchased fashion products before) Observe that this resultis consistent with the findings in the traditional servicecontext which suggests that customers expect quick feedbackon their requests and suggestions for improvements [2]For httptaobaocomrsquos case it can be explained by thefunction of Aliwangwang which provides instant messagingfor httptaobaocomrsquos users to send messages to the sellersThis enhances the fulfillment of httptaobaocom userson queries and needs related to product delivery and itemavailability This function also can reduce the customersrsquodoubt on the product that they want to buy and furtherconfirm their purchase decision In addition the customerscan contact the sellers whenever the sellers are online witha simple click This function extends the accessibility of thesellersonline stores without the control of the store operatinghours With Aliwangwang httptaobaocom users are ableto obtain adequate product information even if it is not givenon the product web page It increases the convenience ofhttptaobaocom Since it is a unique feature provided byhttptaobaocom but not its close competitors it becomes amajor niche for httptaobaocom

Second observe that only one hypothesis among theproposed five is proven to be true statistically A possiblereason may be given as follows Unlike the traditionalphysical store or other kinds of online electronic retailerssuch as httpwwwamazoncom etc httptaobaocomis an online marketplace in which there are thousands ofindependent retailers As a result customersrsquo perception onthe service quality and their loyalty towards the OSP maybe affected by their buying experience with the individ-ual retailers Owing to the fundamental differences manyhypotheses which apply for the more traditional bricks-and-mortar retailingelectronic retailing do not hold for theOSPs such as httptaobaocom In addition notice that inhttptaobaocom communication with individual sellers isespecially important In fact customers would like to gathermore information so that they can make better choice from alarge variety of independent retailers selling similar productson the platformThis accounts for the support ofHypothesis 3(which is about Aliwangwang and related to communicationaspect)

Third we know that a higher level of ldquofulfillment andresponsiveness (Aliwangwang function)rdquo will lead to a higherlevel of customer loyalty According to Edvardsson et al[28] customer loyalty will positively affect the revenue ofthe OSP it will be thus interesting to analytically examinewhether there exists an optimal level of ldquofulfillment andresponsivenessrdquo for the OSP We hence further conduct ananalytical study in the next section

5 Analytical Service OptimizationModel and Findings

Based on the empirical findings from Section 4 we proceedto build an analytical model for further analysis First fromthe linear regression analysis conducted in Section 4 (seeTable 2) we notice that the level of customer loyalty 119897 is related

6 Mathematical Problems in Engineering

Table 2 Summary of regression analysis

Corresponding hypothesis Independent variable Standardized coefficient t-value SigHypothesis 1 Efficiency minus0057 minus0575 0573Hypothesis 2 Reliability 0087 0896 0383Hypothesis 3 Fulfillment amp responsiveness 0582 2255 0038lowastlowast

Hypothesis 4 Flexibility 0062 0406 0690Hypothesis 5 Privacy 0307 1391 0182Remarksdependent variable customer loyaltylowastlowasthypothesis is supported

to the level of ldquofulfillment and responsivenessrdquo 120582 in a linearfunction as follows

119897 = 119886 + 119887120582 (1)

where 119886 119887 gt 0Second since it is intuitive that a higher level of customer

loyalty 119897will ldquostochasticallyrdquo (itmeans expectedly the revenueshould be higher but the exactmagnitude is unknown) lead toa higher revenue 119877(119897) for the OSP such as httptaobaocomwe have the following

119877 (119897) = 119866 (119897) + 120576 (2)

where (i) 119866(119897) is an increasing function of 119897 For analyticaltractability we further assume 119866(119897) as a concave function(ii) 120576 is a random variable which captures the noise (ieuncertainty) associated with 119877(119897) Following the literature[29] we model it to follow a symmetric distribution suchas the normal distribution with a zero mean and constantvariance 1205902

Third providing a higher level of ldquofulfillment and respon-sivenessrdquo120582 requires resourceWe hence define the respectivecost as 119862(120582) which is an increasing function of 120582 Similarto 119866(119897) we assume 119862(120582) as a convex function for analyticaltractability Notice that the above functional definitions areall intuitive and rather general

With the above model we can derive the profit 120587(120582) theexpected profit 119864[120587(120582)] and the variance of profit119881[120587(120582)] asfunctions of 120582 as follows

120587 (120582) = 119877 (119897) minus 119862 (120582)

= 119866 (119886 + 119887120582) minus 119862 (120582) + 120576

119864 [120587 (120582)] = 119866 (119886 + 119887120582) minus 119862 (120582)

119881 [120587 (120582)] = 1205902

(3)

Because e-business is known to be a risky operation weargue that the OSP has concern on risk and possesses a riskaverse attitude [24] As such we employ the following mean-variance safety first measure (SFM(120582)) as the objective func-tion (for the modeling of risk averse optimization objectivessee Xiao and Choi [30] Chiu and Choi [31] Chiu et al [32]Choi and Chiu [33] Choi et al [34] Choi [35] and Shen etal [36]) for the OSP as follows

SFM (120582) =(119864 [120587 (120582)] minus 120573)

radic119881 [120587 (120582)] (4)

where 120573 is the expected profit target that the OSP wants toachieve

For the meaning behind SFM(120582) please notice thatby Bienayme-Tchebycheff inequality we have the followinginequality

119875 (|120587 (120582) minus 119864 [120587 (120582)]| ge 119864 [120587 (120582)] minus 120573) le119881 [120587 (120582)]

(119864 [120587 (120582)] minus 120573)2

(5)

997904rArr 119875 (120587 (120582) le 120573) le119881 [120587 (120582)]

(119864 [120587 (120582)] minus 120573)2equiv

1

SFM(120582)2 (6)

Thus the RHS of (6) gives the analytical upper boundfor 119875(120587(120582) le 120573) Obviously maximizing SFM(120582) yields aminimized upper bound for 119875(120587(120582) le 120573) and hence SFM(120582)

is called the safety first measure (see [37])With the above model we define (7) and then have

Proposition 1 Notice that 119878119866(120582) and 119878

119862(120582) are the first order

derivatives of 119866(119886 + 119887120582) and 119862(120582) respectively and they alsorepresent the corresponding slopes as follows

120582lowast

SFM = argmax120582

SFM (120582)

120582lowast

EP = argmax120582

119864 [120587 (120582)]

119878119866(120582) =

119889119866 (119886 + 119887120582)

119889120582

119878119862(120582) =

119889119862 (120582)

119889120582

(7)

Proposition 1 120582lowast119878119865119872

uniquely exists and it is equal toarg120582119878119866(120582)119878119862(120582) = 1

Proof of Proposition 1 All proofs are placed in the Appendix

Proposition 1 first presents the uniqueness feature of theoptimal level of fulfillment and responsiveness (ie optimalservice level) which maximizes the safety first objectiveIt then reveals that this optimal service level is the onewhich equalizes 119878

119866(120582) and 119878

119862(120582) In other words when the

rates of change of 119866(119886 + 119887120582) and 119862(120582) are the same thecorresponding service level 120582 is optimal From Proposition 1we have Proposition 2

Mathematical Problems in Engineering 7

Proposition 2 (a) A larger 119887 implies a larger 120582lowastSFM (b) 120582lowast

SFMis independent of 120573 and 120590

Observe that a larger 119887 means that the customer loy-alty is more positively correlated to the service level 120582Proposition 2(a) hence indicates that it will lead to a biggeroptimal service level because the optimal solution refersto the one which balances the increase of revenue 119878

119866(120582)

and rise of cost 119878119862(120582) It is also interesting to note from

Proposition 2(b) that 120582lowastSFM is independent of 120573 and 120590 Thisfinding can be explained by the fact that the variance of profit119881[120587(120582)] is a constant which is independent of the service level120582Thus the corresponding optimal service level is not relatedto 120590 as well as the expected profit target 120573 and will in factbehave like the risk neutral case as shown in Proposition 3below

Proposition 3 120582lowast119864119875

uniquely exists and 120582lowastSFM = 120582lowast

119864119875

Proposition 3 shows a rather surprising result that the riskaverse and risk neutral OSPs will have the same optimal ser-vice levels for this particular case In other words irrespectiveof the risk preference the optimal service level is the sameThis finding is important because it shows the fundamentaldifference between different kinds of operations manage-ment problems For example for inventory planning underuncertainty the optimal inventory decision for the risk averseand risk neutral cases will be different However in serviceoptimization the situation can be different as shown here

6 Concluding Remarks

We conclude by discussing some research implicationsderived from the discussions above First the empiricalfindings suggest that in order to enhance customer loyaltyonline stores should develop marketing strategies to enhanceldquofulfillment and responsivenessrdquo since they strongly affectcustomer loyalty httptaobaocom has devoted valuablecorporate resources to it andwe argue that httptaobaocomcan further improve the functions of Aliwangwang toachieve even better communication between the sellersand httptaobaocom users Some probable enhancementsincluding inserting flash games and converting to smart-phone apps can all help to attract the httptaobaocom usersto use Aliwangwang Furthermore httptaobaocom canmake use of Aliwangwang to obtain personalized data relatedto the customersrsquo preferences As a result a higher degreeof web site customization (eg based on past purchases andother customersrsquo information collected) can be provided toindividual customers

Second for the empirical result driven analytical studieswe find that the optimal service level on ldquofulfillment andresponsivenessrdquo function for the risk averse OSP uniquelyexists and can be expressed as the service level which balancesthe increase of revenue 119878

119866(120582) and the rise of cost 119878

119862(120582) We

further show that if the customerrsquos loyalty is more positivelycorrelated to the service level 120582 it will lead to a larger optimalservice level Moreover the optimal service level under thesafety first objective is independent of profit target 120573 and

uncertainty threshold120590 Finally we analytically prove that theoptimal service level does not depend on the risk preferenceof the OSP

For future research there are two directions Empiricallyit is useful to examine more functions of OSP such ashttptaobaocom This paper only focuses on the severalcore functions specifically adopted by httptaobaocom andthere are some more which deserve further explorationsBesides unlike the traditional bricks-and-mortar and elec-tronic retailers the operation of httptaobaocom involvesthousands of independent sellers As a result the customersrsquoperceived service quality of the platform would be consid-erably affected by their buying experience with individualsellers In the future it is worth investigating themodificationof the existing service quality-scale measurement to cater forthis specific type of newbusinessmodelThe second directionfor further studies relates to the theoretical analysis basedon more comprehensive economics analytical models Webelieve that the findings revealed by this paper can lay thefoundation for many further empirical and analytical studies

Appendix

All Proofs of Propositions

Proof of Proposition 1 From (4) we have

SFM (120582) =(119864 [120587 (120582)] minus 120573)

radic119881 [120587 (120582)] (A1)

Since 119881[120587(120582)] = 1205902 (A2) becomes

SFM (120582) =(119864 [120587 (120582)] minus 120573)

120590 (A2)

119889SFM (120582)

119889120582=119889119864 [120587 (120582)] 119889120582

120590

=119889 [119866 (119886 + 119887120582) minus 119862 (120582)] 119889120582

120590

(A3)

1198892SFM (120582)

1198891205822=1

120590(1198892

119866 (119886 + 119887120582)

1198891205822minus1198892

119862 (120582)

1198891205822) (A4)

Since 119866(119886 + 119887120582) is concave and 119862(120582) is convex1198892SFM(120582)119889120582

2

lt 0 which means SFM(120582) is concave and120582lowast

SFM uniquely exists In addition notice that 120582lowast

SFM =

arg120582[119889SFM(120582)119889120582 = 0] Thus we have 120582lowastSFM = arg

120582119878119866(120582)

119878119862(120582) = 1

Proof of Proposition 2 Part (a) notice that 120582lowast

SFM =

arg120582119878119866(120582)119878119862(120582) = 1 and

119878119866(120582) =

119889 [119866 (119886 + 119887120582)]

119889120582

=119889 (119886 + 119887120582)

119889120582

119889119866 (119886 + 119887120582)

119889 (119886 + 119887120582)= 119887

119889119866 (119886 + 119887120582)

119889 (119886 + 119887120582)

(A5)

Thus 120582lowastSFM = arg120582119878119866(120582)119878119862(120582) = 1 = arg119878

119862(120582) = 119887(119889119866(119886+

119887120582)119889(119886 + 119887120582))

8 Mathematical Problems in Engineering

Define 119891(120582) = 119878119862(120582)(119889119866(119897)119889119897)|

119897=119886+119887120582 Consider

1198911015840

(120582) =1

( (119889119866 (119897) 119889119897)|119897=119886+119887120582

)2

times ((119889119866 (119897)

119889119897

10038161003816100381610038161003816100381610038161003816119897=119886+119887120582)119889119878119862(120582)

119889120582

minus119878119862(120582)

119889

119889120582(119889119866 (119897)

119889119897

10038161003816100381610038161003816100381610038161003816119897=119886+119887120582))

(A6)

We have

(a) 119889119878119862(120582)119889120582 = 119889

2

119862(120582)1198891205822

gt 0 [∵ 119862(120582) is convex in120582]

(b) (119889119866(119897)119889119897)|119897=119886+119887120582

gt 0 [∵ 119866(119897) is increasing in 119897](c) 119878119862(120582) = 119889119862(120582)119889120582 gt 0 [∵ 119862(120582) is increasing in 120582]

(d) (119889119889120582)((119889119866(119897)119889119897)|119897=119886+119887120582

) = [1198892

119866(119897)1198891198972

sdot 119889(119897 = 119886 +

119887120582)119889120582]119897=119886+119887120582

= 119887(1198892

119866(119897)1198891198972

)119897=119886+119887120582

lt 0 [∵ 119866(119897) isconcave in 119897]

Therefore we have 1198911015840(120582) gt 0 for all 120582 In other words 119891(120582) isincreasing in 120582 Therefore a larger 119887 implies a larger120582lowastSFM

Part (b) since both 119878119866(120582) and 119878

119862(120582) are independent of 120573

and 120590 it is obvious that 120582lowastSFM = arg120582119878119866(120582)119878119862(120582) = 1 is also

independent of 120573 and 120590

Proof of Proposition 3 First notice that 119864[120587(120582)] is a concavefunction Thus 120582lowastEP uniquely exists Second from (A3) wehave

119889SFM (120582)

119889120582=119889119864 [120587 (120582)] 119889120582

120590=1

120590sdot119889119864 [120587 (120582)]

119889120582 (A7)

Obviously from (A7) we have 120582lowastSFM = arg120582[119889SFM(120582)119889120582 =

0] = arg120582[119889119864[120587(120582)]119889120582 = 0] which is the same as 120582lowastEP Thus

120582lowast

SFM = 120582lowast

EP

Authorsrsquo Contribution

All authors have good contribution to the paper and thelisting follows their surnames

Acknowledgments

The authors thank the editor and the two reviewers for theirkind comments on this paperThey declare that none of themis related to the target case company ldquohttptaobaocomrdquo andother companies named in the paper which may constitute aconflict of interest This paper is partially supported by theRGC(HK)-GRF under grant ac of PolyU 542010H

References

[1] Z Yang ldquoCustomer perceptions of service quality in internet-based electronic commercerdquo inProceedings of the 30th EuropeanMarketing Academy Conference (EMAC rsquo01) pp 8ndash11 BergenNorway 2001

[2] V A Zeithaml ldquoService excellent in electronic channelsrdquoMan-aging Service Quality vol 12 no 3 pp 135ndash139 2002

[3] Parasuraman A L Berry and V A Zeithaml ldquoSERVQUALa multiple-item scale for measuring consumer perceptions ofservice qualityrdquo Journal of Retailing vol 64 no 1 pp 12ndash401988

[4] A Parasuraman and D Grewal ldquoThe impact of technology onthe quality-value-loyalty chain a research agendardquo Journal ofthe Academy of Marketing Science vol 28 no 1 pp 168ndash1742000

[5] A C R Van Riel V Liljander and P Jurriens ldquoExploringconsumer evaluations of e-services a portal siterdquo InternationalJournal of Service Industry Management vol 12 no 4 pp 359ndash377 2001

[6] S Devaraj M Fan and R Kohli ldquoAntecedents of B2C channelsatisfaction and preference validating e-commerce metricsrdquoInformation Systems Research vol 13 no 3 pp 316ndash333 2002

[7] J Kim and J Lee ldquoCritical design factors for successful e-commerce systemsrdquoBehaviour and Information Technology vol21 no 3 pp 185ndash199 2002

[8] S Negash T Ryan and M Igbaria ldquoQuality and effectivenessin Web-based customer support systemsrdquo Information andManagement vol 40 no 8 pp 757ndash768 2003

[9] Y-F Kuo ldquoA study on service quality of virtual communitywebsitesrdquo Total Quality Management and Business Excellencevol 14 no 4 pp 461ndash473 2003

[10] F X Zhu W Wymer and I Chen ldquoIT-based services and ser-vice quality in consumer bankingrdquo International Journal of Ser-vice Industry Management vol 13 no 1 pp 69ndash90 2002

[11] J Santos ldquoE-service quality a model of virtual service qualitydimensionsrdquo Managing Service Quality vol 13 no 3 pp 233ndash246 2003

[12] F Buttle ldquoSERVQUAL review critique research agendardquo Euro-pean Journal of Marketing vol 30 no 1 pp 8ndash32 1996

[13] H Stewart C Hope and A Muhlemann ldquoProfessional servicequality a step beyond other servicesrdquo Journal of Retailing andConsumer Services vol 5 no 4 pp 209ndash222 1998

[14] J J Cronin and S A Taylor ldquoMeasuring service quality a re-examination and extensionrdquo Journal of Marketing vol 56 no 3pp 55ndash68 1992

[15] A Parasuraman L L Berry and V A Zeithaml ldquoMore onimproving service quality measurementrdquo Journal of Retailingvol 69 no 1 pp 140ndash147 1993

[16] A Parasuraman V A Zeithaml and A Malhotra ldquoE-S-QUALa multiple-item scale for assessing electronic service qualityrdquoJournal of Service Research vol 7 no 3 pp 213ndash233 2005

[17] S Akinci E Atilgan-Inan and S Aksoy ldquoRe-assessment of E-S-Qual and E-RecS-Qual in a pure service settingrdquo Journal ofBusiness Research vol 63 no 3 pp 232ndash240 2010

[18] J R Rossitier ldquoER-SERVCOMPSQUAL a measure of e-retail-ing service components qualityrdquo Service Science vol 1 no 4 pp212ndash2224 2009

[19] D X Ding P J-H Hu and O R L Sheng ldquoE-SELFQUALa scale for measuring online self-service qualityrdquo Journal ofBusiness Research vol 64 no 5 pp 508ndash515 2011

[20] S Ha and L Stoel ldquoOnline apparel retailing roles of e-shoppingquality and experiential e-shopping motivesrdquo Journal of ServiceManagement vol 23 no 2 pp 197ndash215 2012

[21] Y Hu and Q Qiang ldquoAn equilibriummodel of online shoppingsupply chain networks with service capacity investmentrdquo Ser-vice Science 2013

[22] R T Rust and A J Zahorik ldquoCustomer satisfaction customerretention and market sharerdquo Journal of Retailing vol 69 no 2pp 193ndash215 1993

Mathematical Problems in Engineering 9

[23] L Groslashnholdt A Martensen and K Kristensen ldquoThe relation-ship between customer satisfaction and loyalty cross-industrydifferencesrdquo Total Quality Management vol 11 no 4ndash6 ppS509ndashS514 2000

[24] T Xiao T M Choi D Yang and T C E Cheng ldquoService com-mitment strategy and pricing decisions in retail supply chainswith risk-averse playersrdquo Service Science vol 4 no 3 pp 236ndash252 2012

[25] T M Choi ldquoOptimal return service charging policy for afashionmass customization programrdquo Service Science vol 5 no1 pp 56ndash68 2013

[26] B Kwok The service quality of the features of Taobaocom andtheir impacts on customer loyalty [BA FYP thesis] The HongKong Polytechnic University 2011

[27] J P Robinson P R Shaver and L S Wrightsman Measures ofPersonality and Social Psychological Attitudes Academic PressSan Diego Calif USA 1991

[28] B Edvardsson M D Johnson A Gustafsson and T StrandvikldquoThe effects of satisfaction and loyalty on profits and growthproducts versus servicesrdquoTotal QualityManagement vol 11 no7 pp S917ndashS927 2000

[29] C-HChiu T-MChoi andD Li ldquoPricewall orwar the pricingstrategies for retailersrdquo IEEE Transactions on Systems Man andCybernetics A vol 39 no 2 pp 331ndash343 2009

[30] T Xiao and T-M Choi ldquoPurchasing choices and channelstructure strategies for a two-echelon system with risk-averseplayersrdquo International Journal of Production Economics vol 120no 1 pp 54ndash65 2009

[31] C-H Chiu and T-M Choi ldquoOptimal pricing and stockingdecisions for newsvendor problem with value-at-risk consider-ationrdquo IEEE Transactions on Systems Man and Cybernetics Avol 40 no 5 pp 1116ndash1119 2010

[32] C-H Chiu T-M Choi and X Li ldquoSupply chain coordinationwith risk sensitive retailer under target sales rebaterdquo Automat-ica vol 47 no 8 pp 1617ndash1625 2011

[33] T-M Choi and C-H Chiu ldquoMean-downside-risk and mean-variance newsvendor models implications for sustainable fash-ion retailingrdquo International Journal of Production Economicsvol 135 no 2 pp 552ndash560 2012

[34] T M Choi P S Chow and T Xiao ldquoElectronic price-testingscheme for fashion retailing with information updatingrdquo Inter-national Journal of Production Economics vol 140 pp 396ndash4062012

[35] TMChoi ldquoMulti-period riskminimization purchasingmodelsfor fashion products with interest rate budget and profit targetconsiderationsrdquo Annals of Operations Research 2013

[36] B Shen TM Choi YWang andC K Y Lo ldquoThe coordinationof fashion supply chains with a risk averse supplier by themarkdown money policyrdquo IEEE Transactions on Systems Manand CyberneticsmdashSystems vol 43 pp 266ndash276 2013

[37] A D Roy ldquoSafety first and the holding of assetsrdquo Econometricavol 20 no 3 pp 431ndash449 1952

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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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Mathematical Problems in Engineering

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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Function Spaces

Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of Mathematics and Mathematical Sciences

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Algebra

Discrete Dynamics in Nature and Society

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Decision SciencesAdvances in

Discrete MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Stochastic AnalysisInternational Journal of

Mathematical Problems in Engineering 5

OSP The 7-point Likert scale is used in which 1 representsldquostrongly disagreerdquo and 7 represents ldquostrongly agreerdquo

31 Descriptive Statistics Among all the completed ques-tionnaires received 64 of the respondents have purchasedfashion products on httptaobaocom before while 36have not Thus the remaining analysis will be based on theresponses of these 64 of the collected sample in order toget valid data To be specific these consumers are consideredas httptaobaocomrsquos customers (for fashion products)The percentage of the httptaobaocom customers whobrowse products on httptaobaocom daily occupies 813 11 and 26 of httptaobaocomrsquos customers browseproducts on httptaobaocom 2-3 times a week once aweek and 2-3 times per month respectivelyThe percentagesof the httptaobaocomrsquos customers who buy products viahttptaobaocom daily 2-3 times a week once a week and2-3 times a month are 3 2 2 and 11 respectively

Regarding spending 75 of the httptaobaocomusers spend less than $500 on httptaobaocom monthly19 of them spend $500-1000 on Tobaocom monthly2 1 and 3 of the users spend $1000ndash1500 $1500ndash2000 and more than $2000 on httptaobaocom monthlyrespectively Regarding the users shopping experiences onhttptaobaocom ranking their enjoyment (in a 1ndash7 scalewith 7 being the best) more than half of the respondentsscore their enjoyment level strictly above 4 (64) 36 ofthem score as score 5 which represents the greatest group23 of them score as score 6 and 5 of them rank it 7The percentage of score 4 which means neutral excitementoccupies 15The percentages of low scores 1 2 and 3 occupy5 5 and 11 respectively

32 Hypotheses Testing Before testing hypotheses we firstconduct the reliability test to assess the internal consistencyof the variables Reliability test is measured by the Cronbachrsquosalpha in which the alpha value of 07 or above is widelyrecognized as an acceptable level of reliability [27] TheCronbach alpha values for Searching functions Guaranteeof Trade Safety Aliwangwang Alipay and Taodot are foundto be 0933 0919 0956 0958 and 0921 respectively All ofthem are higher than the acceptable level Besides the sevenquestions of customer loyalty are tested in this part TheCronbach alpha value of the customer loyalty is 0905 whichalso exceeds the acceptable level Thus we conclude that allthese dimensions are sufficiently reliable Next we proceedto test the relationships of the five featured functions andcustomer loyalty by performing multiple linear regressionanalysis It is shown that there is a statistically significantrelationship between customer loyalty and the Aliwangwangfunction The related statistical results of hypotheses aresummarized in Table 2 As such hypothesis H3 is supported(more details on the statistical analysis can be found in thesisresearch of Kwok [26])

4 Empirical Findings and Insights

From the above empirical analysis first of all we know thatthe factor of ldquofulfillment and responsiveness (Aliwangwang

function)rdquo is the most critical one as it most strongly affectscustomer loyalty of httptaobaocomrsquos customers (whopurchased fashion products before) Observe that this resultis consistent with the findings in the traditional servicecontext which suggests that customers expect quick feedbackon their requests and suggestions for improvements [2]For httptaobaocomrsquos case it can be explained by thefunction of Aliwangwang which provides instant messagingfor httptaobaocomrsquos users to send messages to the sellersThis enhances the fulfillment of httptaobaocom userson queries and needs related to product delivery and itemavailability This function also can reduce the customersrsquodoubt on the product that they want to buy and furtherconfirm their purchase decision In addition the customerscan contact the sellers whenever the sellers are online witha simple click This function extends the accessibility of thesellersonline stores without the control of the store operatinghours With Aliwangwang httptaobaocom users are ableto obtain adequate product information even if it is not givenon the product web page It increases the convenience ofhttptaobaocom Since it is a unique feature provided byhttptaobaocom but not its close competitors it becomes amajor niche for httptaobaocom

Second observe that only one hypothesis among theproposed five is proven to be true statistically A possiblereason may be given as follows Unlike the traditionalphysical store or other kinds of online electronic retailerssuch as httpwwwamazoncom etc httptaobaocomis an online marketplace in which there are thousands ofindependent retailers As a result customersrsquo perception onthe service quality and their loyalty towards the OSP maybe affected by their buying experience with the individ-ual retailers Owing to the fundamental differences manyhypotheses which apply for the more traditional bricks-and-mortar retailingelectronic retailing do not hold for theOSPs such as httptaobaocom In addition notice that inhttptaobaocom communication with individual sellers isespecially important In fact customers would like to gathermore information so that they can make better choice from alarge variety of independent retailers selling similar productson the platformThis accounts for the support ofHypothesis 3(which is about Aliwangwang and related to communicationaspect)

Third we know that a higher level of ldquofulfillment andresponsiveness (Aliwangwang function)rdquo will lead to a higherlevel of customer loyalty According to Edvardsson et al[28] customer loyalty will positively affect the revenue ofthe OSP it will be thus interesting to analytically examinewhether there exists an optimal level of ldquofulfillment andresponsivenessrdquo for the OSP We hence further conduct ananalytical study in the next section

5 Analytical Service OptimizationModel and Findings

Based on the empirical findings from Section 4 we proceedto build an analytical model for further analysis First fromthe linear regression analysis conducted in Section 4 (seeTable 2) we notice that the level of customer loyalty 119897 is related

6 Mathematical Problems in Engineering

Table 2 Summary of regression analysis

Corresponding hypothesis Independent variable Standardized coefficient t-value SigHypothesis 1 Efficiency minus0057 minus0575 0573Hypothesis 2 Reliability 0087 0896 0383Hypothesis 3 Fulfillment amp responsiveness 0582 2255 0038lowastlowast

Hypothesis 4 Flexibility 0062 0406 0690Hypothesis 5 Privacy 0307 1391 0182Remarksdependent variable customer loyaltylowastlowasthypothesis is supported

to the level of ldquofulfillment and responsivenessrdquo 120582 in a linearfunction as follows

119897 = 119886 + 119887120582 (1)

where 119886 119887 gt 0Second since it is intuitive that a higher level of customer

loyalty 119897will ldquostochasticallyrdquo (itmeans expectedly the revenueshould be higher but the exactmagnitude is unknown) lead toa higher revenue 119877(119897) for the OSP such as httptaobaocomwe have the following

119877 (119897) = 119866 (119897) + 120576 (2)

where (i) 119866(119897) is an increasing function of 119897 For analyticaltractability we further assume 119866(119897) as a concave function(ii) 120576 is a random variable which captures the noise (ieuncertainty) associated with 119877(119897) Following the literature[29] we model it to follow a symmetric distribution suchas the normal distribution with a zero mean and constantvariance 1205902

Third providing a higher level of ldquofulfillment and respon-sivenessrdquo120582 requires resourceWe hence define the respectivecost as 119862(120582) which is an increasing function of 120582 Similarto 119866(119897) we assume 119862(120582) as a convex function for analyticaltractability Notice that the above functional definitions areall intuitive and rather general

With the above model we can derive the profit 120587(120582) theexpected profit 119864[120587(120582)] and the variance of profit119881[120587(120582)] asfunctions of 120582 as follows

120587 (120582) = 119877 (119897) minus 119862 (120582)

= 119866 (119886 + 119887120582) minus 119862 (120582) + 120576

119864 [120587 (120582)] = 119866 (119886 + 119887120582) minus 119862 (120582)

119881 [120587 (120582)] = 1205902

(3)

Because e-business is known to be a risky operation weargue that the OSP has concern on risk and possesses a riskaverse attitude [24] As such we employ the following mean-variance safety first measure (SFM(120582)) as the objective func-tion (for the modeling of risk averse optimization objectivessee Xiao and Choi [30] Chiu and Choi [31] Chiu et al [32]Choi and Chiu [33] Choi et al [34] Choi [35] and Shen etal [36]) for the OSP as follows

SFM (120582) =(119864 [120587 (120582)] minus 120573)

radic119881 [120587 (120582)] (4)

where 120573 is the expected profit target that the OSP wants toachieve

For the meaning behind SFM(120582) please notice thatby Bienayme-Tchebycheff inequality we have the followinginequality

119875 (|120587 (120582) minus 119864 [120587 (120582)]| ge 119864 [120587 (120582)] minus 120573) le119881 [120587 (120582)]

(119864 [120587 (120582)] minus 120573)2

(5)

997904rArr 119875 (120587 (120582) le 120573) le119881 [120587 (120582)]

(119864 [120587 (120582)] minus 120573)2equiv

1

SFM(120582)2 (6)

Thus the RHS of (6) gives the analytical upper boundfor 119875(120587(120582) le 120573) Obviously maximizing SFM(120582) yields aminimized upper bound for 119875(120587(120582) le 120573) and hence SFM(120582)

is called the safety first measure (see [37])With the above model we define (7) and then have

Proposition 1 Notice that 119878119866(120582) and 119878

119862(120582) are the first order

derivatives of 119866(119886 + 119887120582) and 119862(120582) respectively and they alsorepresent the corresponding slopes as follows

120582lowast

SFM = argmax120582

SFM (120582)

120582lowast

EP = argmax120582

119864 [120587 (120582)]

119878119866(120582) =

119889119866 (119886 + 119887120582)

119889120582

119878119862(120582) =

119889119862 (120582)

119889120582

(7)

Proposition 1 120582lowast119878119865119872

uniquely exists and it is equal toarg120582119878119866(120582)119878119862(120582) = 1

Proof of Proposition 1 All proofs are placed in the Appendix

Proposition 1 first presents the uniqueness feature of theoptimal level of fulfillment and responsiveness (ie optimalservice level) which maximizes the safety first objectiveIt then reveals that this optimal service level is the onewhich equalizes 119878

119866(120582) and 119878

119862(120582) In other words when the

rates of change of 119866(119886 + 119887120582) and 119862(120582) are the same thecorresponding service level 120582 is optimal From Proposition 1we have Proposition 2

Mathematical Problems in Engineering 7

Proposition 2 (a) A larger 119887 implies a larger 120582lowastSFM (b) 120582lowast

SFMis independent of 120573 and 120590

Observe that a larger 119887 means that the customer loy-alty is more positively correlated to the service level 120582Proposition 2(a) hence indicates that it will lead to a biggeroptimal service level because the optimal solution refersto the one which balances the increase of revenue 119878

119866(120582)

and rise of cost 119878119862(120582) It is also interesting to note from

Proposition 2(b) that 120582lowastSFM is independent of 120573 and 120590 Thisfinding can be explained by the fact that the variance of profit119881[120587(120582)] is a constant which is independent of the service level120582Thus the corresponding optimal service level is not relatedto 120590 as well as the expected profit target 120573 and will in factbehave like the risk neutral case as shown in Proposition 3below

Proposition 3 120582lowast119864119875

uniquely exists and 120582lowastSFM = 120582lowast

119864119875

Proposition 3 shows a rather surprising result that the riskaverse and risk neutral OSPs will have the same optimal ser-vice levels for this particular case In other words irrespectiveof the risk preference the optimal service level is the sameThis finding is important because it shows the fundamentaldifference between different kinds of operations manage-ment problems For example for inventory planning underuncertainty the optimal inventory decision for the risk averseand risk neutral cases will be different However in serviceoptimization the situation can be different as shown here

6 Concluding Remarks

We conclude by discussing some research implicationsderived from the discussions above First the empiricalfindings suggest that in order to enhance customer loyaltyonline stores should develop marketing strategies to enhanceldquofulfillment and responsivenessrdquo since they strongly affectcustomer loyalty httptaobaocom has devoted valuablecorporate resources to it andwe argue that httptaobaocomcan further improve the functions of Aliwangwang toachieve even better communication between the sellersand httptaobaocom users Some probable enhancementsincluding inserting flash games and converting to smart-phone apps can all help to attract the httptaobaocom usersto use Aliwangwang Furthermore httptaobaocom canmake use of Aliwangwang to obtain personalized data relatedto the customersrsquo preferences As a result a higher degreeof web site customization (eg based on past purchases andother customersrsquo information collected) can be provided toindividual customers

Second for the empirical result driven analytical studieswe find that the optimal service level on ldquofulfillment andresponsivenessrdquo function for the risk averse OSP uniquelyexists and can be expressed as the service level which balancesthe increase of revenue 119878

119866(120582) and the rise of cost 119878

119862(120582) We

further show that if the customerrsquos loyalty is more positivelycorrelated to the service level 120582 it will lead to a larger optimalservice level Moreover the optimal service level under thesafety first objective is independent of profit target 120573 and

uncertainty threshold120590 Finally we analytically prove that theoptimal service level does not depend on the risk preferenceof the OSP

For future research there are two directions Empiricallyit is useful to examine more functions of OSP such ashttptaobaocom This paper only focuses on the severalcore functions specifically adopted by httptaobaocom andthere are some more which deserve further explorationsBesides unlike the traditional bricks-and-mortar and elec-tronic retailers the operation of httptaobaocom involvesthousands of independent sellers As a result the customersrsquoperceived service quality of the platform would be consid-erably affected by their buying experience with individualsellers In the future it is worth investigating themodificationof the existing service quality-scale measurement to cater forthis specific type of newbusinessmodelThe second directionfor further studies relates to the theoretical analysis basedon more comprehensive economics analytical models Webelieve that the findings revealed by this paper can lay thefoundation for many further empirical and analytical studies

Appendix

All Proofs of Propositions

Proof of Proposition 1 From (4) we have

SFM (120582) =(119864 [120587 (120582)] minus 120573)

radic119881 [120587 (120582)] (A1)

Since 119881[120587(120582)] = 1205902 (A2) becomes

SFM (120582) =(119864 [120587 (120582)] minus 120573)

120590 (A2)

119889SFM (120582)

119889120582=119889119864 [120587 (120582)] 119889120582

120590

=119889 [119866 (119886 + 119887120582) minus 119862 (120582)] 119889120582

120590

(A3)

1198892SFM (120582)

1198891205822=1

120590(1198892

119866 (119886 + 119887120582)

1198891205822minus1198892

119862 (120582)

1198891205822) (A4)

Since 119866(119886 + 119887120582) is concave and 119862(120582) is convex1198892SFM(120582)119889120582

2

lt 0 which means SFM(120582) is concave and120582lowast

SFM uniquely exists In addition notice that 120582lowast

SFM =

arg120582[119889SFM(120582)119889120582 = 0] Thus we have 120582lowastSFM = arg

120582119878119866(120582)

119878119862(120582) = 1

Proof of Proposition 2 Part (a) notice that 120582lowast

SFM =

arg120582119878119866(120582)119878119862(120582) = 1 and

119878119866(120582) =

119889 [119866 (119886 + 119887120582)]

119889120582

=119889 (119886 + 119887120582)

119889120582

119889119866 (119886 + 119887120582)

119889 (119886 + 119887120582)= 119887

119889119866 (119886 + 119887120582)

119889 (119886 + 119887120582)

(A5)

Thus 120582lowastSFM = arg120582119878119866(120582)119878119862(120582) = 1 = arg119878

119862(120582) = 119887(119889119866(119886+

119887120582)119889(119886 + 119887120582))

8 Mathematical Problems in Engineering

Define 119891(120582) = 119878119862(120582)(119889119866(119897)119889119897)|

119897=119886+119887120582 Consider

1198911015840

(120582) =1

( (119889119866 (119897) 119889119897)|119897=119886+119887120582

)2

times ((119889119866 (119897)

119889119897

10038161003816100381610038161003816100381610038161003816119897=119886+119887120582)119889119878119862(120582)

119889120582

minus119878119862(120582)

119889

119889120582(119889119866 (119897)

119889119897

10038161003816100381610038161003816100381610038161003816119897=119886+119887120582))

(A6)

We have

(a) 119889119878119862(120582)119889120582 = 119889

2

119862(120582)1198891205822

gt 0 [∵ 119862(120582) is convex in120582]

(b) (119889119866(119897)119889119897)|119897=119886+119887120582

gt 0 [∵ 119866(119897) is increasing in 119897](c) 119878119862(120582) = 119889119862(120582)119889120582 gt 0 [∵ 119862(120582) is increasing in 120582]

(d) (119889119889120582)((119889119866(119897)119889119897)|119897=119886+119887120582

) = [1198892

119866(119897)1198891198972

sdot 119889(119897 = 119886 +

119887120582)119889120582]119897=119886+119887120582

= 119887(1198892

119866(119897)1198891198972

)119897=119886+119887120582

lt 0 [∵ 119866(119897) isconcave in 119897]

Therefore we have 1198911015840(120582) gt 0 for all 120582 In other words 119891(120582) isincreasing in 120582 Therefore a larger 119887 implies a larger120582lowastSFM

Part (b) since both 119878119866(120582) and 119878

119862(120582) are independent of 120573

and 120590 it is obvious that 120582lowastSFM = arg120582119878119866(120582)119878119862(120582) = 1 is also

independent of 120573 and 120590

Proof of Proposition 3 First notice that 119864[120587(120582)] is a concavefunction Thus 120582lowastEP uniquely exists Second from (A3) wehave

119889SFM (120582)

119889120582=119889119864 [120587 (120582)] 119889120582

120590=1

120590sdot119889119864 [120587 (120582)]

119889120582 (A7)

Obviously from (A7) we have 120582lowastSFM = arg120582[119889SFM(120582)119889120582 =

0] = arg120582[119889119864[120587(120582)]119889120582 = 0] which is the same as 120582lowastEP Thus

120582lowast

SFM = 120582lowast

EP

Authorsrsquo Contribution

All authors have good contribution to the paper and thelisting follows their surnames

Acknowledgments

The authors thank the editor and the two reviewers for theirkind comments on this paperThey declare that none of themis related to the target case company ldquohttptaobaocomrdquo andother companies named in the paper which may constitute aconflict of interest This paper is partially supported by theRGC(HK)-GRF under grant ac of PolyU 542010H

References

[1] Z Yang ldquoCustomer perceptions of service quality in internet-based electronic commercerdquo inProceedings of the 30th EuropeanMarketing Academy Conference (EMAC rsquo01) pp 8ndash11 BergenNorway 2001

[2] V A Zeithaml ldquoService excellent in electronic channelsrdquoMan-aging Service Quality vol 12 no 3 pp 135ndash139 2002

[3] Parasuraman A L Berry and V A Zeithaml ldquoSERVQUALa multiple-item scale for measuring consumer perceptions ofservice qualityrdquo Journal of Retailing vol 64 no 1 pp 12ndash401988

[4] A Parasuraman and D Grewal ldquoThe impact of technology onthe quality-value-loyalty chain a research agendardquo Journal ofthe Academy of Marketing Science vol 28 no 1 pp 168ndash1742000

[5] A C R Van Riel V Liljander and P Jurriens ldquoExploringconsumer evaluations of e-services a portal siterdquo InternationalJournal of Service Industry Management vol 12 no 4 pp 359ndash377 2001

[6] S Devaraj M Fan and R Kohli ldquoAntecedents of B2C channelsatisfaction and preference validating e-commerce metricsrdquoInformation Systems Research vol 13 no 3 pp 316ndash333 2002

[7] J Kim and J Lee ldquoCritical design factors for successful e-commerce systemsrdquoBehaviour and Information Technology vol21 no 3 pp 185ndash199 2002

[8] S Negash T Ryan and M Igbaria ldquoQuality and effectivenessin Web-based customer support systemsrdquo Information andManagement vol 40 no 8 pp 757ndash768 2003

[9] Y-F Kuo ldquoA study on service quality of virtual communitywebsitesrdquo Total Quality Management and Business Excellencevol 14 no 4 pp 461ndash473 2003

[10] F X Zhu W Wymer and I Chen ldquoIT-based services and ser-vice quality in consumer bankingrdquo International Journal of Ser-vice Industry Management vol 13 no 1 pp 69ndash90 2002

[11] J Santos ldquoE-service quality a model of virtual service qualitydimensionsrdquo Managing Service Quality vol 13 no 3 pp 233ndash246 2003

[12] F Buttle ldquoSERVQUAL review critique research agendardquo Euro-pean Journal of Marketing vol 30 no 1 pp 8ndash32 1996

[13] H Stewart C Hope and A Muhlemann ldquoProfessional servicequality a step beyond other servicesrdquo Journal of Retailing andConsumer Services vol 5 no 4 pp 209ndash222 1998

[14] J J Cronin and S A Taylor ldquoMeasuring service quality a re-examination and extensionrdquo Journal of Marketing vol 56 no 3pp 55ndash68 1992

[15] A Parasuraman L L Berry and V A Zeithaml ldquoMore onimproving service quality measurementrdquo Journal of Retailingvol 69 no 1 pp 140ndash147 1993

[16] A Parasuraman V A Zeithaml and A Malhotra ldquoE-S-QUALa multiple-item scale for assessing electronic service qualityrdquoJournal of Service Research vol 7 no 3 pp 213ndash233 2005

[17] S Akinci E Atilgan-Inan and S Aksoy ldquoRe-assessment of E-S-Qual and E-RecS-Qual in a pure service settingrdquo Journal ofBusiness Research vol 63 no 3 pp 232ndash240 2010

[18] J R Rossitier ldquoER-SERVCOMPSQUAL a measure of e-retail-ing service components qualityrdquo Service Science vol 1 no 4 pp212ndash2224 2009

[19] D X Ding P J-H Hu and O R L Sheng ldquoE-SELFQUALa scale for measuring online self-service qualityrdquo Journal ofBusiness Research vol 64 no 5 pp 508ndash515 2011

[20] S Ha and L Stoel ldquoOnline apparel retailing roles of e-shoppingquality and experiential e-shopping motivesrdquo Journal of ServiceManagement vol 23 no 2 pp 197ndash215 2012

[21] Y Hu and Q Qiang ldquoAn equilibriummodel of online shoppingsupply chain networks with service capacity investmentrdquo Ser-vice Science 2013

[22] R T Rust and A J Zahorik ldquoCustomer satisfaction customerretention and market sharerdquo Journal of Retailing vol 69 no 2pp 193ndash215 1993

Mathematical Problems in Engineering 9

[23] L Groslashnholdt A Martensen and K Kristensen ldquoThe relation-ship between customer satisfaction and loyalty cross-industrydifferencesrdquo Total Quality Management vol 11 no 4ndash6 ppS509ndashS514 2000

[24] T Xiao T M Choi D Yang and T C E Cheng ldquoService com-mitment strategy and pricing decisions in retail supply chainswith risk-averse playersrdquo Service Science vol 4 no 3 pp 236ndash252 2012

[25] T M Choi ldquoOptimal return service charging policy for afashionmass customization programrdquo Service Science vol 5 no1 pp 56ndash68 2013

[26] B Kwok The service quality of the features of Taobaocom andtheir impacts on customer loyalty [BA FYP thesis] The HongKong Polytechnic University 2011

[27] J P Robinson P R Shaver and L S Wrightsman Measures ofPersonality and Social Psychological Attitudes Academic PressSan Diego Calif USA 1991

[28] B Edvardsson M D Johnson A Gustafsson and T StrandvikldquoThe effects of satisfaction and loyalty on profits and growthproducts versus servicesrdquoTotal QualityManagement vol 11 no7 pp S917ndashS927 2000

[29] C-HChiu T-MChoi andD Li ldquoPricewall orwar the pricingstrategies for retailersrdquo IEEE Transactions on Systems Man andCybernetics A vol 39 no 2 pp 331ndash343 2009

[30] T Xiao and T-M Choi ldquoPurchasing choices and channelstructure strategies for a two-echelon system with risk-averseplayersrdquo International Journal of Production Economics vol 120no 1 pp 54ndash65 2009

[31] C-H Chiu and T-M Choi ldquoOptimal pricing and stockingdecisions for newsvendor problem with value-at-risk consider-ationrdquo IEEE Transactions on Systems Man and Cybernetics Avol 40 no 5 pp 1116ndash1119 2010

[32] C-H Chiu T-M Choi and X Li ldquoSupply chain coordinationwith risk sensitive retailer under target sales rebaterdquo Automat-ica vol 47 no 8 pp 1617ndash1625 2011

[33] T-M Choi and C-H Chiu ldquoMean-downside-risk and mean-variance newsvendor models implications for sustainable fash-ion retailingrdquo International Journal of Production Economicsvol 135 no 2 pp 552ndash560 2012

[34] T M Choi P S Chow and T Xiao ldquoElectronic price-testingscheme for fashion retailing with information updatingrdquo Inter-national Journal of Production Economics vol 140 pp 396ndash4062012

[35] TMChoi ldquoMulti-period riskminimization purchasingmodelsfor fashion products with interest rate budget and profit targetconsiderationsrdquo Annals of Operations Research 2013

[36] B Shen TM Choi YWang andC K Y Lo ldquoThe coordinationof fashion supply chains with a risk averse supplier by themarkdown money policyrdquo IEEE Transactions on Systems Manand CyberneticsmdashSystems vol 43 pp 266ndash276 2013

[37] A D Roy ldquoSafety first and the holding of assetsrdquo Econometricavol 20 no 3 pp 431ndash449 1952

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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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Mathematical Problems in Engineering

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Differential EquationsInternational Journal of

Volume 2014

Applied MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

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Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical PhysicsAdvances in

Complex AnalysisJournal of

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OptimizationJournal of

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CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

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Operations ResearchAdvances in

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Function Spaces

Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of Mathematics and Mathematical Sciences

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Algebra

Discrete Dynamics in Nature and Society

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Decision SciencesAdvances in

Discrete MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Stochastic AnalysisInternational Journal of

6 Mathematical Problems in Engineering

Table 2 Summary of regression analysis

Corresponding hypothesis Independent variable Standardized coefficient t-value SigHypothesis 1 Efficiency minus0057 minus0575 0573Hypothesis 2 Reliability 0087 0896 0383Hypothesis 3 Fulfillment amp responsiveness 0582 2255 0038lowastlowast

Hypothesis 4 Flexibility 0062 0406 0690Hypothesis 5 Privacy 0307 1391 0182Remarksdependent variable customer loyaltylowastlowasthypothesis is supported

to the level of ldquofulfillment and responsivenessrdquo 120582 in a linearfunction as follows

119897 = 119886 + 119887120582 (1)

where 119886 119887 gt 0Second since it is intuitive that a higher level of customer

loyalty 119897will ldquostochasticallyrdquo (itmeans expectedly the revenueshould be higher but the exactmagnitude is unknown) lead toa higher revenue 119877(119897) for the OSP such as httptaobaocomwe have the following

119877 (119897) = 119866 (119897) + 120576 (2)

where (i) 119866(119897) is an increasing function of 119897 For analyticaltractability we further assume 119866(119897) as a concave function(ii) 120576 is a random variable which captures the noise (ieuncertainty) associated with 119877(119897) Following the literature[29] we model it to follow a symmetric distribution suchas the normal distribution with a zero mean and constantvariance 1205902

Third providing a higher level of ldquofulfillment and respon-sivenessrdquo120582 requires resourceWe hence define the respectivecost as 119862(120582) which is an increasing function of 120582 Similarto 119866(119897) we assume 119862(120582) as a convex function for analyticaltractability Notice that the above functional definitions areall intuitive and rather general

With the above model we can derive the profit 120587(120582) theexpected profit 119864[120587(120582)] and the variance of profit119881[120587(120582)] asfunctions of 120582 as follows

120587 (120582) = 119877 (119897) minus 119862 (120582)

= 119866 (119886 + 119887120582) minus 119862 (120582) + 120576

119864 [120587 (120582)] = 119866 (119886 + 119887120582) minus 119862 (120582)

119881 [120587 (120582)] = 1205902

(3)

Because e-business is known to be a risky operation weargue that the OSP has concern on risk and possesses a riskaverse attitude [24] As such we employ the following mean-variance safety first measure (SFM(120582)) as the objective func-tion (for the modeling of risk averse optimization objectivessee Xiao and Choi [30] Chiu and Choi [31] Chiu et al [32]Choi and Chiu [33] Choi et al [34] Choi [35] and Shen etal [36]) for the OSP as follows

SFM (120582) =(119864 [120587 (120582)] minus 120573)

radic119881 [120587 (120582)] (4)

where 120573 is the expected profit target that the OSP wants toachieve

For the meaning behind SFM(120582) please notice thatby Bienayme-Tchebycheff inequality we have the followinginequality

119875 (|120587 (120582) minus 119864 [120587 (120582)]| ge 119864 [120587 (120582)] minus 120573) le119881 [120587 (120582)]

(119864 [120587 (120582)] minus 120573)2

(5)

997904rArr 119875 (120587 (120582) le 120573) le119881 [120587 (120582)]

(119864 [120587 (120582)] minus 120573)2equiv

1

SFM(120582)2 (6)

Thus the RHS of (6) gives the analytical upper boundfor 119875(120587(120582) le 120573) Obviously maximizing SFM(120582) yields aminimized upper bound for 119875(120587(120582) le 120573) and hence SFM(120582)

is called the safety first measure (see [37])With the above model we define (7) and then have

Proposition 1 Notice that 119878119866(120582) and 119878

119862(120582) are the first order

derivatives of 119866(119886 + 119887120582) and 119862(120582) respectively and they alsorepresent the corresponding slopes as follows

120582lowast

SFM = argmax120582

SFM (120582)

120582lowast

EP = argmax120582

119864 [120587 (120582)]

119878119866(120582) =

119889119866 (119886 + 119887120582)

119889120582

119878119862(120582) =

119889119862 (120582)

119889120582

(7)

Proposition 1 120582lowast119878119865119872

uniquely exists and it is equal toarg120582119878119866(120582)119878119862(120582) = 1

Proof of Proposition 1 All proofs are placed in the Appendix

Proposition 1 first presents the uniqueness feature of theoptimal level of fulfillment and responsiveness (ie optimalservice level) which maximizes the safety first objectiveIt then reveals that this optimal service level is the onewhich equalizes 119878

119866(120582) and 119878

119862(120582) In other words when the

rates of change of 119866(119886 + 119887120582) and 119862(120582) are the same thecorresponding service level 120582 is optimal From Proposition 1we have Proposition 2

Mathematical Problems in Engineering 7

Proposition 2 (a) A larger 119887 implies a larger 120582lowastSFM (b) 120582lowast

SFMis independent of 120573 and 120590

Observe that a larger 119887 means that the customer loy-alty is more positively correlated to the service level 120582Proposition 2(a) hence indicates that it will lead to a biggeroptimal service level because the optimal solution refersto the one which balances the increase of revenue 119878

119866(120582)

and rise of cost 119878119862(120582) It is also interesting to note from

Proposition 2(b) that 120582lowastSFM is independent of 120573 and 120590 Thisfinding can be explained by the fact that the variance of profit119881[120587(120582)] is a constant which is independent of the service level120582Thus the corresponding optimal service level is not relatedto 120590 as well as the expected profit target 120573 and will in factbehave like the risk neutral case as shown in Proposition 3below

Proposition 3 120582lowast119864119875

uniquely exists and 120582lowastSFM = 120582lowast

119864119875

Proposition 3 shows a rather surprising result that the riskaverse and risk neutral OSPs will have the same optimal ser-vice levels for this particular case In other words irrespectiveof the risk preference the optimal service level is the sameThis finding is important because it shows the fundamentaldifference between different kinds of operations manage-ment problems For example for inventory planning underuncertainty the optimal inventory decision for the risk averseand risk neutral cases will be different However in serviceoptimization the situation can be different as shown here

6 Concluding Remarks

We conclude by discussing some research implicationsderived from the discussions above First the empiricalfindings suggest that in order to enhance customer loyaltyonline stores should develop marketing strategies to enhanceldquofulfillment and responsivenessrdquo since they strongly affectcustomer loyalty httptaobaocom has devoted valuablecorporate resources to it andwe argue that httptaobaocomcan further improve the functions of Aliwangwang toachieve even better communication between the sellersand httptaobaocom users Some probable enhancementsincluding inserting flash games and converting to smart-phone apps can all help to attract the httptaobaocom usersto use Aliwangwang Furthermore httptaobaocom canmake use of Aliwangwang to obtain personalized data relatedto the customersrsquo preferences As a result a higher degreeof web site customization (eg based on past purchases andother customersrsquo information collected) can be provided toindividual customers

Second for the empirical result driven analytical studieswe find that the optimal service level on ldquofulfillment andresponsivenessrdquo function for the risk averse OSP uniquelyexists and can be expressed as the service level which balancesthe increase of revenue 119878

119866(120582) and the rise of cost 119878

119862(120582) We

further show that if the customerrsquos loyalty is more positivelycorrelated to the service level 120582 it will lead to a larger optimalservice level Moreover the optimal service level under thesafety first objective is independent of profit target 120573 and

uncertainty threshold120590 Finally we analytically prove that theoptimal service level does not depend on the risk preferenceof the OSP

For future research there are two directions Empiricallyit is useful to examine more functions of OSP such ashttptaobaocom This paper only focuses on the severalcore functions specifically adopted by httptaobaocom andthere are some more which deserve further explorationsBesides unlike the traditional bricks-and-mortar and elec-tronic retailers the operation of httptaobaocom involvesthousands of independent sellers As a result the customersrsquoperceived service quality of the platform would be consid-erably affected by their buying experience with individualsellers In the future it is worth investigating themodificationof the existing service quality-scale measurement to cater forthis specific type of newbusinessmodelThe second directionfor further studies relates to the theoretical analysis basedon more comprehensive economics analytical models Webelieve that the findings revealed by this paper can lay thefoundation for many further empirical and analytical studies

Appendix

All Proofs of Propositions

Proof of Proposition 1 From (4) we have

SFM (120582) =(119864 [120587 (120582)] minus 120573)

radic119881 [120587 (120582)] (A1)

Since 119881[120587(120582)] = 1205902 (A2) becomes

SFM (120582) =(119864 [120587 (120582)] minus 120573)

120590 (A2)

119889SFM (120582)

119889120582=119889119864 [120587 (120582)] 119889120582

120590

=119889 [119866 (119886 + 119887120582) minus 119862 (120582)] 119889120582

120590

(A3)

1198892SFM (120582)

1198891205822=1

120590(1198892

119866 (119886 + 119887120582)

1198891205822minus1198892

119862 (120582)

1198891205822) (A4)

Since 119866(119886 + 119887120582) is concave and 119862(120582) is convex1198892SFM(120582)119889120582

2

lt 0 which means SFM(120582) is concave and120582lowast

SFM uniquely exists In addition notice that 120582lowast

SFM =

arg120582[119889SFM(120582)119889120582 = 0] Thus we have 120582lowastSFM = arg

120582119878119866(120582)

119878119862(120582) = 1

Proof of Proposition 2 Part (a) notice that 120582lowast

SFM =

arg120582119878119866(120582)119878119862(120582) = 1 and

119878119866(120582) =

119889 [119866 (119886 + 119887120582)]

119889120582

=119889 (119886 + 119887120582)

119889120582

119889119866 (119886 + 119887120582)

119889 (119886 + 119887120582)= 119887

119889119866 (119886 + 119887120582)

119889 (119886 + 119887120582)

(A5)

Thus 120582lowastSFM = arg120582119878119866(120582)119878119862(120582) = 1 = arg119878

119862(120582) = 119887(119889119866(119886+

119887120582)119889(119886 + 119887120582))

8 Mathematical Problems in Engineering

Define 119891(120582) = 119878119862(120582)(119889119866(119897)119889119897)|

119897=119886+119887120582 Consider

1198911015840

(120582) =1

( (119889119866 (119897) 119889119897)|119897=119886+119887120582

)2

times ((119889119866 (119897)

119889119897

10038161003816100381610038161003816100381610038161003816119897=119886+119887120582)119889119878119862(120582)

119889120582

minus119878119862(120582)

119889

119889120582(119889119866 (119897)

119889119897

10038161003816100381610038161003816100381610038161003816119897=119886+119887120582))

(A6)

We have

(a) 119889119878119862(120582)119889120582 = 119889

2

119862(120582)1198891205822

gt 0 [∵ 119862(120582) is convex in120582]

(b) (119889119866(119897)119889119897)|119897=119886+119887120582

gt 0 [∵ 119866(119897) is increasing in 119897](c) 119878119862(120582) = 119889119862(120582)119889120582 gt 0 [∵ 119862(120582) is increasing in 120582]

(d) (119889119889120582)((119889119866(119897)119889119897)|119897=119886+119887120582

) = [1198892

119866(119897)1198891198972

sdot 119889(119897 = 119886 +

119887120582)119889120582]119897=119886+119887120582

= 119887(1198892

119866(119897)1198891198972

)119897=119886+119887120582

lt 0 [∵ 119866(119897) isconcave in 119897]

Therefore we have 1198911015840(120582) gt 0 for all 120582 In other words 119891(120582) isincreasing in 120582 Therefore a larger 119887 implies a larger120582lowastSFM

Part (b) since both 119878119866(120582) and 119878

119862(120582) are independent of 120573

and 120590 it is obvious that 120582lowastSFM = arg120582119878119866(120582)119878119862(120582) = 1 is also

independent of 120573 and 120590

Proof of Proposition 3 First notice that 119864[120587(120582)] is a concavefunction Thus 120582lowastEP uniquely exists Second from (A3) wehave

119889SFM (120582)

119889120582=119889119864 [120587 (120582)] 119889120582

120590=1

120590sdot119889119864 [120587 (120582)]

119889120582 (A7)

Obviously from (A7) we have 120582lowastSFM = arg120582[119889SFM(120582)119889120582 =

0] = arg120582[119889119864[120587(120582)]119889120582 = 0] which is the same as 120582lowastEP Thus

120582lowast

SFM = 120582lowast

EP

Authorsrsquo Contribution

All authors have good contribution to the paper and thelisting follows their surnames

Acknowledgments

The authors thank the editor and the two reviewers for theirkind comments on this paperThey declare that none of themis related to the target case company ldquohttptaobaocomrdquo andother companies named in the paper which may constitute aconflict of interest This paper is partially supported by theRGC(HK)-GRF under grant ac of PolyU 542010H

References

[1] Z Yang ldquoCustomer perceptions of service quality in internet-based electronic commercerdquo inProceedings of the 30th EuropeanMarketing Academy Conference (EMAC rsquo01) pp 8ndash11 BergenNorway 2001

[2] V A Zeithaml ldquoService excellent in electronic channelsrdquoMan-aging Service Quality vol 12 no 3 pp 135ndash139 2002

[3] Parasuraman A L Berry and V A Zeithaml ldquoSERVQUALa multiple-item scale for measuring consumer perceptions ofservice qualityrdquo Journal of Retailing vol 64 no 1 pp 12ndash401988

[4] A Parasuraman and D Grewal ldquoThe impact of technology onthe quality-value-loyalty chain a research agendardquo Journal ofthe Academy of Marketing Science vol 28 no 1 pp 168ndash1742000

[5] A C R Van Riel V Liljander and P Jurriens ldquoExploringconsumer evaluations of e-services a portal siterdquo InternationalJournal of Service Industry Management vol 12 no 4 pp 359ndash377 2001

[6] S Devaraj M Fan and R Kohli ldquoAntecedents of B2C channelsatisfaction and preference validating e-commerce metricsrdquoInformation Systems Research vol 13 no 3 pp 316ndash333 2002

[7] J Kim and J Lee ldquoCritical design factors for successful e-commerce systemsrdquoBehaviour and Information Technology vol21 no 3 pp 185ndash199 2002

[8] S Negash T Ryan and M Igbaria ldquoQuality and effectivenessin Web-based customer support systemsrdquo Information andManagement vol 40 no 8 pp 757ndash768 2003

[9] Y-F Kuo ldquoA study on service quality of virtual communitywebsitesrdquo Total Quality Management and Business Excellencevol 14 no 4 pp 461ndash473 2003

[10] F X Zhu W Wymer and I Chen ldquoIT-based services and ser-vice quality in consumer bankingrdquo International Journal of Ser-vice Industry Management vol 13 no 1 pp 69ndash90 2002

[11] J Santos ldquoE-service quality a model of virtual service qualitydimensionsrdquo Managing Service Quality vol 13 no 3 pp 233ndash246 2003

[12] F Buttle ldquoSERVQUAL review critique research agendardquo Euro-pean Journal of Marketing vol 30 no 1 pp 8ndash32 1996

[13] H Stewart C Hope and A Muhlemann ldquoProfessional servicequality a step beyond other servicesrdquo Journal of Retailing andConsumer Services vol 5 no 4 pp 209ndash222 1998

[14] J J Cronin and S A Taylor ldquoMeasuring service quality a re-examination and extensionrdquo Journal of Marketing vol 56 no 3pp 55ndash68 1992

[15] A Parasuraman L L Berry and V A Zeithaml ldquoMore onimproving service quality measurementrdquo Journal of Retailingvol 69 no 1 pp 140ndash147 1993

[16] A Parasuraman V A Zeithaml and A Malhotra ldquoE-S-QUALa multiple-item scale for assessing electronic service qualityrdquoJournal of Service Research vol 7 no 3 pp 213ndash233 2005

[17] S Akinci E Atilgan-Inan and S Aksoy ldquoRe-assessment of E-S-Qual and E-RecS-Qual in a pure service settingrdquo Journal ofBusiness Research vol 63 no 3 pp 232ndash240 2010

[18] J R Rossitier ldquoER-SERVCOMPSQUAL a measure of e-retail-ing service components qualityrdquo Service Science vol 1 no 4 pp212ndash2224 2009

[19] D X Ding P J-H Hu and O R L Sheng ldquoE-SELFQUALa scale for measuring online self-service qualityrdquo Journal ofBusiness Research vol 64 no 5 pp 508ndash515 2011

[20] S Ha and L Stoel ldquoOnline apparel retailing roles of e-shoppingquality and experiential e-shopping motivesrdquo Journal of ServiceManagement vol 23 no 2 pp 197ndash215 2012

[21] Y Hu and Q Qiang ldquoAn equilibriummodel of online shoppingsupply chain networks with service capacity investmentrdquo Ser-vice Science 2013

[22] R T Rust and A J Zahorik ldquoCustomer satisfaction customerretention and market sharerdquo Journal of Retailing vol 69 no 2pp 193ndash215 1993

Mathematical Problems in Engineering 9

[23] L Groslashnholdt A Martensen and K Kristensen ldquoThe relation-ship between customer satisfaction and loyalty cross-industrydifferencesrdquo Total Quality Management vol 11 no 4ndash6 ppS509ndashS514 2000

[24] T Xiao T M Choi D Yang and T C E Cheng ldquoService com-mitment strategy and pricing decisions in retail supply chainswith risk-averse playersrdquo Service Science vol 4 no 3 pp 236ndash252 2012

[25] T M Choi ldquoOptimal return service charging policy for afashionmass customization programrdquo Service Science vol 5 no1 pp 56ndash68 2013

[26] B Kwok The service quality of the features of Taobaocom andtheir impacts on customer loyalty [BA FYP thesis] The HongKong Polytechnic University 2011

[27] J P Robinson P R Shaver and L S Wrightsman Measures ofPersonality and Social Psychological Attitudes Academic PressSan Diego Calif USA 1991

[28] B Edvardsson M D Johnson A Gustafsson and T StrandvikldquoThe effects of satisfaction and loyalty on profits and growthproducts versus servicesrdquoTotal QualityManagement vol 11 no7 pp S917ndashS927 2000

[29] C-HChiu T-MChoi andD Li ldquoPricewall orwar the pricingstrategies for retailersrdquo IEEE Transactions on Systems Man andCybernetics A vol 39 no 2 pp 331ndash343 2009

[30] T Xiao and T-M Choi ldquoPurchasing choices and channelstructure strategies for a two-echelon system with risk-averseplayersrdquo International Journal of Production Economics vol 120no 1 pp 54ndash65 2009

[31] C-H Chiu and T-M Choi ldquoOptimal pricing and stockingdecisions for newsvendor problem with value-at-risk consider-ationrdquo IEEE Transactions on Systems Man and Cybernetics Avol 40 no 5 pp 1116ndash1119 2010

[32] C-H Chiu T-M Choi and X Li ldquoSupply chain coordinationwith risk sensitive retailer under target sales rebaterdquo Automat-ica vol 47 no 8 pp 1617ndash1625 2011

[33] T-M Choi and C-H Chiu ldquoMean-downside-risk and mean-variance newsvendor models implications for sustainable fash-ion retailingrdquo International Journal of Production Economicsvol 135 no 2 pp 552ndash560 2012

[34] T M Choi P S Chow and T Xiao ldquoElectronic price-testingscheme for fashion retailing with information updatingrdquo Inter-national Journal of Production Economics vol 140 pp 396ndash4062012

[35] TMChoi ldquoMulti-period riskminimization purchasingmodelsfor fashion products with interest rate budget and profit targetconsiderationsrdquo Annals of Operations Research 2013

[36] B Shen TM Choi YWang andC K Y Lo ldquoThe coordinationof fashion supply chains with a risk averse supplier by themarkdown money policyrdquo IEEE Transactions on Systems Manand CyberneticsmdashSystems vol 43 pp 266ndash276 2013

[37] A D Roy ldquoSafety first and the holding of assetsrdquo Econometricavol 20 no 3 pp 431ndash449 1952

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical Problems in Engineering

Hindawi Publishing Corporationhttpwwwhindawicom

Differential EquationsInternational Journal of

Volume 2014

Applied MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical PhysicsAdvances in

Complex AnalysisJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

OptimizationJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Operations ResearchAdvances in

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Function Spaces

Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of Mathematics and Mathematical Sciences

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Algebra

Discrete Dynamics in Nature and Society

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Decision SciencesAdvances in

Discrete MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Stochastic AnalysisInternational Journal of

Mathematical Problems in Engineering 7

Proposition 2 (a) A larger 119887 implies a larger 120582lowastSFM (b) 120582lowast

SFMis independent of 120573 and 120590

Observe that a larger 119887 means that the customer loy-alty is more positively correlated to the service level 120582Proposition 2(a) hence indicates that it will lead to a biggeroptimal service level because the optimal solution refersto the one which balances the increase of revenue 119878

119866(120582)

and rise of cost 119878119862(120582) It is also interesting to note from

Proposition 2(b) that 120582lowastSFM is independent of 120573 and 120590 Thisfinding can be explained by the fact that the variance of profit119881[120587(120582)] is a constant which is independent of the service level120582Thus the corresponding optimal service level is not relatedto 120590 as well as the expected profit target 120573 and will in factbehave like the risk neutral case as shown in Proposition 3below

Proposition 3 120582lowast119864119875

uniquely exists and 120582lowastSFM = 120582lowast

119864119875

Proposition 3 shows a rather surprising result that the riskaverse and risk neutral OSPs will have the same optimal ser-vice levels for this particular case In other words irrespectiveof the risk preference the optimal service level is the sameThis finding is important because it shows the fundamentaldifference between different kinds of operations manage-ment problems For example for inventory planning underuncertainty the optimal inventory decision for the risk averseand risk neutral cases will be different However in serviceoptimization the situation can be different as shown here

6 Concluding Remarks

We conclude by discussing some research implicationsderived from the discussions above First the empiricalfindings suggest that in order to enhance customer loyaltyonline stores should develop marketing strategies to enhanceldquofulfillment and responsivenessrdquo since they strongly affectcustomer loyalty httptaobaocom has devoted valuablecorporate resources to it andwe argue that httptaobaocomcan further improve the functions of Aliwangwang toachieve even better communication between the sellersand httptaobaocom users Some probable enhancementsincluding inserting flash games and converting to smart-phone apps can all help to attract the httptaobaocom usersto use Aliwangwang Furthermore httptaobaocom canmake use of Aliwangwang to obtain personalized data relatedto the customersrsquo preferences As a result a higher degreeof web site customization (eg based on past purchases andother customersrsquo information collected) can be provided toindividual customers

Second for the empirical result driven analytical studieswe find that the optimal service level on ldquofulfillment andresponsivenessrdquo function for the risk averse OSP uniquelyexists and can be expressed as the service level which balancesthe increase of revenue 119878

119866(120582) and the rise of cost 119878

119862(120582) We

further show that if the customerrsquos loyalty is more positivelycorrelated to the service level 120582 it will lead to a larger optimalservice level Moreover the optimal service level under thesafety first objective is independent of profit target 120573 and

uncertainty threshold120590 Finally we analytically prove that theoptimal service level does not depend on the risk preferenceof the OSP

For future research there are two directions Empiricallyit is useful to examine more functions of OSP such ashttptaobaocom This paper only focuses on the severalcore functions specifically adopted by httptaobaocom andthere are some more which deserve further explorationsBesides unlike the traditional bricks-and-mortar and elec-tronic retailers the operation of httptaobaocom involvesthousands of independent sellers As a result the customersrsquoperceived service quality of the platform would be consid-erably affected by their buying experience with individualsellers In the future it is worth investigating themodificationof the existing service quality-scale measurement to cater forthis specific type of newbusinessmodelThe second directionfor further studies relates to the theoretical analysis basedon more comprehensive economics analytical models Webelieve that the findings revealed by this paper can lay thefoundation for many further empirical and analytical studies

Appendix

All Proofs of Propositions

Proof of Proposition 1 From (4) we have

SFM (120582) =(119864 [120587 (120582)] minus 120573)

radic119881 [120587 (120582)] (A1)

Since 119881[120587(120582)] = 1205902 (A2) becomes

SFM (120582) =(119864 [120587 (120582)] minus 120573)

120590 (A2)

119889SFM (120582)

119889120582=119889119864 [120587 (120582)] 119889120582

120590

=119889 [119866 (119886 + 119887120582) minus 119862 (120582)] 119889120582

120590

(A3)

1198892SFM (120582)

1198891205822=1

120590(1198892

119866 (119886 + 119887120582)

1198891205822minus1198892

119862 (120582)

1198891205822) (A4)

Since 119866(119886 + 119887120582) is concave and 119862(120582) is convex1198892SFM(120582)119889120582

2

lt 0 which means SFM(120582) is concave and120582lowast

SFM uniquely exists In addition notice that 120582lowast

SFM =

arg120582[119889SFM(120582)119889120582 = 0] Thus we have 120582lowastSFM = arg

120582119878119866(120582)

119878119862(120582) = 1

Proof of Proposition 2 Part (a) notice that 120582lowast

SFM =

arg120582119878119866(120582)119878119862(120582) = 1 and

119878119866(120582) =

119889 [119866 (119886 + 119887120582)]

119889120582

=119889 (119886 + 119887120582)

119889120582

119889119866 (119886 + 119887120582)

119889 (119886 + 119887120582)= 119887

119889119866 (119886 + 119887120582)

119889 (119886 + 119887120582)

(A5)

Thus 120582lowastSFM = arg120582119878119866(120582)119878119862(120582) = 1 = arg119878

119862(120582) = 119887(119889119866(119886+

119887120582)119889(119886 + 119887120582))

8 Mathematical Problems in Engineering

Define 119891(120582) = 119878119862(120582)(119889119866(119897)119889119897)|

119897=119886+119887120582 Consider

1198911015840

(120582) =1

( (119889119866 (119897) 119889119897)|119897=119886+119887120582

)2

times ((119889119866 (119897)

119889119897

10038161003816100381610038161003816100381610038161003816119897=119886+119887120582)119889119878119862(120582)

119889120582

minus119878119862(120582)

119889

119889120582(119889119866 (119897)

119889119897

10038161003816100381610038161003816100381610038161003816119897=119886+119887120582))

(A6)

We have

(a) 119889119878119862(120582)119889120582 = 119889

2

119862(120582)1198891205822

gt 0 [∵ 119862(120582) is convex in120582]

(b) (119889119866(119897)119889119897)|119897=119886+119887120582

gt 0 [∵ 119866(119897) is increasing in 119897](c) 119878119862(120582) = 119889119862(120582)119889120582 gt 0 [∵ 119862(120582) is increasing in 120582]

(d) (119889119889120582)((119889119866(119897)119889119897)|119897=119886+119887120582

) = [1198892

119866(119897)1198891198972

sdot 119889(119897 = 119886 +

119887120582)119889120582]119897=119886+119887120582

= 119887(1198892

119866(119897)1198891198972

)119897=119886+119887120582

lt 0 [∵ 119866(119897) isconcave in 119897]

Therefore we have 1198911015840(120582) gt 0 for all 120582 In other words 119891(120582) isincreasing in 120582 Therefore a larger 119887 implies a larger120582lowastSFM

Part (b) since both 119878119866(120582) and 119878

119862(120582) are independent of 120573

and 120590 it is obvious that 120582lowastSFM = arg120582119878119866(120582)119878119862(120582) = 1 is also

independent of 120573 and 120590

Proof of Proposition 3 First notice that 119864[120587(120582)] is a concavefunction Thus 120582lowastEP uniquely exists Second from (A3) wehave

119889SFM (120582)

119889120582=119889119864 [120587 (120582)] 119889120582

120590=1

120590sdot119889119864 [120587 (120582)]

119889120582 (A7)

Obviously from (A7) we have 120582lowastSFM = arg120582[119889SFM(120582)119889120582 =

0] = arg120582[119889119864[120587(120582)]119889120582 = 0] which is the same as 120582lowastEP Thus

120582lowast

SFM = 120582lowast

EP

Authorsrsquo Contribution

All authors have good contribution to the paper and thelisting follows their surnames

Acknowledgments

The authors thank the editor and the two reviewers for theirkind comments on this paperThey declare that none of themis related to the target case company ldquohttptaobaocomrdquo andother companies named in the paper which may constitute aconflict of interest This paper is partially supported by theRGC(HK)-GRF under grant ac of PolyU 542010H

References

[1] Z Yang ldquoCustomer perceptions of service quality in internet-based electronic commercerdquo inProceedings of the 30th EuropeanMarketing Academy Conference (EMAC rsquo01) pp 8ndash11 BergenNorway 2001

[2] V A Zeithaml ldquoService excellent in electronic channelsrdquoMan-aging Service Quality vol 12 no 3 pp 135ndash139 2002

[3] Parasuraman A L Berry and V A Zeithaml ldquoSERVQUALa multiple-item scale for measuring consumer perceptions ofservice qualityrdquo Journal of Retailing vol 64 no 1 pp 12ndash401988

[4] A Parasuraman and D Grewal ldquoThe impact of technology onthe quality-value-loyalty chain a research agendardquo Journal ofthe Academy of Marketing Science vol 28 no 1 pp 168ndash1742000

[5] A C R Van Riel V Liljander and P Jurriens ldquoExploringconsumer evaluations of e-services a portal siterdquo InternationalJournal of Service Industry Management vol 12 no 4 pp 359ndash377 2001

[6] S Devaraj M Fan and R Kohli ldquoAntecedents of B2C channelsatisfaction and preference validating e-commerce metricsrdquoInformation Systems Research vol 13 no 3 pp 316ndash333 2002

[7] J Kim and J Lee ldquoCritical design factors for successful e-commerce systemsrdquoBehaviour and Information Technology vol21 no 3 pp 185ndash199 2002

[8] S Negash T Ryan and M Igbaria ldquoQuality and effectivenessin Web-based customer support systemsrdquo Information andManagement vol 40 no 8 pp 757ndash768 2003

[9] Y-F Kuo ldquoA study on service quality of virtual communitywebsitesrdquo Total Quality Management and Business Excellencevol 14 no 4 pp 461ndash473 2003

[10] F X Zhu W Wymer and I Chen ldquoIT-based services and ser-vice quality in consumer bankingrdquo International Journal of Ser-vice Industry Management vol 13 no 1 pp 69ndash90 2002

[11] J Santos ldquoE-service quality a model of virtual service qualitydimensionsrdquo Managing Service Quality vol 13 no 3 pp 233ndash246 2003

[12] F Buttle ldquoSERVQUAL review critique research agendardquo Euro-pean Journal of Marketing vol 30 no 1 pp 8ndash32 1996

[13] H Stewart C Hope and A Muhlemann ldquoProfessional servicequality a step beyond other servicesrdquo Journal of Retailing andConsumer Services vol 5 no 4 pp 209ndash222 1998

[14] J J Cronin and S A Taylor ldquoMeasuring service quality a re-examination and extensionrdquo Journal of Marketing vol 56 no 3pp 55ndash68 1992

[15] A Parasuraman L L Berry and V A Zeithaml ldquoMore onimproving service quality measurementrdquo Journal of Retailingvol 69 no 1 pp 140ndash147 1993

[16] A Parasuraman V A Zeithaml and A Malhotra ldquoE-S-QUALa multiple-item scale for assessing electronic service qualityrdquoJournal of Service Research vol 7 no 3 pp 213ndash233 2005

[17] S Akinci E Atilgan-Inan and S Aksoy ldquoRe-assessment of E-S-Qual and E-RecS-Qual in a pure service settingrdquo Journal ofBusiness Research vol 63 no 3 pp 232ndash240 2010

[18] J R Rossitier ldquoER-SERVCOMPSQUAL a measure of e-retail-ing service components qualityrdquo Service Science vol 1 no 4 pp212ndash2224 2009

[19] D X Ding P J-H Hu and O R L Sheng ldquoE-SELFQUALa scale for measuring online self-service qualityrdquo Journal ofBusiness Research vol 64 no 5 pp 508ndash515 2011

[20] S Ha and L Stoel ldquoOnline apparel retailing roles of e-shoppingquality and experiential e-shopping motivesrdquo Journal of ServiceManagement vol 23 no 2 pp 197ndash215 2012

[21] Y Hu and Q Qiang ldquoAn equilibriummodel of online shoppingsupply chain networks with service capacity investmentrdquo Ser-vice Science 2013

[22] R T Rust and A J Zahorik ldquoCustomer satisfaction customerretention and market sharerdquo Journal of Retailing vol 69 no 2pp 193ndash215 1993

Mathematical Problems in Engineering 9

[23] L Groslashnholdt A Martensen and K Kristensen ldquoThe relation-ship between customer satisfaction and loyalty cross-industrydifferencesrdquo Total Quality Management vol 11 no 4ndash6 ppS509ndashS514 2000

[24] T Xiao T M Choi D Yang and T C E Cheng ldquoService com-mitment strategy and pricing decisions in retail supply chainswith risk-averse playersrdquo Service Science vol 4 no 3 pp 236ndash252 2012

[25] T M Choi ldquoOptimal return service charging policy for afashionmass customization programrdquo Service Science vol 5 no1 pp 56ndash68 2013

[26] B Kwok The service quality of the features of Taobaocom andtheir impacts on customer loyalty [BA FYP thesis] The HongKong Polytechnic University 2011

[27] J P Robinson P R Shaver and L S Wrightsman Measures ofPersonality and Social Psychological Attitudes Academic PressSan Diego Calif USA 1991

[28] B Edvardsson M D Johnson A Gustafsson and T StrandvikldquoThe effects of satisfaction and loyalty on profits and growthproducts versus servicesrdquoTotal QualityManagement vol 11 no7 pp S917ndashS927 2000

[29] C-HChiu T-MChoi andD Li ldquoPricewall orwar the pricingstrategies for retailersrdquo IEEE Transactions on Systems Man andCybernetics A vol 39 no 2 pp 331ndash343 2009

[30] T Xiao and T-M Choi ldquoPurchasing choices and channelstructure strategies for a two-echelon system with risk-averseplayersrdquo International Journal of Production Economics vol 120no 1 pp 54ndash65 2009

[31] C-H Chiu and T-M Choi ldquoOptimal pricing and stockingdecisions for newsvendor problem with value-at-risk consider-ationrdquo IEEE Transactions on Systems Man and Cybernetics Avol 40 no 5 pp 1116ndash1119 2010

[32] C-H Chiu T-M Choi and X Li ldquoSupply chain coordinationwith risk sensitive retailer under target sales rebaterdquo Automat-ica vol 47 no 8 pp 1617ndash1625 2011

[33] T-M Choi and C-H Chiu ldquoMean-downside-risk and mean-variance newsvendor models implications for sustainable fash-ion retailingrdquo International Journal of Production Economicsvol 135 no 2 pp 552ndash560 2012

[34] T M Choi P S Chow and T Xiao ldquoElectronic price-testingscheme for fashion retailing with information updatingrdquo Inter-national Journal of Production Economics vol 140 pp 396ndash4062012

[35] TMChoi ldquoMulti-period riskminimization purchasingmodelsfor fashion products with interest rate budget and profit targetconsiderationsrdquo Annals of Operations Research 2013

[36] B Shen TM Choi YWang andC K Y Lo ldquoThe coordinationof fashion supply chains with a risk averse supplier by themarkdown money policyrdquo IEEE Transactions on Systems Manand CyberneticsmdashSystems vol 43 pp 266ndash276 2013

[37] A D Roy ldquoSafety first and the holding of assetsrdquo Econometricavol 20 no 3 pp 431ndash449 1952

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical Problems in Engineering

Hindawi Publishing Corporationhttpwwwhindawicom

Differential EquationsInternational Journal of

Volume 2014

Applied MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical PhysicsAdvances in

Complex AnalysisJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

OptimizationJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Operations ResearchAdvances in

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Function Spaces

Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of Mathematics and Mathematical Sciences

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Algebra

Discrete Dynamics in Nature and Society

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Decision SciencesAdvances in

Discrete MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Stochastic AnalysisInternational Journal of

8 Mathematical Problems in Engineering

Define 119891(120582) = 119878119862(120582)(119889119866(119897)119889119897)|

119897=119886+119887120582 Consider

1198911015840

(120582) =1

( (119889119866 (119897) 119889119897)|119897=119886+119887120582

)2

times ((119889119866 (119897)

119889119897

10038161003816100381610038161003816100381610038161003816119897=119886+119887120582)119889119878119862(120582)

119889120582

minus119878119862(120582)

119889

119889120582(119889119866 (119897)

119889119897

10038161003816100381610038161003816100381610038161003816119897=119886+119887120582))

(A6)

We have

(a) 119889119878119862(120582)119889120582 = 119889

2

119862(120582)1198891205822

gt 0 [∵ 119862(120582) is convex in120582]

(b) (119889119866(119897)119889119897)|119897=119886+119887120582

gt 0 [∵ 119866(119897) is increasing in 119897](c) 119878119862(120582) = 119889119862(120582)119889120582 gt 0 [∵ 119862(120582) is increasing in 120582]

(d) (119889119889120582)((119889119866(119897)119889119897)|119897=119886+119887120582

) = [1198892

119866(119897)1198891198972

sdot 119889(119897 = 119886 +

119887120582)119889120582]119897=119886+119887120582

= 119887(1198892

119866(119897)1198891198972

)119897=119886+119887120582

lt 0 [∵ 119866(119897) isconcave in 119897]

Therefore we have 1198911015840(120582) gt 0 for all 120582 In other words 119891(120582) isincreasing in 120582 Therefore a larger 119887 implies a larger120582lowastSFM

Part (b) since both 119878119866(120582) and 119878

119862(120582) are independent of 120573

and 120590 it is obvious that 120582lowastSFM = arg120582119878119866(120582)119878119862(120582) = 1 is also

independent of 120573 and 120590

Proof of Proposition 3 First notice that 119864[120587(120582)] is a concavefunction Thus 120582lowastEP uniquely exists Second from (A3) wehave

119889SFM (120582)

119889120582=119889119864 [120587 (120582)] 119889120582

120590=1

120590sdot119889119864 [120587 (120582)]

119889120582 (A7)

Obviously from (A7) we have 120582lowastSFM = arg120582[119889SFM(120582)119889120582 =

0] = arg120582[119889119864[120587(120582)]119889120582 = 0] which is the same as 120582lowastEP Thus

120582lowast

SFM = 120582lowast

EP

Authorsrsquo Contribution

All authors have good contribution to the paper and thelisting follows their surnames

Acknowledgments

The authors thank the editor and the two reviewers for theirkind comments on this paperThey declare that none of themis related to the target case company ldquohttptaobaocomrdquo andother companies named in the paper which may constitute aconflict of interest This paper is partially supported by theRGC(HK)-GRF under grant ac of PolyU 542010H

References

[1] Z Yang ldquoCustomer perceptions of service quality in internet-based electronic commercerdquo inProceedings of the 30th EuropeanMarketing Academy Conference (EMAC rsquo01) pp 8ndash11 BergenNorway 2001

[2] V A Zeithaml ldquoService excellent in electronic channelsrdquoMan-aging Service Quality vol 12 no 3 pp 135ndash139 2002

[3] Parasuraman A L Berry and V A Zeithaml ldquoSERVQUALa multiple-item scale for measuring consumer perceptions ofservice qualityrdquo Journal of Retailing vol 64 no 1 pp 12ndash401988

[4] A Parasuraman and D Grewal ldquoThe impact of technology onthe quality-value-loyalty chain a research agendardquo Journal ofthe Academy of Marketing Science vol 28 no 1 pp 168ndash1742000

[5] A C R Van Riel V Liljander and P Jurriens ldquoExploringconsumer evaluations of e-services a portal siterdquo InternationalJournal of Service Industry Management vol 12 no 4 pp 359ndash377 2001

[6] S Devaraj M Fan and R Kohli ldquoAntecedents of B2C channelsatisfaction and preference validating e-commerce metricsrdquoInformation Systems Research vol 13 no 3 pp 316ndash333 2002

[7] J Kim and J Lee ldquoCritical design factors for successful e-commerce systemsrdquoBehaviour and Information Technology vol21 no 3 pp 185ndash199 2002

[8] S Negash T Ryan and M Igbaria ldquoQuality and effectivenessin Web-based customer support systemsrdquo Information andManagement vol 40 no 8 pp 757ndash768 2003

[9] Y-F Kuo ldquoA study on service quality of virtual communitywebsitesrdquo Total Quality Management and Business Excellencevol 14 no 4 pp 461ndash473 2003

[10] F X Zhu W Wymer and I Chen ldquoIT-based services and ser-vice quality in consumer bankingrdquo International Journal of Ser-vice Industry Management vol 13 no 1 pp 69ndash90 2002

[11] J Santos ldquoE-service quality a model of virtual service qualitydimensionsrdquo Managing Service Quality vol 13 no 3 pp 233ndash246 2003

[12] F Buttle ldquoSERVQUAL review critique research agendardquo Euro-pean Journal of Marketing vol 30 no 1 pp 8ndash32 1996

[13] H Stewart C Hope and A Muhlemann ldquoProfessional servicequality a step beyond other servicesrdquo Journal of Retailing andConsumer Services vol 5 no 4 pp 209ndash222 1998

[14] J J Cronin and S A Taylor ldquoMeasuring service quality a re-examination and extensionrdquo Journal of Marketing vol 56 no 3pp 55ndash68 1992

[15] A Parasuraman L L Berry and V A Zeithaml ldquoMore onimproving service quality measurementrdquo Journal of Retailingvol 69 no 1 pp 140ndash147 1993

[16] A Parasuraman V A Zeithaml and A Malhotra ldquoE-S-QUALa multiple-item scale for assessing electronic service qualityrdquoJournal of Service Research vol 7 no 3 pp 213ndash233 2005

[17] S Akinci E Atilgan-Inan and S Aksoy ldquoRe-assessment of E-S-Qual and E-RecS-Qual in a pure service settingrdquo Journal ofBusiness Research vol 63 no 3 pp 232ndash240 2010

[18] J R Rossitier ldquoER-SERVCOMPSQUAL a measure of e-retail-ing service components qualityrdquo Service Science vol 1 no 4 pp212ndash2224 2009

[19] D X Ding P J-H Hu and O R L Sheng ldquoE-SELFQUALa scale for measuring online self-service qualityrdquo Journal ofBusiness Research vol 64 no 5 pp 508ndash515 2011

[20] S Ha and L Stoel ldquoOnline apparel retailing roles of e-shoppingquality and experiential e-shopping motivesrdquo Journal of ServiceManagement vol 23 no 2 pp 197ndash215 2012

[21] Y Hu and Q Qiang ldquoAn equilibriummodel of online shoppingsupply chain networks with service capacity investmentrdquo Ser-vice Science 2013

[22] R T Rust and A J Zahorik ldquoCustomer satisfaction customerretention and market sharerdquo Journal of Retailing vol 69 no 2pp 193ndash215 1993

Mathematical Problems in Engineering 9

[23] L Groslashnholdt A Martensen and K Kristensen ldquoThe relation-ship between customer satisfaction and loyalty cross-industrydifferencesrdquo Total Quality Management vol 11 no 4ndash6 ppS509ndashS514 2000

[24] T Xiao T M Choi D Yang and T C E Cheng ldquoService com-mitment strategy and pricing decisions in retail supply chainswith risk-averse playersrdquo Service Science vol 4 no 3 pp 236ndash252 2012

[25] T M Choi ldquoOptimal return service charging policy for afashionmass customization programrdquo Service Science vol 5 no1 pp 56ndash68 2013

[26] B Kwok The service quality of the features of Taobaocom andtheir impacts on customer loyalty [BA FYP thesis] The HongKong Polytechnic University 2011

[27] J P Robinson P R Shaver and L S Wrightsman Measures ofPersonality and Social Psychological Attitudes Academic PressSan Diego Calif USA 1991

[28] B Edvardsson M D Johnson A Gustafsson and T StrandvikldquoThe effects of satisfaction and loyalty on profits and growthproducts versus servicesrdquoTotal QualityManagement vol 11 no7 pp S917ndashS927 2000

[29] C-HChiu T-MChoi andD Li ldquoPricewall orwar the pricingstrategies for retailersrdquo IEEE Transactions on Systems Man andCybernetics A vol 39 no 2 pp 331ndash343 2009

[30] T Xiao and T-M Choi ldquoPurchasing choices and channelstructure strategies for a two-echelon system with risk-averseplayersrdquo International Journal of Production Economics vol 120no 1 pp 54ndash65 2009

[31] C-H Chiu and T-M Choi ldquoOptimal pricing and stockingdecisions for newsvendor problem with value-at-risk consider-ationrdquo IEEE Transactions on Systems Man and Cybernetics Avol 40 no 5 pp 1116ndash1119 2010

[32] C-H Chiu T-M Choi and X Li ldquoSupply chain coordinationwith risk sensitive retailer under target sales rebaterdquo Automat-ica vol 47 no 8 pp 1617ndash1625 2011

[33] T-M Choi and C-H Chiu ldquoMean-downside-risk and mean-variance newsvendor models implications for sustainable fash-ion retailingrdquo International Journal of Production Economicsvol 135 no 2 pp 552ndash560 2012

[34] T M Choi P S Chow and T Xiao ldquoElectronic price-testingscheme for fashion retailing with information updatingrdquo Inter-national Journal of Production Economics vol 140 pp 396ndash4062012

[35] TMChoi ldquoMulti-period riskminimization purchasingmodelsfor fashion products with interest rate budget and profit targetconsiderationsrdquo Annals of Operations Research 2013

[36] B Shen TM Choi YWang andC K Y Lo ldquoThe coordinationof fashion supply chains with a risk averse supplier by themarkdown money policyrdquo IEEE Transactions on Systems Manand CyberneticsmdashSystems vol 43 pp 266ndash276 2013

[37] A D Roy ldquoSafety first and the holding of assetsrdquo Econometricavol 20 no 3 pp 431ndash449 1952

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical Problems in Engineering

Hindawi Publishing Corporationhttpwwwhindawicom

Differential EquationsInternational Journal of

Volume 2014

Applied MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical PhysicsAdvances in

Complex AnalysisJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

OptimizationJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Operations ResearchAdvances in

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Function Spaces

Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of Mathematics and Mathematical Sciences

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Algebra

Discrete Dynamics in Nature and Society

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Decision SciencesAdvances in

Discrete MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Stochastic AnalysisInternational Journal of

Mathematical Problems in Engineering 9

[23] L Groslashnholdt A Martensen and K Kristensen ldquoThe relation-ship between customer satisfaction and loyalty cross-industrydifferencesrdquo Total Quality Management vol 11 no 4ndash6 ppS509ndashS514 2000

[24] T Xiao T M Choi D Yang and T C E Cheng ldquoService com-mitment strategy and pricing decisions in retail supply chainswith risk-averse playersrdquo Service Science vol 4 no 3 pp 236ndash252 2012

[25] T M Choi ldquoOptimal return service charging policy for afashionmass customization programrdquo Service Science vol 5 no1 pp 56ndash68 2013

[26] B Kwok The service quality of the features of Taobaocom andtheir impacts on customer loyalty [BA FYP thesis] The HongKong Polytechnic University 2011

[27] J P Robinson P R Shaver and L S Wrightsman Measures ofPersonality and Social Psychological Attitudes Academic PressSan Diego Calif USA 1991

[28] B Edvardsson M D Johnson A Gustafsson and T StrandvikldquoThe effects of satisfaction and loyalty on profits and growthproducts versus servicesrdquoTotal QualityManagement vol 11 no7 pp S917ndashS927 2000

[29] C-HChiu T-MChoi andD Li ldquoPricewall orwar the pricingstrategies for retailersrdquo IEEE Transactions on Systems Man andCybernetics A vol 39 no 2 pp 331ndash343 2009

[30] T Xiao and T-M Choi ldquoPurchasing choices and channelstructure strategies for a two-echelon system with risk-averseplayersrdquo International Journal of Production Economics vol 120no 1 pp 54ndash65 2009

[31] C-H Chiu and T-M Choi ldquoOptimal pricing and stockingdecisions for newsvendor problem with value-at-risk consider-ationrdquo IEEE Transactions on Systems Man and Cybernetics Avol 40 no 5 pp 1116ndash1119 2010

[32] C-H Chiu T-M Choi and X Li ldquoSupply chain coordinationwith risk sensitive retailer under target sales rebaterdquo Automat-ica vol 47 no 8 pp 1617ndash1625 2011

[33] T-M Choi and C-H Chiu ldquoMean-downside-risk and mean-variance newsvendor models implications for sustainable fash-ion retailingrdquo International Journal of Production Economicsvol 135 no 2 pp 552ndash560 2012

[34] T M Choi P S Chow and T Xiao ldquoElectronic price-testingscheme for fashion retailing with information updatingrdquo Inter-national Journal of Production Economics vol 140 pp 396ndash4062012

[35] TMChoi ldquoMulti-period riskminimization purchasingmodelsfor fashion products with interest rate budget and profit targetconsiderationsrdquo Annals of Operations Research 2013

[36] B Shen TM Choi YWang andC K Y Lo ldquoThe coordinationof fashion supply chains with a risk averse supplier by themarkdown money policyrdquo IEEE Transactions on Systems Manand CyberneticsmdashSystems vol 43 pp 266ndash276 2013

[37] A D Roy ldquoSafety first and the holding of assetsrdquo Econometricavol 20 no 3 pp 431ndash449 1952

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical Problems in Engineering

Hindawi Publishing Corporationhttpwwwhindawicom

Differential EquationsInternational Journal of

Volume 2014

Applied MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical PhysicsAdvances in

Complex AnalysisJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

OptimizationJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Operations ResearchAdvances in

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Function Spaces

Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of Mathematics and Mathematical Sciences

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Algebra

Discrete Dynamics in Nature and Society

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Decision SciencesAdvances in

Discrete MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Stochastic AnalysisInternational Journal of

Submit your manuscripts athttpwwwhindawicom

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical Problems in Engineering

Hindawi Publishing Corporationhttpwwwhindawicom

Differential EquationsInternational Journal of

Volume 2014

Applied MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Probability and StatisticsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Mathematical PhysicsAdvances in

Complex AnalysisJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

OptimizationJournal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

CombinatoricsHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Operations ResearchAdvances in

Journal of

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Function Spaces

Abstract and Applied AnalysisHindawi Publishing Corporationhttpwwwhindawicom Volume 2014

International Journal of Mathematics and Mathematical Sciences

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

The Scientific World JournalHindawi Publishing Corporation httpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Algebra

Discrete Dynamics in Nature and Society

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Decision SciencesAdvances in

Discrete MathematicsJournal of

Hindawi Publishing Corporationhttpwwwhindawicom

Volume 2014 Hindawi Publishing Corporationhttpwwwhindawicom Volume 2014

Stochastic AnalysisInternational Journal of