Performance Impact of E-Business Initiatives on the US Retail Industry. Yao Chen, Luvai Motiwalla and M. Riaz Khan

Transcription

1 Perfrmace Impact f E-Busiess Iitiatives the US Retail Idustr Ya Che, Luvai Mtiwalla ad M. Riaz Kha Cllege f Maagemet Uiversit f Massachusetts Lwell, MA 0845 USA Ya_Che@uml.edu Jauar 2003

2 Perfrmace Impact f E-Busiess Iitiatives the US Retail Idustr Ya Che, Luvai Mtiwalla ad M. Riaz Kha Abstract: Electric Busiess (EB) the Iteret is a attractive techlg fr traditial busiess rgaizatis t imprve their fiacial perfrmace. The Web-based ecmic mdel is suppsedl mre efficiet at the trasacti cst level. It als prvides cst-effective marketig, glbal market access, ad a disitermediati f cstl distributi chaels. The curret paper uses super-efficiec data evelpmet aalsis (DEA) mdel t evaluate the fiacial perfrmace f e-busiess iitiatives i the retail idustr. Because f the pssible ifeasibilit f the super-efficiec DEA mdel, the use f super-efficiec DEA mdel has bee restricted. The paper demstrates that if superefficiec is iterpreted as iput savig r utput surplus achieved b a specific efficiet, ifeasibilit des t ecessar mea the best perfrmace. A ew apprach is prpsed t crrectl characterize the perfrmace f busiess firms. Usig the ewl prpsed super-efficiec DEA apprach, we are able t full rak the fiacial perfrmace f a set f EB cmpaies ad -EB cmpaies frm the retail idustr. This aalsis idicates that the EB cmpaies did perfrm better i sme areas tha their -EB cuter part. Ke wrds: Data evelpmet aalsis (DEA); super-efficiec; IT ivestmet, electric busiess

3 . Itrducti As pited ut b Bigi et al. (2000), the Iteret has eperieced a phemeal grwth i attractig peple ad cmmerce activities ver the last decade -- frm a few thusad peple i 993, t 50+ milli i 999 the Web is epected t attract ver e billi b This grwth has attracted a variet f rgaizatis iitiall t prvide marketig ifrmati abut their prducts ad services, custmer supprt, ad later t cduct busiess trasactis with custmers r busiess parters ver the Web. These EB iitiatives culd be implemetati f Itraet ad/r Etraet applicatis like B2C, B2B, Web-CRM, Web-Marketig, ad thers t take advatage f the Web-based ecmic mdel which ffers pprtuities fr iteral efficiecies ad eteral grwth. Wigad ad Beami (995) fid that the Iteret ecmic mdel is mre efficiet at the trasacti cst level. Fr eample, the cst f prcessig a Airlie ticket thrugh traditial apprach is eight dllars, but is l e dllar thrugh the Web. Similarl, ther efficiecies ca be derived frm marketig ad advertisemets, lie ifrmati prcessig with frms that are electricall liked t databases, ad lie custmer supprt (Hffma, et. al., 995). Elimiati f middlema i the distributi chael (r disitermediati) als ca have a big impact the market efficiec (Michalski, 995). Other efficiecies are geerated due t less r ivetr, strage r real-estate space, larger custmer base, ad 247 hur access at additial cst (Steifield ad Whitte, 999). Marketig the Web ca result i additial uit sales at ver lw uit cst. I additi t the lwer cst, the Web als eables higher level f custmizati t the eeds f idividual cliets (Chi ad Wist, 2000). Aut maufacturers, such as Frd ad GM, are eperimetig with custm desiged cars that ca be delivered i less tha tw weeks at custmer s hme (White, 999). Thus, webeablig busiess prcesses is particularl attractive i the ew ecm where prduct life ccles are shrt ad efficiet, while the market fr prducts ad services is glbal. Similarl, maagemet f these cmpaies epects a much better fiacial perfrmace tha their cuterparts i the idustr wh had t adpted these EB iitiatives. EB als allws rgaizatis t epad their busiess reach. Oe f the ke beefits f the Web is access t ad frm glbal markets. The Web elimiates several gegraphical barriers fr crprati that wats t cduct glbal cmmerce. Plitical,

4 trade, ad cultural barriers ma still make it difficult t take true advatage f the glbal busiess evirmet. While traditial cmmerce relied value-added etwrks (VANs) r private etwrks, which were epesive ad prvided limited cectivit (Ple, 996), the Web makes electric cmmerce cheaper with etesive glbal cectivit. Crpratis have bee able t prduce gds awhere ad deliver electricall r phsicall via curiers (Steifield & Whitte, 999). This eables rgaizati the fleibilit t epad it differet prduct lies ad markets quickl, with lw ivestmets. Secdl, 247 availabilit, better cmmuicati with custmers, ad sharig f the rgaizatial kwledgebase allws rgaizati t prvide better custmer service. This ca traslate t better custmer reteti rates as well as repeat rders. Fiall, the rich iteractive media ad database techlg f the Web allws fr ucstraied awareess, visibilit, ad pprtuit fr a rgaizati t prmte its prducts ad services. This ehaces rgaizatis abilit t attract ew custmers, thereb icreasig their verall markets ad prfitabilit. Despite the recet dt-cm failures, EB has made tremedus i-rads i traditial crpratis. Frrester Research i their surve fud 90% f the firms pla t cduct sme e-cmmerce, busiess-tcsumer (B2C) r busiess-t-busiess (B2B), ad predicts EB trasactis t rise t abut $6.9 trilli b As a result, the maagemet f rgaizatis has started t believe i the Iteret because f its abilit t attract ad retai mre custmers, reduce sales ad distributi verheads ad glbal access t markets with a epectati f a icrease i sales reveues, higher prfits ad better returs fr the stckhlders. This paper discusses a EB perfrmace stud which has fcused fiacial perfrmace f a set f publicl-held Crpratis frm the retail idustr, betwee the ears We use DEA (data evelpmet aalsis) t aalze the fiacial perfrmace f cmpaies i retail ad distributi busiess that had implemeted EB iitiatives, sice 996. The cmpaies i this aalsis were t the pure click eterprises, like Amaz.cm, e-ba, r Yah because, we were iterested the fiacial impact f EB cmpaies. Specificall, the bective f this stud was t determie whether the fiacial data supprt the beeficial claims made i the ppular literature that EB has bsted the bttm-lie. Results idicated that the EB cmpaies perfrmed better i sme measures tha the -EB cmpaies i ur sample. 2

5 2. DEA ad Super-Efficiec Data evelpmet aalsis (DEA) is rigiall develped t measure the relative efficiec f peer decisi makig uits (s) i multiple iput-multiple utput settigs (Chares et al. 978). DEA idetifies a efficiet frtier where all s have a uit scre. I rder t discrimiate the perfrmace amg efficiet s, based up the CCR mdel, a super-efficiec DEA mdel i which a uder evaluati is ecluded frm the referece set is first develped b Baker ad Giffrd (988) ad Baker et al. (989) (see als Aderse ad Peterse, 993). This super-efficiec DEA mdel is develped uder (i) the DEA frtier ehibits cstat returs t scale (CRS) ad (ii) all iputs (r utputs) are simultaeusl chaged i the same prprti. Whe either f the cditis is t satisfied, ifeasibilit f the related liear prgram is ver likel t ccur (see, e.g., Seifrd ad Zhu 998a; 998b). As a result, we d t have a value assciated with ifeasibilit t represet the super-efficiec, ad the use f superefficiec DEA is restricted. Baker ad Chag (2000) have demstrated that the use f the super-efficiec mdel fr rakig efficiet s is iapprpriate. Hwever, i additi t rakig, super-efficiec ccept has bee used i ther situatis. Fr eample, DEA sesitivit aalsis (Chares et al. 992, Zhu 996), tw-pers rati efficiec games (Russeau ad Semple 995), detectig ifluetial bservatis (Baker et al. 989), ad acceptace decisi rules (Seifrd ad Zhu 998c), amg thers. Therefre, a stud ifeasibilit f super-efficiec DEA mdel is a wrthwhile bective. While the ecessar ad sufficiet cditis fr ifeasibilit i varius superefficiec DEA mdels are develped (Seifrd ad Zhu 999), attempt has bee made t slve the ifeasibilit prblem. This is partl due t the fact that the meaig f superefficiec has differet iterpretatis. I fact, a iput-rieted super-efficiec DEA mdel measures the iput superefficiec whe utputs are fied at their curret levels. A utput-rieted superefficiec DEA mdel measures the utput super-efficiec whe iputs are fied at their curret levels. Frm the differet uses f super-efficiec ccept, we see that superefficiec ca be iterpreted as the degree f efficiec stabilit r iput savig/utput surplus achieved b a efficiet. If super-efficiec is used as a efficiec 3

6 stabilit measure, the based up Seifrd ad Zhu (998b), ifeasibilit meas that a efficiet s efficiec classificati is stable t a iput chages if a iputrieted super-efficiec DEA mdel is used (r a utput chages if a utput-rieted super-efficiec DEA mdel is used). Therefre, we ca use + t represet the superefficiec scre. i.e., ifeasibilit meas the highest super-efficiec. Usig variable returs t scale (VRS) super-efficiec DEA mdel as a eample, the curret stud discusses the situati whe super-efficiec is iterpreted as iput savig r utput surplus. The curret stud shws that if the VRS frtier has icreasig, cstat, ad decreasig returs t scale (IRS, CRS, ad DRS) s, e f the iputrieted ad utput-rieted super-efficiec DEA mdels must be feasible. This implies that (i) if a efficiet des t pssess a iput super-efficiec (iput savig), it must pssess utput super-efficiec (utput surplus), ad (ii) if a efficiet des t pssess a utput super-efficiec, it must pssess iput super-efficiec. We ca use bth iput-rieted ad utput-rieted super-efficiec DEA mdels t full characterize the super-efficiec. 3. Super-efficiec DEA Mdels A DEA mdel evaluates the perfrmace f a set f s, { :, 2,, }, which prduce multiple utputs b utilizig multiple iputs. Each has a set f s utput measures, r, (r, 2,..., s), ad a set f m iput measures, i, (i, 2,..., m). A DEA mdel which ehibits VRS ca be writte as (Baker et al. 984) s. t. miθ i r 0 θ r i i,2,..., m; r,2,..., s;,...,. where, i ad r are respectivel the ith iput ad rth utput fr a uder evaluati. () 4

7 ad Zhu 999) The VRS super-efficiec DEA mdel related t () ca be epressed as (Seifrd s. t. miθ VRS-super i θ VRS super i i,2,..., m; r r r,2,..., s; (2) θ VRS-super 0 0 where the uder evaluati is ecluded frm the referece set. If we drp, we btai a super-efficiec DEA mdel uder CRS. While the CRS super-efficiec DEA mdel is usuall feasible, mdel (2) is t. Nte that mdel (2) is a iput-rieted super-efficiec DEA mdel. We ca als have a utput-rieted versi. s. t. ma φ i VRS super i i,2,..., m; r φ VRS super r r,2,..., s; (3) φ VRS super 0 0 ( ) Zhu (996) idicates that the (iput-rieted) CRS super-efficiec DEA mdel is alwas feasible uless certai patters f zer data etries are preset i the iputs. Therefre, if e assumes all data are psitive, the the (iput-rieted) CRS super-efficiec DEA mdel is alwas feasible. 5

8 6 The fllwig therem idicates a relatiship betwee mdels (2) ad (3) with respect t the ifeasibilit. Therem : If mdel (2) is ifeasible ad is CRS-iefficiet, the mdel (3) must be feasible. [Prf]: Recall that Seifrd ad Zhu (999) shw that whe mdel (2) is ifeasible, the must ehibit CRS r DRS. Nw, if is iefficiet uder the CRS assumpti, it must ehibit DRS. Therefre, r r i CRS i θ (4) where CRS θ < is the CRS efficiec scre ad >, represetig ptimal slutis frm the CRS DEA mdel (Chares et al. 978) 2. Let ', the (4) becmes ' ' ' r r i i CRS i θ (5) 2 Sice is CRS iefficiet, (7) is equivalet t the set f rigial cstraits i the CRS DEA mdel (Chares et al. 978).

9 (5) idicates that the utput-rieted super-efficiec DEA mdel (3) is feasible.ν Similar t Therem, we have Therem 2: If mdel (3) is ifeasible ad must be feasible. is CRS-iefficiet, the mdel (2) Nw, if is als efficiet uder CRS, i.e., Therem 3: Bth mdels (2) ad (3) are ifeasible if ad l if efficiet. ehibits CRS, we have is the l VRS [Prf]: Suppse bth mdels (2) ad (3) are ifeasible. We have that uder the cditi f VRS, has the largest utputs ad smallest iputs. Therefre, all ther s ad their cve cmbiatis. This idicates that dmiates is the l efficiet uder bth VRS ad CRS. Nw, suppse is the l VRS efficiet, the it must als be the l CRS efficiet. Thus, fr each f ther s uder CRS evaluati, we have i < i ( ) ad r < r ( ). Further, whe, we have > i i ad r < r. This implies bth mdels (2) ad (3) are ifeasible. ν Therems, 2 ad 3 shw that e f the iput-rieted ad utput-rieted VRS super-efficiec DEA mdels must be feasible if the VRS frtier ctais IRS, CRS ad DRS s. Nte that Therem 3 describes a ver rare situati which fte des t eist i real wrld data sets. Nw, suppse mdel (2) is feasible whe a efficiet is uder evaluati. The ptimal value f VRS super θ idicates that the iputs f ca be icreased t reach a level that is used b ther s r b the cve cmbiati f ther s. Csider the five s (A, B, C, D, ad H) with e utput ad e iput i Figure. Whe mdel (2) is applied t B, we have VRS super 3 θ B, idicatig that B 2 7

10 3 3 ca icrease its iput t ( 3), the iput level used b B a cve 7 2 cmbiati f A ad C. This pssible iput icrease ca actuall be viewed as a iput savig achieved b B cmpared t the remaiig s. <Isert Figure Abut Here> Whe mdel (2) is applied t D, mdel (2) is ifeasible. Althugh D remais efficiet uder the cditi f VRS whe its iput icreases, this iput icrease des t represet a iput savigs after passig H which uses the iput level f H. Because H s iput level is the largest. Thus, the super-efficiec f D shuld be cmpared t H -- a radial DEA precti. T achieve this, we slve mdel (2) i the fllwig frmat s. t. ~ miθ 0 VRS-super ~ θ ˆ ˆ i r r VRS super i r i,2,..., m; r,2,..., s; (6) where ŷ r φ r ad φ is the ptimal value t the fllwig utput-rieted VRS DEA mdel s. t. φ ma φ i r 0 i φ r i,2,..., m; r,2,..., s; (7) 8

11 Applig mdel (6) is equivalet t applig mdel (2) after all iefficiet s are prected t the VRS frtier via prprtial utput augmetati thrugh mdel (7) 3. ~ super D VRS Applig mdel (6) t D ields θ.. Nte that mdel (6) ma still be ifeasible. Fr eample, if we d t have H i Figure, mdel (6) is ifeasible whe D is uder evaluati. I such cases, we sa that des t idicate super-efficiec i terms f iput savig, sice cat be mved t the frtier cstructed frm the remaiig s via iput icreases, idicatig that has the largest iput levels give the curret utput levels. We therefre dete VRS super θ ~ VRS super θ whe mdel (6) is ifeasible, idicatig zer iput superefficiec which meas zer iput savig fr. Let γ represet the scre fr characterizig the super-efficiec i terms f iput savig, we have γ θ ~ θ VRS super VRS super if mdel (2) isfeasible if mde(2) isifeasible ad mdel (6) is feasible if mdel (6) isifeasible (8) If γ, Nte that γ >. If γ >, a specific efficiet des t have iput super-efficiec. has iput super-efficiec. Whe mdel (6) is ifeasible, the super-efficiec is actuall reflected i s utputs via utput surplus. Suppse agai that H is t icluded i Figure. Figure idicates that the super-efficiec f D is represeted b its utput rather tha its iput. Sice give the curret iput level, D achieves a utput surplus f 0.5 if C uses the iput level f D. Thus, we shuld als use mdel (3) t characterize the super-efficiec via utput super-efficiec sigaled b utput surplus. Csider D i Figure (assume that H is t icluded), we have 0.9. i.e., D s utput super-efficiec scre is 0.9. VRS super θ D 3 Alterative precti the frtier ca be btaied if e has priri ifrmati the iput-utput tradeffs. 9

12 Similar t mdel (6), we ma als adust the iput values i mdel (3) b the iput-rieted VRS DEA mdel () whe mdel (3) is feasible. ~ VRS super ma φ where ˆ i evaluati. θ s. t. i ad i ˆ ˆ r 0 i ~ φ i VRS super ( ) r i,2,..., m; r,2,..., s; θ is the ptimal value t mdel () whe is uder Bth mdels (3) ad (9) are ifeasible whe A is uder evaluati, idicatig that A des t have super-efficiec i its utput. I fact, ther (9) s prduce less utput tha A des. Thus, we dete VRS sup φ φ VRS super ~ er whe mdel (9) is ifeasible, idicatig zer utput super-efficiec which meas zer utput surplus fr. τ φ ~ φ Let τ represet the scre fr characterizig the utput super-efficiec, we have VRS super VRS super if mdel (3) is feasible if mde(3) isifeasible ad mdel (9) is feasible if mdel (9) is ifeasible Nte that τ <. If τ <, a specific efficiet has utput superefficiec. If τ, des t have utput super-efficiec. (0) Frm the abve discussi we see that super-efficiec is represeted b l the iput savig r b l the utput surplus whe ifeasibilit ccurs. Therems, 2, ad 3 prvide a basis fr emplig bth iput-rieted ad utput-rieted VRS superefficiec mdels t full characterize the super-efficiec that are iheret i s iputs r utputs. 0

13 ma select We ma itegrate γ ad τ it e super-efficiec scre. Fr eample, we w γ ad w τ such that w γ + w τ ad defie S wγ γ + wτ τ () r Ŝ wγ + wτ γ τ (2) Obviusl, S > ad Ŝ <. Larger S r smaller Ŝ idicates higher superefficiec perfrmace Fiacial Perfrmace f E-Busiess: Evidece frm the US Retail Idustr While there was sme aecdtal evidece fud i the literature abut icreased fiacial beefits ad retur ivestmet (ROI) eperieced b idividual cmpaies thrugh EB, there is csistet apprach f IT evaluati perfrmace measures. While sme studies have measured IT ifluece at the firm level, thers have measured at the activit r the idustr level. The ctemprar IT evaluati apprach which bega i the 980s (Farbe, Lad & Targett, 999) fcused Retur Ivestmet (ROI) ad Retur Maagemet (ROM). It relied quatitative assessmet f IT csts, beefits, ad risk durig the sstems develpmet life ccle with ver few pst impleme tati evaluati studies. There were iheret prblems t the cst-beefits apprach. Fr eample, hw t quatif future savigs, large cst verrus, ad arbitrar estimates f sstems life, risk, ad iflati all erded the cfidece f eecutives i this apprach. This led t studies that fcused itagible csts-beefits, lg-term ROI studies, ad the issue f prductivit parad ad bed (Brlfss & Hitt, 998) which questied wh IT ivestmet had sigificat impact wrker prductivit. Rai, et al. (997) suggests that while IT imprves rgaizatial efficiec, its ifluece admiistrative prductivit ad busiess perfrmace depeds ther rgaizatial factrs. These 4 Nte that uder the cditi f CRS, we have γ /τ. Thus, S Ŝ γ /τ. i.e., either the iput-rieted CRS super-efficiec DEA mdel r the utput-rieted CRS super-efficiec DEA mdel is sufficiet i rakig the efficiet s.

14 studies idicate that there were a variet f prblems i measurig r evaluatig IT s ifluece fiacial perfrmace f the firm. The difficult f dealig with itagibles, shrt-time frame, arrw fcus, r usig traditial prductivit measuremets has led t ivative research appraches measurig the ifluece f IT ivestmets. I additi, rgaizatis d t keep accurate recrds IT csts-beefits ad i ma cases d t wat t reveal the data eve if the eist fr securit ad cmpetitive reass. As such, ecmic framewrks r mdels were available t measure the impact f EB crprate fiacial perfrmace. Therefre, we utilized the ewl develped super-efficiec DEA mdel t stud the fiacial perfrmace f a set f EB ad -EB firms frm the US retail idustr. I fact, the cvetial DEA mdels have bee widel used i perfrmace evaluati ad bechmarkig (see, e.g., Zhu (2000, 2002)). Als, the impact f EB iitiatives, fr eample, sales reveues, cst f prducig gds r services, glbal sales, ad qualit f services were idetified ad reflected i icme ad ROI. Such ratis measure the fiacial perfrmace f a cmpa ad were iflueced b its busiess strategies. We develp the DEA iputs ad utputs based up the these measures. Specificall, we use fur DEA iputs, amel (i) umber f emplees, (ii) ivetr cst, (iii) ttal curret assets, ad (iv) cst f sales, ad tw DEA utputs, amel (i) reveue ad (ii) et icme. See Mtiwalla ad Kha (2002) fr a detailed discussi these measures. These fiacial measures allw the idetificati f EB impact the iteral peratial efficiec f the firms. A review f these cmpaies prfile revealed that the mst frequetl bserved ear f EB implemetati was either late 995 r earl 996. Thus, the cmpaies that wet lie with their website i were categrized as EB cmpaies, while the cmpaies that registered their dmai i Jue 999 r later were categrized as -EB cmpaies. Table prvides the data which reprts three EB cmpaies ad seve -EB cmpaies. Give the fact that we are lkig at the perfrmace f these cmpaies betwee , we actuall have 40 s i ttal, each cmpa i each represetig a. 2

15 Table 2 reprts the ptimal values t (2) ad (3) i clums 3 ad 4, ad the last clum reprts the DEA efficiec scres btaied frm mdel (). Based up mdel () (last clum f Table 2), 75% f the EB cmpaies are efficiet, ad 57% f the - EB cmpaies are efficiet. Therefre, i verall, the EB cmpaies have a better perfrmace. Net, we tur t mdels (2) ad (3). We have tw ifeasible cases assciate with mdel (2) ad fur ifeasible cases assciated with mdel (3). This idicates that althugh these cmpaies are efficiet, their super-efficiec is zer i terms f iput savig r utput surpluses. Nte that ut f si ifeasibilit cases, l tw f them are frm the EB cmpaies. Based up the develpmet i the previus secti, we shuld use zer as their super-efficiec scres. Furthermre, based up mdel (2), the average (iput) super-efficiec scres fr EB ad -EB cmpaies are.023 ad 0.975, respectivel. Nte that larger values (iput) super-efficiec idicates a better perfrmace. Based up mdel (3), the average (utput) super-efficiec scres fr EB ad -EB cmpaies are ad 0.936, respectivel. Nte that smaller values (iput) super-efficiec idicates a better perfrmace. Therefre, i terms f the super-efficiec DEA mdel, the EB cmpaies have a better perfrmace tha the -EB cmpaies. 5. Cclusis The curret paper discusses the relatiship betwee super-efficiec ad the ifeasibilit f super-efficiec DEA mdel. It is shw that i rder t full characterize the super-efficiec, bth iput-rieted ad utput-rieted super-efficiec DEA mdels are eeded. Althugh the discussi is based up VRS super-efficiec DEA mdels, all the develpmets ca be applied t ther super-efficiec DEA mdels (Seifrd ad Zhu, 999). I particular, the curret stud ca als beefit the calculati f Malmquist prductivit ide (Färe et al., 994). As ted i Che (2000), mst f the applicatis DEA-based Malmquist prductivit aalsis are based up the CRS assumpti. There are a few applicatis f Malmquist prductivit idees usig VRS DEA mdels i literature, but the authrs either prvide the detail cmputati results r meti the ccurrece f ifeasibilit. 3

16 The ew super-efficiec DEA apprach is applied t measure the fiacial perfrmace f e-busiess iitiatives i the US retail idustr. The aalsis f the retail idustr data supprts the view that there was sme evidece that the EB iitiatives have favrable impact the fiacial perfrmace f cmpaies that udertake them. Based the sample data eamied i this stud, ccrete cclusis ca be draw; the prpsed mdel appears ptetiall suitable i measurig the fiacial perfrmace f EB iitiatives at the firm level. Further, b ctrastig the perfrmaces f EB cmpaies agaist -EB, the fidigs ca revalidate that the EB cmpaies beefited frm ivati ad strategic itegrati f EB techlgies. While the curret stud is based up a relativel small data set, it is imprtat t ctiue t ivestigate ur mdel with large scale applicatis. 4

17 Refereces. Aderse, P., N.C. Peterse A prcedure fr rakig efficiet uits i data evelpmet aalsis. Maagemet Sciece Baker, R.D., ad H. Chag Evaluatig the super-efficiec prcedure i data evelpmet aalsis fr utlier idetificati ad fr rakig efficiet uits. Schl f Maagemet, The Uiversit f Teas at Dallas, Richards, TX Baker, R.D. ad J. L. Giffrd A relative efficiec mdel fr the evaluati f public health urse prductivit. Schl f Urba ad Public Affairs, Caregie Mell Uiversit. 4. Baker, R.D, A. Chares, W.W. Cper Sme mdels fr estimatig techical ad scale efficiecies i data evelpmet aalsis. Maagemet Sciece Baker, R.D., S. Das, S.M. Datar Aalsis f cst variaces fr maagemet ctrl i Hspitals. Research i Gvermetal ad Nprfit Accutig Bigi, P., Mir, A., Khamalah, J The Challeges Facig Glbal E- Cmmerce. Ifrmati Sstems Maagemet, Fall, pp Brlfss, E. ad Hitt, L., (998), Bed the Prductivit Parad, Cmmuicatis f the ACM, v36, 2, pp Chares, A, W.W. Cper, E. Rhdes Measurig the efficiec f decisi makig uits. Eurpea Jural f Operatial Research Chares, A, S. Haag, P. Jaska ad J. Semple Sesitivit f efficiec classificatis i the additive mdel f data evelpmet aalsis. It. J. Sstems Sci Che, Ya Prductivit ad Cmparative Aalsis i Service ad Maufacturig Operatis. Ph.D. dissertati, Uiversit f Massachusetts at Amherst.. Chi, S., Wist, A., (2000), Beefits ad Requiremets fr Iterperabilit i Electric Marketplace, Techlg i Sciet, v. 22, pp

18 2. Farbe, B., Lad, F. & Targett, D., (999), mvig IS evaluati frward: learig themes ad research issues, Jural f Strategic Ifrmati Sstems, v 8, pp Färe, R., Grsskpf, S., ad Lvell, C. A. K. (994), Prducti Frtiers, Cambridge Uiversit Press. 4. Hffma, D., Nvak, T., & Chatteree, P., (995), Cmmercial Scearis fr the Web: Opprtuities ad Challeges, Jural f Cmputer-Mediated Cmmuicatis, (3). 5. Michalski, et al., (995), Peple are the Killer APP, Frbes, Jue 5, v. 55, i. 2, pp Mtiwalla, L. ad M.R. Kha (2002), Fiacial impact f e-busiess iitiatives i the retail idustr, Jural f Electric Cmmerce i Orgaizati, (), Rai, A., Pataakui, R., ad Pataakui, N., (997), Techlg Ivestmet ad Busiess Perfrmace, Cmmuicatis f the ACM, v40, 7, pp Russeau, J.J. ad J.H. Semple Tw-pers rati efficiec games. Maagemet Sciece Seifrd, L.M. ad Je Zhu 998a. Stabilit regis fr maitaiig efficiec i data evelpmet aalsis. Eurpea Jural f Operatial Research Seifrd, L.M. ad Je Zhu 998b. Sesitivit aalsis f DEA mdels fr simultaeus chages i all the data. Jural f the Operatial Research Sciet Seifrd, L.M. ad Je Zhu. 998c. A acceptace sstem decisi rule with data evelpmet aalsis. Cmputers Operatis Research Seifrd, L.M. ad Je Zhu 999. Ifeasibilit f super-efficiec data evelpmet aalsis mdels. INFOR 37 (Ma) Steifield, C. ad Whitte, P., (999), Cmmuit Level Sci-Ecmic Impacts Of Electric Cmmerce, Jural Of Cmputer-Mediated Cmmuicatis, 5(2). 6

19 24. White, G., (999), Hw GM, Frd Thik Web ca Make a Splash the Factr Flr, Wall Street Jural, Dec. 3, p. 25. Wigad, R. & Beami, R. (995), Electric cmmerce: Effects electric markets, Jural f Cmputer Mediated Cmmuicati, (3) r [ 26. Zhu, Je Rbustess f the efficiet s i data evelpmet aalsis. Eurpea Jural f Operatial Research Zhu, J. (2000), Multi-factr Perfrmace Measure Mdel with A Applicati t Frtue 500 Cmpaies, Eurpea J. f Operatial Research, Vl. 23, N., Zhu, J. (2002), Quatitative Mdels fr Perfrmace Evaluati ad Bechmarkig: Data Evelpmet Aalsis with Spreadsheets ad DEA Ecel Slver. Kluwer Academic Publishers, Bst 7

20 Table Data EB cmpa RadiShack RadiShack RadiShack RadiShack Wilss Leather Eperts Wilss Leather Eperts Wilss Leather Eperts Wilss Leather Eperts Cld Water Creek Cld Water Creek Cld Water Creek Cld Water Creek N-EB cmpa Abercrmbie & Fitch C. Abercrmbie & Fitch C. Abercrmbie & Fitch C. Abercrmbie & Fitch C. ATalr Stres Crp. ATalr Stres Crp. ATalr Stres Crp. ATalr Stres Crp. Burligt Cat Factr Warehuse Burligt Cat Factr Warehuse Burligt Cat Factr Warehuse Burligt Cat Factr Warehuse Fiish Lie Fiish Lie Fiish Lie Fiish Lie Gap Gap Gap Gap Eagle Fds Eagle Fds Eagle Fds Eagle Fds Alberts's Alberts's Alberts's Alberts's ear Emplees Ivetr Assets Cst f sales Reveue Net icme

21 Table 2 Results Cmpa ear Mdel (2) Mdel (3) Mdel () RadiShack 2000 RadiShack 999 RadiShack 998 RadiShack Wilss Leather Eperts Wilss Leather Eperts Wilss Leather Eperts Wilss Leather Eperts Cld Water Creek Cld Water Creek Cld Water Creek Cld Water Creek Abercrmbie & Fitch C Abercrmbie & Fitch C Abercrmbie & Fitch C Abercrmbie & Fitch C ATalr Stres Crp ATalr Stres Crp ATalr Stres Crp ATalr Stres Crp Burligt Cat Factr Warehuse Burligt Cat Factr Warehuse Burligt Cat Factr Warehuse Burligt Cat Factr Warehuse Fiish Lie Fiish Lie Fiish Lie Fiish Lie Gap Gap Gap Gap Eagle Fds Eagle Fds Eagle Fds Eagle Fds Alberts's Alberts's Alberts's Alberts's

22 6 Output 5 C (5, 4.5) Ifeasibilit D (0,5) H' (,5) 4 B (3,4) B' CRS efficiet H (,4) 3 2 A (,) Iput Figure VRS Super-efficiec 20