Performace Measuremet I The ecommerce Idustry Jamshed Mistry (E-mail: mistry@wpi.edu), Worcester Polytechic Istitute Abstract The Balaced Scorecard framework (BSC) developed by Kapla ad Norto (1992) has bee widely accepted by most academics ad adopted by umerous practitioers i idustry. Despite beig widely accepted by practitioers i idustry, little extat research has focused o the BSC ad little empirical aalysis has focused o validatig the model. This paper first develops performace measures specifically desiged for the ecommerce idustry by drawig o the BSC ad other measures developed by practitioers. Next, the paper reports evidece of the utility of the BSC framework i measurig ad moitorig the performace of e-commerce compaies. The study utilizes a itegrated Data Evelopmet Aalysis (DEA) model to examie ad evaluate the relative efficiecy of the measures idetified withi the BSC framework for measurig the performace of ecommerce compaies. Fially, the study examies the effectiveess of the BSC framework i predictig the success or failure of ecommerce compaies by focusig o three successful ecommerce ad three ecommerce compaies that subsequetly failed. 1. Itroductio I the last decade, maagemet accoutig researchers have become icreasigly iterested i aalyzig the impact of o-fiacial performace measures o the performace of the firm. As competitio i the marketplace has itesified, o-fiacial performace measures have become progressively more importat as ew sources of relevat iformatio (Hemmer, 1996). The eed for plaig, iformatio ad cotrol systems that ca assist maagers i their decisio-makig has also stimulated the eed for ew o-fiacial measures of performace. This eed has focused attetio o developig ew models to assist maagers with their strategic decisio-makig, plaig ad cotrol decisios (Baker ad Johsto, 2000). Oe model that has geerated attetio i the past decade is the Balaced Scorecard model developed by Kapla ad Norto (1992). This framework emphasizes the eed to measure ad moitor the performace of compaies withi the broad framework of both fiacial ad o-fiacial parameters of performace. As a part of the ew age ecoomy, Busiess-to-Cosumer compaies or Dot-coms or e-retailers are amog the ew age compaies that have revolutioized the marketplace. These ew ecoomy compaies appear to defy the basic rules of busiess. The global reach of the Iteret ad the cosequet bargaiig power it has provided the worldwide customer has ivalidated most of the older maagemet practices. Dot-coms or ecommerce compaies have ecessitated the developmet of a whole ew set of performace measuremet parameters for moitorig ad measurig their performace. For example, reach, click through ratio, hits, visits, umber of subscribers, quick loadig time, persoalizatio, umber of affiliates ad avigatio have bee suggested as parameters that idicate the operatioal ad marketig efficiecy of these compaies (Seybold, 2000). Although the Balaced Scorecard model was iitially proposed i 1992, ad the model has bee widely accepted by most practitioers, little empirical aalysis has focused o validatig the model. I this paper, four sets of performace measuremet parameters specifically desiged for ecommerce compaies are developed, drawig o the Balaced Scorecard framework. Data Evelopmet Aalysis (DEA) usig these measures is the employed to examie the efficiecy of Balaced Scorecard parameters i measurig ad moitorig the performace of eightee ecommerce compaies. Fially, six of the eightee compaies are aalyzed to compare the three most successful compaies with three that subsequetly failed i order to examie the effectiveess of the Balaced Scorecard parameters i predictig bakruptcy. 33
2. Backgroud ad Sigificace I their pioeerig research o measurig the performace of orgaizatios, Kapla ad Norto (1992) describe the iovatio of the balaced scorecard as follows: "The balaced scorecard retais traditioal fiacial measures. But fiacial measures tell the story of past evets, a adequate story for idustrial age compaies for which ivestmets i logterm capabilities ad customer relatioships were ot critical for success. These fiacial measures are iadequate, however, for guidig ad evaluatig the ourey that iformatio age compaies must make to create future value through ivestmet i customers, suppliers, employees, processes, techology, ad iovatio." Kapla ad Norto (1992) suggest a balaced scorecard, which requires maagers to balace four differet but liked perspectives i order to idetify appropriate measures of performace. The first perspective represets (traditioal) accoutig measures that report the fiacial cosequeces of actios already take. This fiacial perspective highlights how the compay appears to shareholders ad cocetrates o measures relatig to profitability ad growth, cash flow ad gearig. The Balaced Scorecard supplemets these fiacial measures with three other perspectives dealig with (a) customers, (b) iteral processes, ad (c) the firm's iovatio ad learig record - all three areas that are importat drivers of future fiacial performace. The customer perspective is desiged to highlight the factors that really matter to customers such as value for moey, time ad performace. The iteral busiess perspective is desiged to focus o those critical busiess activities that must be performed i order to satisfy the expectatios of its customers. These iclude cycle time, quality ad efficiecy of operatios. The iovatio ad learig perspective highlights the fact that, i the face of itese competitio, firms must make cotiual improvemet ad have the ability to itroduce ew products i the future. Thus, the four perspectives of the balaced scorecard ca be summarized as follows: Table 1: Balaced Scorecard Framework Perspective Fiacial Iteral Customer Iovatio ad Learig Focus How do we look to our stockholders? How ca we improve the efficiecy of operatios? What do our existig ad ew customers wat from us? How ca we cotiue to iovate ad lear? A automatic side beefit of this critical thikig is the developmet of a deeper uderstadig of the various dimesios of the busiess ad what activities must be performed well if the firm is to achieve success. I tur, such measures ca be valuable i exteral bechmarkig exercises. I additio, by workig closely with productio, marketig ad other staff to agree ad obtai such iformatio, the maagemet accoutat ca help to brig together these disciplies ad istall a greater sese of purpose ad focus. Kapla ad Norto (1992) argue that maagers should ot have to choose betwee fiacial ad operatioal measures of performace. Rather, maagers wat a balaced presetatio of both fiacial ad o-fiacial measures. Measurig the performace of ecommerce compaies has always bee a relatively difficult task. Practitioers ad cosultats have suggested differet parameters to measure the success of these compaies. For example, umbers of subscribers, reach (uique visitors), ad reveue have bee idetified as relevat measures to assess the performace of these compaies. I terms of marketig parameters, persoalizatio ad offerig value to customers have bee liked with the success of the ecommerce firms (Seybold, 2000). ecommerce firms have also focused o cotiuous iovatio i order to itegrate techology with offerig customized tailor-made services to the customers. Through the itegratio of techology, oe-o-oe marketig, permissio marketig ad persoalizatio have become ecessary tools for ay ecommerce compay i order to stay competitive. The focus has bee o itegratig various offlie ad olie processes to provide solutios to customer eeds. ecommerce firms are presetly usig parameters such as reveue, click-through-ratios ad other idirect parameters to measure their performace. Thus, i practice, ecommerce compaies already measure their performace by usig a mix of traditioal ad ew parameters. 34
Both academics ad practitioers have attempted to apply the Balaced Scorecard cocepts to ecommerce compaies. These attempts differ from Kapla s scorecard i terms of the perspectives ad parameters. Lauched i 1999, McKisey s e-performace scorecard collects data about a variety of visitor, customer, ad fiacial metrics (Agarwal, Aroa, ad Lemmes, 2001). The scorecard comprises 21 idicators that measure performace both statically (at oe poit i time) ad dyamically (over a period of time). These idicators are grouped ito three categories attractio, coversio, ad retetio ad the folded ito the overall e-performace scorecard, which is a weighted average of the twety-oe idicators. The McKisey scorecard highlights two key dimesios: the efficiecy of costs (for example, the cost of attractig visitors to a site ad of maitaiig active customers) ad the effectiveess of a site s operatios (such as coversio rates, the rate at which the umber of customers icreases, ad customer gross margis). Best practice i the ebusiess sector combies the lowest costs with the highest effectiveess. I her book Customers.com, Seybold lists eight success factors for ecommerce compaies (Seybold, 2000). These factors cover various aspects of the busiess but she suggests that the mai focus should be the customer. While these factors are primarily related to the customer, other researchers focus o other areas of ecommerce such as Logistics. The future role of distributio ad fulfillmet has bee summed up as follows: "ecommerce delivery will become the oe area i which a busiess ca truly distiguish itself. It will become the critical core competece. Its speed, quality ad resposiveess may well become the decisive competitive factor, eve where brads seem to be etreched. Ad there are o multiatioal busiesses ad altogether very few busiesses that are orgaized for it. Very few yet eve thik that way," (Drucker, 2000). To summarize, from a practitioer or applied perspective, parameters that assess much more tha fiacial performace have bee cosistetly highlighted. The customer poit of view ad itegratio of techology to produce persoalized web cotet for customers are cosidered importat measures of performace i ecommerce compaies. Processes, logistics, ad techological iovatios are other measures of performace for ecommerce compaies that have bee emphasized. The applied perspectives highlighted i this sectio suggest that the focus of practitioers is o measures such as techology, busiess model, web-site features, customer value ad iovatio rather tha the core busiess perspective of geeratig ecoomic value for the busiess. These o-fiacial measures are very cosistet with those emphasized by Kapla ad Norto (1992) i their Balaced Scorecard approach to measure ad moitor the performace of orgaizatios. Sice the Balaced Scorecard Framework focuses simultaeously o both fiacial ad o-fiacial measures of performace, it is cosidered particularly appropriate for ecommerce compaies. Therefore, i this paper, the BSC is utilized to assess the performace of a sample of ecommerce compaies. First, the framework is developed for applicatio to ecommerce by selectig measures developed by practitioers to represet the three o-fiacial perspectives, i.e. the Customer, Iteral, ad Iovatio dimesios. The, the four sets of measures (1 fiacial, ad 3 o-fiacial) are derived for a sample of 18 ecommerce compaies that were active i 1999. DEA aalysis is performed o these measures to examie the efficiecy of these compaies o each of the BSC perspectives. As Kapla ad Norto (2001) suggest, performace measuremet has cosequeces far beyod reportig o the past. They suggest that measuremet creates a focus o the future as the measures chose commuicate importat messages to all orgaizatioal uits ad employees. Thus, the DEA aalysis is followed by a compariso of the efficiecy of the fiacial ad o-fiacial parameters for compaies that remaied successful i 2000-2001 with three compaies that subsequetly filed for bakruptcy. Based o these comparisos, it is argued that these Balaced Scorecard parameters ca effectively help us to uderstad ad explai the success ad failure of the selected ecommerce compaies. 35
3. Developmet of BSC dimesios for ecommerce compaies I this sectio, performace idicators idetified by practitioers i ecommerce, are selected to represet the four dimesios of Kapla ad Norto s (1992) BSC framework. The specific measures derived for each of the four dimesios are preseted i Table 2. Table 2. ecommerce performace idicators for the BSC dimesios Perspective Iputs Outputs Customer Marketig Number of Reveue Number of Expediture Affiliates Customers Iteral Number of Fiacig Reveue Number of Processes Employees Customers I./Kow. Number of Tech./Dev. Reveue Number of Mgmt. Employees Expediture Customers Fiace Fiacig Net Icome Reveue Number of visitors Number of visitors Fiacial Dimesio: The first perspective represets traditioal accoutig measures that report the fiacial cosequeces of actios already take. This fiacial perspective highlights how the compay appears to shareholders ad cocetrates o measures relatig to profitability ad growth, cash flow ad gearig. Traditioal fiacial measures iclude ROS (retur o sales) ad fiacig as a percetage of reveue. These measures are applicable to ecommerce compaies ad therefore, have bee icluded as represetig the fiacial perspective. Customer dimesio: The customer perspective is desiged to highlight the factors that really matter to customers such as value for moey, time, ad performace. Numbers of uique visitors ad customers are importat performace idicators for ecommerce compaies. Marketig expeditures ad umber of affiliates are used to geerate visitors, some of whom will become customers ad buy the products ad services. Thus, potetial iputs such as marketig expediture ad umber of affiliates are assumed to geerate umber of visitors, umber of customers, ad sales reveues as outputs represetig the customer dimesio. Iteral processes dimesio: The iteral busiess perspective is desiged to focus o those critical busiess activities that must be performed i order to satisfy the expectatios of its customers. These iclude cycle time, quality, ad efficiecy of operatios. It is argued that umber of employees ad available fiacig iflueces the cycle time, quality, ad efficiecy of operatios ad thus represet the iteral processes dimesio. More efficiet use of these resources will impact the coversio factor (i.e. umbers of uique visitors who become customers). Hece, outputs are umber of customers ad sales reveues. Iovatio ad learig dimesio: The iovatio ad learig perspective highlights the fact that, i the face of itese competitio, firms must make cotiual improvemet ad have the ability to itroduce ew products i the future. Developmet expediture ad umber of employees are measures of the amout of resources that are allocated to develop ew products ad services ad improvemets i service quality. Thus, these are cosidered the iputs represetig the iovatio dimesio to geerate umbers of customers ad reveue as outputs. The fiacial ad o-fiacial performace measures derived by applyig the BSC to performace idicators developed by practitioers to assess ecommerce compaies were the utilized to ivestigate empirically the utility of the framework ad measures. The methodology of the empirical ivestigatio is described i the followig sectio. 36
4. Methodology 4.1. Sample The data were obtaied from the ecommerce Almaac data set collected by the Itermarket Group. This almaac from the Itermarket group compiles exhaustive iformatio about ecommerce compaies ad icludes fiacial, marketig, operatioal ad other iformatio that ca be categorized ito the balaced scorecard framework. The origial data set icludes eighty-two ecommerce compaies. However, data o all the performace measures that were derived for each of the four BSC dimesios were available for oly eightee compaies. Hece, the fial sample cosists of these eightee ecommerce compaies. I order to aalyze the relevace of the balaced scorecard i differetiatig betwee both successful ad failed ecommerce compaies, it was very importat to iclude compaies that were active ad fuctioig compaies at the time of data collectio. Hece, the sample set icludes data from the year 1999 whe all of the eightee compaies were i operatio ad were goig cocers (i.e., compaies that were expected to be i operatio i the ear future). I the first set of aalysis, DEA methodology is applied to measures used to represet each of the four BSC dimesios. I the secod set of the aalysis, three compaies that subsequetly filed for bakruptcy (i 2000-2001) were chose for aalysis ad compared with three compaies that were highly raked i the DEA aalysis coducted o all 18 compaies based o data from 1999. 4.2. Pla of Aalysis The primary purpose of the empirical aalysis was to examie if the performace measures derived from the Balaced Scorecard were useful i differetiatig betwee successful ad subsequetly usuccessful ecommerce compaies. I the first set of aalysis, DEA methodology was utilized to examie the efficiecy of all eightee compaies o each of the four dimesios. DEA methodology ca be briefly described as follows: Through the optimizatio for each idividual uit, DEA yields a efficiet frotier that represets ad estimates the relatios amog the multiple performace measures (Chares, Cooper ad Rhodes, 1978). Suppose we have a set of decisio makig uits (DMUs) (e.g., compaies), DMU ( = 1,, ) ad let x i (i = 1,, m) be the m iput performace measures where smaller values are preferred, e.g., cost measures ad y r (r = 1,, s) be the s output performace measures where larger values are preferred, e.g., reveue. Thus, we have m+s performace measures for the DMUs. Further, we have x i as the observed value o the ith iput performace measure ad y r as the observed value o the rth output performace measure. Based upo the observatios, we have the followig DEA model for evaluatig the relative efficiecy of DMU amog other DMUs: o mi s. t. 1 1 1 x x y i r 1 0, y io ro 1,..., i 1,..., m r 1,..., s (1) 37
Model (1) is called variable returs to scale (VRS) model i DEA (Baker, Chares ad Cooper, 1984). Model (1) is iput-orieted, sice it miimizes iputs while keepig the outputs at their curret levels. We ca have a output-orieted model, which maximizes outputs while keepig the iputs at their curret levels. max s. t. 1 1 1 x y i r 1 x io y ro 0, 1,..., i 1,..., m r 1,..., s (2) The above two models allow us to deal with egative iputs ad outputs. See Zhu (2002) for additioal DEA models. 5. Results I the first set of aalyses, DEA methodology was applied ad iput ad output-orieted models were ru to assess the efficiecy of eightee E-commerce compaies o each of the four dimesios of the BSC. While the fiacial perspective utilizes traditioal fiacial measures, the customer, iteral processes ad iovatio dimesios utilize o-fiacial measures. O each dimesio, the compaies have bee preseted based o their decreasig order of efficiecy scores for each dimesio (see tables 3-6). For example, i the fiacial dimesio, 1 is the most efficiet (Amazo.com) ad 1.23 is the least efficiet (Webva) (See Table 3) as a output-orieted model was utilized, while i the customer dimesio 1 is the most efficiet (agai Amazo.com) ad 0.14 is the least efficiet (PlaetRX.com) as a iput-orieted model was used. Sice our obective was to examie the utility of the DEA efficiecy scores i predictig future success or failure of the compaies, we idetified three compaies that subsequetly failed ad three that remaied successful ad located these six compaies i the rak-ordered list i Table 3. The three compaies that remaied active i 2000-2001 were Amazo.com, ebay, ad Pricelie.com. The three compaies that subsequetly failed were Webva.com, PlaetRx.com, ad Furiture.com As evidet from Table 3, two successful compaies (Amazo.com ad ebay) ad oe of the oes that subsequetly failed (Furiture.com), emerge as fiacially efficiet i 1999, fallig amog the seve compaies with the highest rak-orders (1-7). The two compaies that subsequetly failed (Webva ad PlaetRX.com) ad oe of the successful compaies fall amog the lowest raked compaies. Webva is the lowest raked with Pricelie.com also showig that it does ot appear to be a fiacially efficiet compay ad PlaetRX.com fallig withi the six lowest raked firms. Hece, the results from the fiacial dimesio are mixed ad do ot appear to preset the full picture. Table 3: Fiacial Dimesio DMU Name Iput-orieted VRS Efficiecy Amazo.com 1.00000 ebay 1.00000 E*Trade 1.00000 iprit 1.03084 Peapod 1.05091 Outpost.com 1.06077 Furiture.com 1.07603 iow 1.08086 PetsMart.com 1.08399 1-800-Flowers 1.08678 CarsDirect.com 1.11428 NextCard 1.12178 PlaetRX.com 1.15505 Buy.com 1.15614 Cdow 1.19104 Beyod.com 1.20081 Pricelie.com 1.20808 Webva 1.22904 38
From the customer perspective (please see Table 4), it ca be oted that the three successful compaies (Amazo.com, ebay, ad Pricelie.com) are highly efficiet (scores betwee.93 ad 1.0), whereas two of the three failed compaies (Furiture.com ad PlaetRx.com) rak lowest o efficiecy with scores ragig from 0.22 ad 0.14, respectively, i the Customer perspective. The other failed compay, Webva falls i the middle rage of efficiecy (0.61). Apparetly, these efficiecy scores based o data from 1999 whe all the compaies were active, do differetiate betwee the oes that remaied successful ad those that subsequetly failed. O the iovatio ad learig perspective, agai the efficiecy scores appear to discrimiate betwee the subsequetly successful ad usuccessful compaies (Table 5). All three successful compaies fall withi the top 7 rak-ordered compaies ad have efficiecy scores ragig from.79 to 1.0. O the other had, the three compaies that subsequetly failed fall i the lowest six raks ordered compaies, with efficiecy scores less tha.38. As evidet from Table 6, the results are mixed o the iteral process perspective (as they were o the fiacial dimesio). The two most successful compaies (Amazo.com ad ebay.com) were optimally efficiet oce agai (1.0), while oe failed compay (Furiture.com) also had high efficiecy scores (.89). Oe successful ad oe failed compay (Pricelie.com ad PlaetRx.com) had medium efficiecy levels (0.68 ad 0.51), ad oe failed compay (Webva) had the lowest efficiecy score of 0.08. Table 4: Customer Dimesio DMU Iput-orieted Name VRS Efficiecy Amazo.com 1.00000 ebay 1.00000 Buy.com 1.00000 iprit 1.00000 Peapod 1.00000 Pricelie.com 0.93173 1-800-Flowers 0.82261 Webva 0.61021 NextCard 0.53494 Outpost.com 0.53112 Cdow 0.48576 CarsDirect.com 0.38828 iow 0.37709 Beyod.com 0.31434 E*Trade 0.26294 Furiture.com 0.22682 PetsMart.com 0.22603 PlaetRX.com 0.13916 Table 5: Iovatio & Learig Dimesio DMU Name Iput-orieted VRS Efficiecy Amazo.com 1.00000 ebay 1.00000 Buy.com 1.00000 CarsDirect.com 1.00000 1-800-Flowers 1.00000 PetsMart.com 1.00000 Pricelie.com 0.79800 Outpost.com 0.77800 iprit 0.72561 Peapod 0.63112 Beyod.com 0.62029 Cdow 0.38981 Furiture.com 0.38274 iow 0.25468 NextCard 0.21103 PlaetRX.com 0.19840 E*Trade 0.17813 Webva 0.15688 Table 6: Iteral Process Dimesio DMU Name Iput-orieted VRS Efficiecy Amazo.com 1.00000 ebay 1.00000 Buy.com 1.00000 Cdow 1.00000 iow 1.00000 iprit 1.00000 1-800-Flowers 1.00000 PetsMart.com 1.00000 Furiture.com 0.89217 Outpost.com 0.84993 Pricelie.com 0.68200 Beyod.com 0.58523 PlaetRX.com 0.51092 Peapod 0.49084 NextCard 0.44615 CarsDirect.com 0.18986 E*Trade 0.17813 Webva 0.08401 39
I summary, DEA efficiecy scores represetig the customer dimesio ad the iovatio dimesio (that are particularly relevat for ecommerce compaies) do differetiate betwee the three successful ad the three subsequetly failed ecommerce compaies. The results o the fiacial dimesio ad the iteral processes dimesio (represetig more traditioal dimesios of compay performace) are mixed, although the two most successful compaies Amazo.com ad ebay are cosistetly raked first ad secod. 6. Compariso Of Key Performace Idicators For Successful Ad Failed Compaies I the ext set of aalyses, the three compaies that remaied successful were compared to the three compaies that subsequetly failed (filed for bakruptcy i 2000-01) o key performace idicators (KPI) represetig the four dimesios of the BSC (see Table 7 for the key performace idicators). Table 7: Key Performace Idicators for the Four BSC dimesios Perspective Customer Iteral Process Iovatio Customer Coversio Factor Customer Coversio Factor Employee Value Reveue/Employee Key Performace Idicators Customer Profitability Profitability per customer Reveue/ Techical/Dev. expediture Employee Profitability Profitability/Employee Customer cotributio Reveue per customer Reveue/ Marketig expediture Customers/ Techical-Dev. expediture Fiace Fudig Reveues Net icome These key performace idicators were selected for each dimesio based o practitioer measures ad measures typically utilized i idustry. Data o the key performace idicators represetig each of the four BSC dimesios is preseted for the six compaies selected for compariso i.e. the three successful compaies ad the three that subsequetly failed (see Tables 8-11). As evidet from Table 8 o the fiacial dimesio, although the fudig ad reveue KPI s for the three successful compaies are geerally higher tha for the three failed compaies, five of the six compaies show o profit (egative profitability). Although some ecommerce compaies have started postig profits lately, this was typical i the 1999-2000 time frame, with oly ebay postig a profit for the curret year. Table 8: Fiacial Dimesio: KPI Results DMU Fudig Reveue Profit Amazo.com 2680.00 1640.00-719.70 ebay 823.90 224.70 10.83 Pricelie 1592.00 482.40-152.60 Furiture 84.00 10.90-46.46 PlaetRx 144.50 8.99-98.01 Webva 966.03 13.31-144.60 Thus, based o the KPIs for the fiacial dimesio alog, it would ot have bee possible to predict that ay of the compaies would be successful i subsequet years. Results o the KPIs for the fiacial perspective are therefore cosistet with the results from the previous aalysis usig DEA methodology o the fiacial dimesio, idicatig that this dimesio does ot provide a complete picture of the performace of ecommerce compaies. The KPIs represetig the Customer dimesio, however, do add importat iformatio regardig the performace of the compaies. As ca be see from Table 9, two of the KPI s (customer coversio factor ad 40
profitability per customer) clearly differetiate betwee the successful ad failed compaies, with the coversio factor for the three successful compaies ragig from 5.9 to 9.5 ad the coversio factor for the failed firms ragig from 1.5 to 2.8. The profitability per customer rages from a loss of $(40) to a profit of $1 per customer for the successful firms while the results for the usuccessful firms depict a loss ragig from $(179) to $(3,077) per customer. The data o the reveue per customer KPI however is mixed - the reveue per customer for two of the successful firms varies from is relatively higher, though oe of the failed firms shows the highest reveue per customer (Webva). Although the reveue per customer for Webva appears to be relatively high, the loss per customer of $(3,077) depicts a differet story. Table 9: Customer Dimesio: KPI Results: DMU Customer Reveue/Customer Profitability/Customer Coversio Factor Amazo.com 9.50 97-42 ebay 5.94 22 1 Pricelie 7.16 127-40 Furiture 2.44 42-179 PlaetRx 1.48 35-386 Webva 2.80 283-3077 The KPIs represetig the Iovatio dimesio also add relevat iformatio regardig the performace of the compaies. Two of the three KPI s clearly differetiate betwee the successful ad failed compaies (see Table 10). The reveue per employee KPI is visibly higher (ragig from $215,789 to $1,276,190) for the successful compaies ad lower for the failed oes (ragig from $23,051 to $51,192). The profit per employee KPI does ot differetiate as much because five out of the six firms are ot profitable. However, the customer per developmet expediture does differetiate as it depicts a higher ratio ragig from 106 to 421 for the successful firms ad a markedly lower 3 to 39 ratio for the three firms that subsequetly failed. Table 10: Iovatio Perspective: KPI Results DMU Reveue/Employee Profitability/Employee Customer/Developmet expediture ( 000) Amazo.com 215789-93680 106 ebay 741700 36093 421 Pricelie 1276190-403704 271 Furiture 51192-218122 39 PlaetRx 23051-251318 20 Webva 47706-518280 3 For the Iteral process dimesio, all three key performace idicators very clearly differetiate betwee the successful ad failed compaies (see Table 11). The coversio factor KPI rages from 5.9 to 9.5 for the successful firms ad rages from 1.5 to 2.8 for the failed firms. The reveue per developmet expediture also shows that there is a marked differece sice the scores rage from $9.4 to 34.5 for the successful firms ad oly rage from $0.7 to $1.6 for the failed firms. Fially, the reveue per marketig expediture KPI also shows a marked differece i that the KPI s for the successful firms rage from $2.3 to $6.1 while the KPI s for the failed firms rage from $0.2 to $1.1. 41
Table 11: Iteral Process Dimesio: KPI DMU Customer coversio factor Reveue/Marketig Expediture Reveue/Developmet Expediture Amazo.com 9.51 4.00 10.30 ebay 5.94 2.30 9.40 Pricelie 7.16 6.10 34.50 Furiture 2.44 0.30 1.60 PlaetRx 1.48 0.20 0.70 Webva 2.80 1.10 0.90 7. Coclusio I coclusio, results from the two types of aalyses (DEA ad KPI methodology) suggest that the fiacial dimesio of the Balaced Scorecard framework provides isufficiet iformatio to differetiate betwee the ecommerce compaies that remaied successful ad those that subsequetly failed. The results of the DEA aalysis were mixed, while the KPI aalysis idicated that five out of the six firms chose could fail (due to egative earigs) i the ear future. Icludig the o-fiacial dimesios of the Balaced Scorecard framework provides a much more complete picture of the performace of the selected compaies, based o which it would have bee possible to predict subsequet success or failure. I both aalyses (DEA ad KPI methodology), the customer ad iovatio ad learig dimesios eable differetiatio betwee successful ad subsequetly failed ecommerce firms. O the iteral process dimesio, although the DEA aalysis does ot differetiate clearly betwee the successful ad failed compaies, the KPIs clearly differetiate betwee the two sets of compaies. Overall, these results show that the customer ad iovatio ad learig dimesios are able to differetiate betwee ecommerce compaies with the potetial for cotiued success ad those that are likely to fail. The fact that these dimesios are especially importat for ecommerce compaies is cosistet with covetioal wisdom. The results therefore uderscore the importace ad relevace of the Balaced Scorecard framework for the performace measuremet of ecommerce compaies. Refereces 1. Agarwal, V., Aroa L. D., ad Lemmes R. 2001, McKisey B2C e-performace scorecard, The McKisey Quarterly, Number 1: 20-32. 2. Baker, R.D., A. Chares, ad W. W. Cooper. 1984, Some models for estimatig techical ad scale iefficiecies i data evelopmet aalysis, Maagemet Sciece, 30, 1078-1092. 3. ad H. H. Johsto. 1995. A empirical study of the busiess value of the U. S. airlies computerized reservatios system, Joural of Orgaizatioal Computig, 5 (3): 255-275. 4. Chares, A., W. W. Cooper, ad E. Rhodes. 1978. Measurig the efficiecy of decisio makig uits, Europea Joural of Operatioal Research, 2 (6): 429-444. 5. Drucker, P. 2000. Ca ecommerce deliver? The world i 2000, The Ecoomist Publicatios, Lodo. 6. Hemmer, T. 1996. O the desig ad choice of moder maagemet accoutig measures, Joural of Maagemet Accoutig Research, 8: 87-116. 7. Kapla, R. S. ad D. P. Norto. 1992. The Balaced Scorecard Measures that drive performace, Harvard Busiess Review, Ja-Feb.: 71-79. 8.. 2001. Trasformig the balaced scorecard from performace measuremet to strategic maagemet: Part 1, Accoutig Horizos, 15: 87-104. 9. Seybold, P. B. ad R. Marshak, 2000. Customers.Com: How to create a profitable busiess strategy for the iteret ad beyod, Radom House. Audio Books, U. K. 10. Zhu, Joe. 2002. Quatitative Models for Performace Evaluatio ad Bechmarkig: Data Evelopmet Aalysis with Spreadsheets, Kluwer Academic Publishers, Netherlads. 42