NET Institute*

Transcription

1 NET Insttute* Workng Paper #03-03 October 2003 Network Effects and Swtchng Costs n the Market for Routers and Swtches by Chrs Forman and Pe-yu Chen Graduate School of Industral Admnstraton Carnege Mellon Unversty * The Networks, Electronc Commerce, and Telecommuncatons ( NET ) Insttute, s a non-proft nsttuton devoted to research on network ndustres, electronc commerce, telecommuncatons, the Internet, vrtual networks comprsed of computers that share the same techncal standard or operatng system, and on network ssues n general.

2 Network Effects and Swtchng Costs In the Market for Routers and Swtches Chrs Forman Graduate School of Industral Admnstraton Carnege Mellon Unversty Pe-yu Chen Graduate School of Industral Admnstraton Carnege Mellon Unversty September 2003 Abstract Ths research examnes the mpact of swtchng costs on vendor choce n the market for routers and swtches. We show that despte the use of open standards whch attempt to enhance nteroperabltes for equpments from dfferent vendors, vendors n ths market are able to mantan hgh swtchng costs. Because routers and swtches are networked goods, swtchng costs may arse from pror nvestments made at the same establshment and/or at other establshments wthn the same frm. We study how the ntroducton of swtches nto the LAN market affected vendor choce n routers. In partcular, we provde evdence of sgnfcant cross-product swtchng costs and szeable shoppng costs when buyers purchase routers and swtches smultaneously. However, we also show that the ntroducton of swtches may have temporarly reduced swtchng costs for router buyers nvestng n swtches. We thank Mark Doms, Av Goldfarb, Shane Greensten, Lorn Htt, Mke Mazzeo, Sandy Slaughter, Mchael Smth and semnar partcpants at Carnege Mellon and the 2002 Internatonal Conference on Informaton Systems for helpful comments and suggestons. Tm Ward and Harry Georgopolous provded helpful techncal expertse on computer networkng equpment. We thank the General Motors Strategy Center for fnancal support, and Harte Hanks Market Intellgence for provdng essental data. All errors are our own.

3 1. Introducton Informaton technology (IT) nfrastructure represents a crtcal element n the constructon of state-of-the-art nformaton systems. A recent study by Well and Broadbent (1998) ndcates that frms spend about 58% of ther total IT nvestments on computng hardware and nfrastructure, and that nfrastructure nvestment appears to be the most dffcult decson faced by senor management. Another study by Strassman (1999) found that 40% of IT spendng s on nfrastructure actvtes alone. These studes suggest that IT hardware nvestments have a maor mpact on frms and hghlght the mportance of such nvestment decsons. Networkng equpments appears to be neural to the nfrastructure of most modern IS systems, and routers and swtches are among the most mportant networkng gear. Understandng what affects frms decson on the nvestments on routers and swtches thus has substantal and long term mplcatons on nformaton systems (IS) development n frms. Research n nformaton systems (IS) has frequently reported that IT hardware nvestments become embedded. Buyers of nformaton technology make product-specfc nvestments n tranng, complementary software and data, and nstallaton. Because these nvestments usually can not be reused on other vendor products, they create swtchng costs when changng vendors. These swtchng costs can create lock-n to exstng nvestments f they are hgh enough. As a result, legacy hardware nvestments can mpact the techncal traectory of a frm s IT nfrastructure long past the ntal nvestment date. We study how swtchng costs affect vendor choce n the market for LAN equpment over Over ths perod a new technology, swtches, were ntroduced nto the market. Swtchng costs have been studed before n onlne (e.g., Chen and Htt 2002; Brynolfsson and Smth 2000) and offlne (e.g., Greensten 1993; Breuhan 1997) settngs. However, our unque settng allows us to make several contrbutons to the exstng lterature. Because routers and swtches are networked goods, we are able to examne whether the effects of ncumbency and swtchng costs extend across establshments wthn the same frm. We also examne whether they can spll over from one product to another. We examne 1

4 whether compatblty appears to play a role n vendor decsons when buyers are purchase multple product lnes. Ths manfests tself n a buyer tendency to purchase routers and swtches from the same vendor. Last, and perhaps most nterestngly, we examne how the ntroducton of a new nnovaton, swtches, affects the swtchng costs on an exstng good, routers. The ntroducton of new hardware or software may lower the effects of lock-n arsng from swtchng costs or network effects (Breuhan 1997; Brynolfsson and Kemerer 1996). We examne whether the ntroducton of swtches offered a temporary wndow of opportunty for lower swtchng costs for routers. We use dscrete choce to model the mpact of swtchng costs on vendor choce. In these models, the probablty of choosng a partcular vendor s made a functon of buyer characterstcs and the extent of prevous buyer-vendor nteracton. One common concern n nferrng swtchng costs from past vendor nteracton s the dffculty of dentfyng true state dependence from spurous state dependence. We offer a unque method of dentfyng true state dependence from spurous state dependence by estmatng how changes n the quantty of nstalled base affect the magntude of swtchng costs. To estmate these models, we analyze data from over 22,000 frms, concentrated prmarly n the fnance and servces sectors. Harte Hanks Market Intellgence, a commercal market research frm that tracks use of Internet technology n busness, undertook the survey. Our results show that compatblty wth the nstalled base plays a maor role n buyer behavor n ths market. We demonstrate that although the ntroducton of swtches dd temporarly lead to lower router swtchng costs, there remaned sgnfcant cross-product swtchng costs between routers and swtches. These results have two mportant mplcatons for managers. Frst, they confrm the usefulness of broad product lne strateges pursued by frms lke Csco (Gawer and Cusumano 1999). Second, they show that although new product ntroducton dd offer a wndow of lower swtchng costs for router buyers n the short run, n the long run compatblty wth the nstalled base of networkng equpment remaned mportant. 2

5 The rest of ths paper s as follows. In the next secton, we provde some background on swtchng costs as well as the market of routers and swtches. The hypotheses and a descrpton of the data are presented n Secton 3, followed by methodology n Secton 4 and emprcal results n Secton 5. We dscuss the fndngs and gve concludng remarks n Secton 6 and 7, respectvely. 2. Lterature Revew and Background 2.1 Swtchng Costs and Network Effects In many markets, consumers face non-neglgble costs of swtchng from one vendor to another. Swtchng costs can be caused by the need for compatblty wth other users (.e., network effects) or/and wth exstng equpment, transacton costs (or shoppng costs), learnng costs, search costs, and psychologcal costs of swtchng brands. There are also artfcal swtchng costs (vendor-nduced). A frm may change the swtchng cost ts buyers face through certan frm-specfc practces, such as contracts or dscount coupons. Frms may change swtchng costs through product desgn by alterng compatblty wth rval s products (e.g., Matutes and Regbeau, 1988; Economdes, 1989; Enhorn, 1992). Klemperer (1995) provdes an overvew and defntons of the sources of swtchng costs. When facng swtchng costs, a buyer wll fnd t costly to swtch supplers and wll tend to buy from the same vendor over tme. Swtchng costs shft the locus of competton and have been lnked to a number of nterestng compettve phenomena n the economcs (e.g., Katz and Shapro 1985; Shapro and Varan 1999) and IS (e.g., Clemons and Klendorfer 1992; Rggns et. al. 1994; Wang and Sedmann 1995) lteratures. The emprcal lterature on swtchng costs and network effects s much smaller than the theory lterature, due prmarly to the detaled data on ndvdual- or frm-specfc decsons requred to test hypotheses. Greensten (1993) studes manframe procurement decsons n government agences and shows that compatblty wth nstalled base s a maor factor affectng purchase decsons. Breuhan (1997) examnes the role of swtchng costs on frm software purchase decsons; she fnds evdence of swtchng costs and shows that converson from DOS to Wndows lowered the swtchng costs of mgratng from Wordperfect to Mcrosoft Word. Gandal (1994) and Brynolfsson and Kemerer (1996) show that, other thngs beng equal, users are wllng to pay a premum for compatblty and goods wth 3

6 larger network n the software market. Kauffman, McAndrews, and Wang (2000) examne how pror nvestments n propretary networkng technology nfluence ncentves to adopt a mult-bank electronc network. Recently, several emprcal papers have also examned the role of swtchng costs n electronc markets. For example, Brynolfsson and Smth (2000) found, usng data from a prce comparson servce (the DealTme shopbot ), that a consumer s past purchase experence has sgnfcant predctve power of her future store choce and that customers are wllng to pay premum prces for books from the retalers they had dealt wth prevously. Chen and Htt (2002) also fnd sgnfcant swtchng costs at dfferent onlne brokerage frms and show how systems usage, servce desgn and other frm level factors mght affect customer swtchng Routers and Swtches: Technology and Market Structure In ths paper we examne buyer behavor n the market for networkng equpment. In partcular, we observe purchases of networkng hardware that drects packets wthn a LAN or between LANs, and exclude carrer-class gear that routes packets over the broader Internet. 1 There were three maor classes of enterprse networkng gear n 1998: hubs, routers, and swtches. Hubs and swtches work on level 2 of the OSI standards archtecture, whle routers work on level 3. 2 Hubs are the smplest class of networkng equpment. Hubs are most commonly used to connect computers wthn a LAN, or to allow multple users to share a network lne. Unlke routers and swtches, when a data frame reaches a hub, that frame s broadcast out to all of the ports on the hubs. The purchase and management of hubs s generally straghtforward. As Panko (2001) states, Hubs are hubs. The only thngs you have to worry about when buyng a hubs are the number, speeds, and types (R-45, etc.) of ports you want on the hub. Because of ths smplcty, nteroperablty s less of a concern wth hubs than t s wth other classes of networkng equpment. Recently, hubs have begun to be supplanted by swtches. 1 So, for example, n 1998 our data ncludes models n the Csco 7500 seres but not n the Csco seres. 2 Recently, layer 3 swtches have been developed that work on level 3 of the OSI model, but these were not avalable over our sample. 4

7 Dataquest (1999) estmates that end-user spendng on hubs declned 37.4 percent between 1995 and 1998, from $4.8 bllon to $3.0 bllon. The next class of networkng gear that we examne s routers. Pror to the rse n popularty of swtches n 1994 and 1995, routers represented the prmary way n whch networks were nterconnected. 3 Routers use nformaton from layer 3 of the OSI standards archtecture, and use prmarly a store-and forward technque to relay frames: the entre frame s read nto memory before t s sent out (Datapro 1999). Ths means that routers are slower at packet forwardng than ether hubs or swtches, but gves them addtonal functonalty these other classes of equpment do not have. Routers have functonalty whch also enables them to montor and manage network traffc effcently. Routers are able to update ther dynamc forwardng tables by communcatng wth other routers, enablng them to determne the optmal path through whch a packet of nformaton should flow. The archtecture of routers also allows them to perform network management and securty features, allowng network managers to dentfy problems and congeston wthn a network wth ease as well as provdng protecton to keep the network safe from outsde ntruders. Because of ther complexty, routers are the most expensve networkng equpment on a per-port bass, and also have the hghest swtchng costs because of learnng cost. Swtches were ntroduced n the md-1990s to ncrease bandwdth and reduce delay n networks. Swtches operate on layer 2 of the OSI model, combnng the fast throughput of hubs wth some of the ntellgence of routers (Datapro 1999). Unlke hubs, swtches have the capablty to dentfy the MAC addresses connected to each of ts ports n ts forwardng tables; however, unlke routers, swtches can not optmze network traffc. Swtches are much faster than routers, however. Panko (2001) descrbes the reason for ths dfference between router and swtch packet forwardng speeds, Wth a router forwardng table, the destnaton IP address has to be compared wth every entry n the router forwardng table, and, f there are dfferences n match length or metrcs, these factors must be taken nto account. In contrast, the swtch has to fnd only the one match n the table to the destnaton MAC address, and ths lookup can 3 Another type of nternetworkng technology, known as brdges, had been popular n the 1980s but had begun to de out consderably n mportance n the early part of the 1990s. 5

8 be done very quckly. Ths smplcty allows swtches to be both much faster and much less expensve than routers. Because of ther greater speed and lower cost, enterprse swtches are used as a substtute for routers, and have begun to push routers to the edge of ste networks (Panko 2001; Datapro 1999). However, because of ther addtonal features, routers have not been supplanted by swtches completely. Routers contnued to be valued for ther management and securty features. Moreover, because they are much more ntellgent than swtches at managng transmsson lnes, they are commonly used to connect LANs across multple stes (Panko 2001). Both routers and swtches communcate usng open protocol standards lke Ethernet or token rng. Thus, ncompatbltes n ths market cannot explctly arse from propretary communcatons standards. However, routers and swtches are very complcated devces, carryng advanced processng devces and sometmes costng tens of thousands of dollars. The complexty of these devces leads to two common costs n runnng multvendor networks: (1) costs to learnng new devces; and (2) costs of ensurng compatblty and nteroperablty between multple devces. Confguraton of new routers and swtches can be very dffcult. Despte the prevalence of open networkng protocols, vendors often employ propretary software to run ther networkng gear. Propretary software and complcated command-lne nterfaces can make management of these devces. Setup and confguraton s also complcated, and for many buyers entals the use of outsde networkng consultants. These confguraton costs mply the presence of cost savngs for buyers that purchase from ncumbents. A second cost to multvendor networks concerns the ease wth whch gear from dfferent manufacturers s able to work together. In the ndustry trade press, ths s known as the nteroperablty problem. Because of the complexty of the devces, tme-consumng and costly confguraton s sometmes needed to get hardware from dfferent manufacturers to communcate wth one another. These problems are sometmes exacerbated by propretary enhancements added by vendors. 6

9 The mportance of product compatblty (or nteroperablty, as t s known among networkng professonals) s a common theme n the ndustry trade press. Trade press artcles emphasze that wthout proof of nteroperablty, users may fear that devces from new vendors may not work wth ther nstalled base (Tolly, 2000). Csco Systems s occasonally reprmanded n the press for addng propretary enhancements to standards (Wckre, 1996). Industry publcatons have also reported that senor offcals at Csco say the company s tryng to create an end-to-end servce model such as IBM s Systems Network Archtecture (Petrosky, 1996). There also exsts consderable evdence of the mportance of compatblty n ths market from the actons of vendors themselves. Vendors commonly market ther product lnes by creatng sutes of products that work together. Well-known examples nclude 3Com s NetBulder, OffceConnect, and SuperStack II product lnes; Bay Networks BayStack lne; and Csco s NetBeyond and CscoPro brands. Compatblty among routers and swtches was also the drvng force behnd the formaton of the Network Interoperablty Allance (NIA) by 3Com, Bay Networks, and IBM. The stated obectve of the NIA was to smplfy the buldng of networks, to create support for ont standards and open protocols, and to develop nteroperablty testng and the create ncentves for vendors to use common archtectural platforms (Mller, 1996). Although the NIA was short-lved, t provdes evdence of the mportance of standards and compatblty n ths ndustry as well as a concern over the ncreasng domnance of Csco Systems. 3. Hypotheses and Data To study swtchng costs, a useful startng pont s to look at loyalty rates frst to see f the ncumbent vendor enoys an advantage over other vendors. A loyalty rate shows the condtonal probablty of purchasng from the ncumbent vendor, gven that an establshment s purchasng routers and swtches that year. Fgure 1 shows loyalty rates for purchases of routers and swtches over Loyalty rates appear to be hgh n the market for routers and swtches for the well-known vendors. For each of the Bg Three vendors of 3Com, Bay Networks, and Csco, loyalty rates for routers and swtches are qute hgh, rangng from 47.8 percent to 83.5 percent n routers and from 67.3 percent to

10 percent n swtches. Loyalty rates for routers of Other vendors are low; many of ther buyers eventually mgrated to Csco. Loyalty rates for swtches of Other vendors are hgher Source of swtchng costs: hypotheses We examne the effects of swtchng costs on vendor choce by commercal establshments. Whle our observatons are based on establshment level, we allow frm effect on vendor choce decson at the establshment to account for the need for compatblty and possbly coordnaton effort made at the frm level. Table 1 provdes a summary of our hypotheses and the varable constructs used to test them. We descrbe each of these hypotheses n detal below. In secton 2.2 we descrbed how LAN equpment vendors could use network confguraton, network management, and propretary software to create swtchng costs n the presence of open standards. Our frst set of hypotheses asks whether swtchng costs exst n the market for routers and swtches and examnes the magntudes of swtchng costs, f any, n ths market. H1a: Buyers face swtchng costs n choosng a router vendor dfferent from the ncumbent router vendor at the establshment. H1b: Buyers face swtchng costs n choosng a swtch vendor dfferent from the ncumbent swtch vendor at the establshment. As noted above, swtchng costs arse not only at the establshment level due to the need to be compatble wth the nstalled base at the establshment, but also from other establshments wthn the same frm. New LAN equpments may need to be compatble wth the nstalled base at other establshments at the same frm; n addton, nstallaton and management of networkng equpment may also be provded by personnel from corporate headquarters or from other establshments wthn the frm. Our next set of hypotheses estmate the mpact of frm-wde nstalled base on establshment-level vendor choce. 4 The varance n loyalty rates for routers s greater than that for swtches because some establshments wth nstalled base n Bay, 3Com, or Other routers mgrated to Csco over the sample perod. Ths was n part because many Other vendors exted the market. 8

11 H2a: Buyers face swtchng costs when choosng a router vendor dfferent from that nstalled at other establshments wthn the same frm. H2b: Buyers face swtchng costs when choosng a swtch vendor dfferent from that nstalled at other establshments wthn the same frm. As mentoned n Secton 2.2, routers and swtches are often used together as complementary products and must be compatble for networks to functon effectvely. Vendors usually desgn ther products to nteroperate wth one another, and to use common nterfaces and software to reduce learnng costs. For example, Csco s IOS (Internetwork Operatng System) was thought by some to boost compatblty wthn Csco s product lne, but to also ncrease swtchng costs between the products of Csco and ts compettors (Shapro and Varan 1999; Bunnell 2000). As a result, swtchng costs may arse from choosng dfferent router and swtch vendors. In the face of mperfectly compatble products from dfferent vendors, ncumbency n one type of networkng equpment, say routers, wll nfluence the choce of the other, swtches. H3a: Buyers face cross-product swtchng costs n choosng a dfferent router vendor from ts ncumbent vendor n swtches. H3b: Buyers face cross-product swtchng costs n choosng a dfferent swtch vendor from ts ncumbent vendor n routers. There s yet another source of swtchng costs related to mult-products purchases: buyers may ncur shoppng costs when purchasng from multple vendors smultaneously (Klemperer, 1992). It s more convenent for consumers to purchase from the same vendor when they want to buy more than one products at the same tme snce buyng from the same vendor saves on transacton costs and reduces the amount of nteractons requred. We defne shoppng costs as the perceved costs of usng addtonal supplers other than the need of compatblty or nteroperablty consdered n H3a and H3b. Prevous lterature has noted that vendors may strategcally ncrease the breadth of ther product lne to take advantage of buyer s shoppng costs. For example, Klemperer and Padlla (1997) show that when buyers face shoppng costs when purchasng from multple vendors, there exsts a strategc ncentve for frms to 9

12 broaden ther product lnes. We provded some evdence of ths behavor earler n ths secton; the same bg three vendors manufacture both routers and swtches. Whle the result of broader product lne desgn can also be demonstrated usng producton-sde economes of scope (Bulow et al, 1985), producton-sde economes of scope may be less convncng than shoppng cost n explanng why customers mght prefer to use the same vendor for routers and swtches. Our next hypothess s: H4: When buyers purchase routers and swtches smultaneously, they face shoppng costs when choosng dfferent router and swtch vendors. The ntroducton of swtchng technology n the md-1990s represented an nterestng natural experment to study how a new technology affect consumer swtchng costs. Prevous lterature has suggested that a new IT nnovaton may erase the ncumbency advantages arsng from swtchng costs. To take advantage of new IT nnovaton, buyers usually need to nvest consderable sums to learn and mplement new nformaton systems, however, these nvestments are usually ndependent of pror nstalled base, and lead to a decrease n swtchng costs relatve to remanng wth the ncumbent vendor. When purchasng swtches, many frms redesgned ther networks to captalze on the new technology. Because the redesgned networks often requre new nvestments n routers and swtches, we nterpret the smultaneous purchase of routers and swtches as capturng possble network redesgn. 5 Thus, we expect router ncumbency to be less mportant when buyers undergo a network redesgn: 6 H5: Buyers purchasng swtches smultaneously wth routers face lower swtchng costs from exstng router nstalled base. 3.2 Data We obtaned data on technology usage from the CI Technology Database (hereafter CI database) over the perod The CI database contans data on (1) observaton characterstcs such as frm 5 About 80% of the buyers who purchased routers and swtches smultaneously were frst-tme buyers of swtches, suggestng that smultaneous purchase of routers and swtches s a good ndcaton of possble network redesgn. 6 Gven that many mgrants to a swtch-based platform wll have no nstalled base n swtches, we are unable to examne the effects of the ntroducton of new IT on router swtchng costs from nstalled base n swtches. Moreover, snce some frst-tme purchases of swtches were made wthout smultaneous purchases of routers, we are unable to dentfy the effects of platform shft on swtch vendor choce. 10

13 sze, ndustry, and locaton and (2) technology purchases of computers, networkng equpment, prnters, and other offce equpment. Harte Hanks obtans these dfferent components of the CI database at dfferent tmes of year; we assemble our sample by obtanng the most current nformaton as of December of each year. For example, the observaton for an establshment n 1995 wll contan nformaton on the establshment s characterstcs and technology usage as was recorded n the CI database n December The unt of observaton n the CI database s an establshment. The Harte Hanks establshment defnton s smlar to that used by government organzatons such as the Bureau of Labor Statstcs n calculatng government statstcs. Thus, the database wll often have data on multple establshments for a gven frm. A unt of observaton n the database contans establshment characterstcs and the stock of technology goods nstalled by the establshment as of December of each year. To keep the analyss of manageable sze, we focus on ndustres that are generally regarded as heavy users of nformaton technology 7 and establshments of over 100 employees from the CI database over the sample perod. All establshments are from the U.S. In addton, gven the hgh concentraton n the market, we consdered only the largest vendors n the market. Specfcally, we examned the vendor choce decsons of frms that purchased routers from 3Com, Bay Networks, and Csco and that purchased swtches from 3Com, Bay Networks, Cabletron, and Csco. Some observatons were dropped from the analyss for several reasons. 8 The fnal analyss data set contans 6596 observatons from 1996, 6923 observatons from 1997, and 9249 observatons n We obtaned data from the CI database on SIC codes 60-67, 73, 87, and 27. These SIC codes correspond to the ndustral groupngs on Fnance, Insurance, and Real Estate (60-67); Busness Servces (73); Engneerng, Accountng, Research, Management, and Related Servces (87); and Prntng and Publshng (27). 8 We drop establshments that are not n the database n consecutve years, that are mssng felds, and that were located n Europe. 11

14 4. Methodology 4.1 Base Model: Swtchng cost measurements We employ dscrete choce models and the random utlty framework to dentfy swtchng costs (McFadden, 1974). Random utlty modelng frameworks have been extensvely appled n studyng buyer choce among multple alternatves (McFadden, 1974; Greensten, 1993; Brynolfsson and Smth, 2000; Chen and Htt, 2002). Formally, consder a set of buyers who assocate some utlty wth each vendor, U = v +, that are comprsed of two parts: a systematc component (ν) whch captures the ε measured preference of buyers for partcular vendors, and a random component (ε ) whch summarzes the contrbuton of unobserved varables. The followng model forms the bass of our regresson model. We express the utlty a buyer assocates wth a partcular vendor as: U = α + λ Z + si +ε (4-1) The superscrpt ndexes buyers whle the subscrpt ndexes choce (or vendor). α captures the average overall attractveness of (or average utlty a buyer gets from) vendor. Z s a set of observed customer characterstcs for buyer, and vector λ captures varaton n buyer tastes across vendors. a vector capturng pror buyer-vendor nteractons for buyer and vendor. s s a vector that measures how a prevous relatonshp between buyer and vendor affects a buyer s utlty of vendor choce relatve to other alternatves. Fnally, ε, the random component, captures the customer s dosyncratc, specfc tastes or the effects of other unmeasured varables. The estmaton of the swtchng cost vector (s) s our prmary concern. Let Y be a random varable that ndcates the choce made. Each buyer wll choose the vendor that maxmzes ts utlty, that s, a buyer wll choose vendor (e.g. Y =) f and only f u > u,. k k We assume that the error term s ndependently and dentcally dstrbuted across products and consumers I s 12

15 wth the "extreme value" dstrbuton (that s, e prob.( ε ε) = e, where < ε < ). The choce ε probablty of vendor for buyer n a M-choce model s then gven by the multnomal logt model: Pr( Y = ) = M e l= 1 v e v (4-2) However, ths type of error structure s governed by ndependence of rrelevant alternatves (IIA) that s, the ordnal rankng of any two products does not depend on the attrbutes of other alternatves or even presence or absence of an alternatve choce, and thus may produce unreasonable substtuton patterns. Ths may pose a problem f there are dfferent substtuton patterns among alternatves. As a robustness check, we wll run mxed logt models that allow for heterogenety n buyer preferences. We also wll run nested logt models to examne the case where buyers purchase routers and swtches smultaneously. 4.2 Robustness Tests--Controllng for Unobserved Heterogenety One potental problem wth the methodology presented n Secton 4.1 for dentfyng swtchng costs s that the swtchng costs parameters may be pckng up unobserved varaton n buyers tastes. Buyers dffer n ther tastes, and product offerngs from dfferent vendors are heterogeneous. As a result, some vendor s products may represent a better match wth dosyncratc consumer tastes. Therefore, customers wll contnue to buy from the same vendor for reasons unrelated to swtchng costs. Heckman (1981) refers to ths problem as dentfyng between true state dependence and spurous state dependence. In ths paper, we employ two strateges to dentfy true state dependence from spurous state dependence: the nstrumental varable approach and the commonly-used random effect model, as dscussed n and respectvely The Instrumental Varable (IV) Approach Followng the base model shown n Secton 4.1, suppose X s the vector that captures all exogenous varables (as opposed to Z n (4-1)), observed or unobserved, that determne the utlty 13

16 customer gets from choce,.e.,u. β captures varaton n buyer tastes across vendors along wth X. All other notatons follow those presented n Secton 4.1, except that another subscrpt t s added ndcatng tme t. Thus, the new model s: U = α + β X + si + ε t, t, 1 t, (4-3) The problem of spurous state dependency resultng from falure to account for all buyer heterogenety dsappears f we can account for all buyer heterogenety, β X, as shown n (4-3), n whch case, a postve s would ndcate real swtchng cost. Unfortunately, n practce, t s almost mpossble to account for all buyer heterogenety gven that only a subset of X, Z where Z X, can be observed or s actually observed. When only a subset of X s observed, a postve s may smply reflect the effects from uncontrolled buyer heterogenety (.e., spurous state dependence). However, as shown n Greene (2002), as long as I s orthogonal (or unrelated) to X, then the mssng varables n X does not affect estmates of the coeffcents on I, namely, s. As a result, f I s unrelated to X, then a postve s stll ndcates true swtchng cost. However, the queston now s that we are not sure f X and I are related or not. Fortunately, snce I s an accumulated varable that may change from tme to tme, we can actually take advantage of ths property of I to resolve ths ssue. Suppose at some tme t=1, I 0 s related to one or a set of unobserved varables n W where W=X- Z, and there s no real swtchng cost (.e., s=0). So the true model s U = α + λ Z + γ W + si + ε,1,0,1 But snce W s unobserved, the model we actually estmate s: U = α + λ Z + si + ε,1,0,1 In ths case, s wll pck up effects from W (.e., s wll be a functon of γ ) and wll not ndcate the true swtchng cost snce I s correlated wth W, n ths case, we have got a measure wth spurous state dependence. Interestng, we may be able to solve ths problem by usng the change n I as the nstrumental varable snce I s changng over tme. Consder tme t, we can wrte the model as: 14

17 t, = α + λ +,0 + ( t, 1,0) + ε,1 that (I t, 1 I,0 U Z si s I I ) wll not correlate wth the mssng varable W although I,0 may be. Wth ths approach, f α + λ Z +γ W has explaned all systematc utlty contaned n U, that s, there s no real swtchng cost, then the estmate of s wll be zero although s may not be zero. On the other hand, f s s shown to be sgnfcantly dfferent from zero, then we know that there exsts real swtchng cost Random Effects Model The most common strategy adopted n the lterature to dentfy true state dependence from spurous state dependence s to add ncreasng heterogenety to the model through random coeffcents and known buyer characterstcs or to add ndependently and dentcally dstrbuted random brandspecfc effects to buyer utlty. Ths strategy has been followed by, for example, Jan, Vlcassm, and Chntagunta (1994), Keane (1997), and Shum (2002). Goldfarb (2003) suggests that one can add a random error term to loyalty coeffcents as well. Identfcaton s acheved by utlzng the panel structure of the data and assumng the brand and loyalty effects are ndependent. t Formally, we agan have the utlty of buyer for choce as u = v ( β ) + ε, where β s t unobserved for all and vares wth the true populaton densty * ( ) f β θ *, where θ s the true parameters of the dstrbuton and t ε s an d extreme value error term as before. 9 Condtonal on β, the probablty that consumer chooses alternatve n tme t s a standard multnomal logt: t exp( v t ( β )) L ( β ) = t exp( v k ( β )) The uncondtonal probablty s the ntegral over all possble values of k β, whch depends on the parameters of the dstrbuton f().several dstrbutons are commonly used for f, ncludng normal, log normal, unform, and trangular. Jan, Vlcassm, and Chntagunta (1994) argue for approxmatng and 9 Ths secton reles heavly on Revelt and Tran (1998) for the mxed logt model dscusson. 15

18 underlyng contnuous dstrbuton of unobserved heterogenety usng a dscrete dstrbuton. Here, we assume a normal dstrbuton for f(). Thus, the choce probablty for an establshment at tme t under ths densty becomes: Q = L d. * t * t ( θ ) ( β ) φ( β θ ) β For maxmum lkelhood estmaton we need the probablty of each sampled person s sequence of observed choces. Let (,t) denote the alternatve that person chose n tme t. Condtonal on β, the probablty of person s choces s the product of standard logts: S ( β ) = P ( β ) tt (,) t and the uncondtonal probablty for the sequence of choces s: * * P ( θ ) S ( β ) φ( β θ ) dβ = * Ultmately, the goal s to estmate θ, the populaton parameters of the dstrbuton. Maxmum lkelhood estmaton s not possble snce the ntegral above cannot be calculated analytcally. Instead, we approxmate the above ntegral usng smulaton and maxmze the smulated maxmum lkelhood functon. Tran (2002) provdes a fuller dscusson of smulated maxmum lkelhood estmaton under the mxed logt model. We estmaton the above equaton n GAUSS, usng code developed by Ken Tran (1999). 4.3 The Nested Logt Model: Testng Shoppng Costs and the Effect of New IT Innovaton In hypotheses 4 and 5, we seek to dentfy whether buyers who purchase routers and swtches smultaneously may behave somewhat dfferently than those buyng them separately. Snce we beleve substtuton patterns for these two types of buyers are lkely to dffer, we assume a nested model that ncludes a buyer s decson on what type of equpment to buy (routers, swtches, or routers and swtches), gven they make a purchase, followed by the choce of vendor for each type of equpment (Fgure 2). We assume that the utlty buyer assocates wth a vendor choce s addtvely separable by purchase type, t, and vendor choce, v: 16

19 Utv, = αtt + βvv tv, +ε tv, (4-4) tv, where vectors T and V represent varables affectng the decson of purchase type, t, and vendor choce, v, under purchase type t, whle α and β are the effects of these varables on buyer choce. The error term, ε tv,, follows the generalzed extreme value dstrbuton (McFadden, 1981). The nested logt model allows for rcher substtuton patterns across branches, however the choce probabltes wthn each bottom branch agan have the form of smple logt model. Thus, we can assume that the utlty model for vendor choce () assumes the form of a multnomal logt probablty: P tv, exp[ βvvt, v ] = exp[ βvvt, v ] k v Ct (4-5) C t where denotes the set of choces avalable to the buyer after a choce of branch t, and the vector V contans the nstalled base varables I k and the swtchng costs parameters s k. We have dfferent parameter estmates for dfferent branch, and examne how swtchng cost s tk, estmates ( ) dffer across purchase types. In partcular, we examne whether s lower when purchasng routers and swtches smultaneously (Hypothess 5). To dentfy shoppng costs (Hypothess 4), we nclude a dummy varable n the branch n whch buyers purchase routers and swtches smultaneously; the dummy s set to one when a buyer purchases routers and swtches from dfferent vendors. 4.4 Control Varables and Constructs To account for dfferent buyer preferences for vendors or possbly some buyer-vendor match, we nclude several buyer characterstcs n our regresson, ncludng ndustry type, number of employees, total number of network nodes at establshment, whether there s large-scale computng applcatons at establshment, whether the establshment s a branch of larger corporaton and whether the frm s a multestablshment organzaton. In addton to buyer characterstcs, year dummes are added as control s tk, tk, 17

20 varables to capture possble year trend. Table 2 provdes a summary of the control varables and ther descrptve statstcs. 5. Data Analyss 5.1 Swtchng Cost Measures: Base Model Swtchng cost estmates based on equaton set (4-1) are gven n Table 3. The table shows the results of estmatng multnomal logt models of the choce of router (column 1) and swtch (column 2) vendor. The model ncludes controls for possble buyer-vendor match wth avalable buyer attrbutes (Z ), vendor-specfc dummy varables and tme dummy varables. Columns 1 and 2 show that there exsts sgnfcant costs of swtchng from the establshment s ncumbent vendor (Hypothess 1); ths holds both for routers and swtches. The presence of swtchng costs makes swtchng to other vendors less attractve than remanng wth the ncumbent vendor. We calculate margnal effects to dentfy the mpact of ncumbency on vendor choce. Because margnal effects n the logt model are a functon of the attrbutes of the alternatve of nterest relatve to other alternatves, they generally dffer across alternatves. 10 To derve the margnal effects of swtchng costs on vendor choce, suppose the probablty of vendor beng chosen by a buyer wth no swtchng cost s P. Then the probablty of choosng ths sk e P same vendor when there exsts swtchng costs sk, becomes 1 ( sk + e 1) P P, whch s greater than for postve swtchng costs. For nstance, n Table 3, Column 1, the swtchng cost of router ncumbency on router vendor choce s Thus, the margnal mpact of ncumbency e P 3.6P s ( e 1) P = P, whch s depcted n Fgure 3 as a functon of P. In Fgure 3 we use 10 Tran (2003) provdes a dervaton of margnal effects n the multnomal logt. 18

21 ths formula to calculate the probabltes of the three router vendors beng chosen by an average buyer 11 wth no router ncumbency (n green lne) and wth router ncumbency (n red lne), the dfferences between the two lnes captures the margnal effect of router ncumbency on the vendor choces for these three vendors. For example, the probablty that an average buyer wll purchase 3Com wthout ncumbency s 11%, however wth ncumbency the probablty umps to 31% -- an ncrease of over 100%. On the other hand, the probablty of an average buyer purchasng from Csco ncreases from 72% to 90% wth Csco ncumbency, a 25% ncrease. Column 2 suggests that swtch ncumbency at an establshment also has a smlar effect on swtch choce (swtchng cost of ). The results n Table 3 further show that the effects of swtchng costs extend across establshments wthn the same frm, supportng hypothess 2. Column 1 shows that the frm-wde nstalled base of routers has a sgnfcant mpact on router vendor choce (1.5319). The effect of router ncumbency throughout the frm s as mportant as that at the establshment: for example, an ncrease n the share of Csco ncumbency from 0 to 100 percent ncreases the lkelhood of buyng a router from Csco at the establshment level from 72% to 92%. The frm-wde nstalled base of swtches has a weaker margnal effect (0.6062) on swtch vendor choce at establshment than the nstalled base of swtches at the establshment (1.6420). Ths s not surprsng, however, as routers are usually the gateway between an establshment and the outsde world and are used more frequently than swtches for nter-establshment communcaton. Columns 1 and 2 also show the mportance of cross-product swtchng costs on router and swtch vendor choce, supportng Hypothess 3. For example, swtch ncumbency at the establshment not only ncreases the probablty of buyng from the same swtch vendor but also nfluences the buyer s router vendor choce. Specfcally, the nstalled base of swtches at the establshment creates sgnfcant swtchng cost (1.8453) for buyers makng a router vendor choce, whch corresponds to an ncrease of over 30% n the lkelhood of purchasng Csco routers (from 72% to 94%). The nstalled base of swtches 11 As an example, an average buyer s descrbed as takng mean values for all contnuous varables and zero for other dummes except the year dummy of

22 at other establshments s less mportant n nfluencng the router vendor choce at establshment, however ths agan s expected as swtches are manly used for ntra-establshment communcaton. On the other hand, the nstalled base of routers at the establshment and throughout the frm also creates sgnfcant cross-product swtchng costs on swtch vendor choce, although at smaller magntudes than swtches ( and respectvely). 5.2 Swtchng Cost Measurements: Random Coeffcent Mxed Logt Models Columns 3 and 4 of Table 3 present the results of the mxed logt model. We assume that the parameter estmates for router and swtch nstalled base are normally dstrbuted, whle other parameters are held fxed. In contrast to the models n secton 5.1, we nclude establshments who dd not purchase routers or swtches n our sample and nclude varables that control for the propensty to purchase networkng hardware. Ths s because many establshments do not purchase routers and swtches every year, and our strategy s to employ the full panel of data to dentfy swtchng costs from spurous state dependence. Although the models are not drectly comparable, they do suggest that swtchng costs contnue to have an effect on vendor choce, even when buyer heterogenety s accounted for. The only excepton s n the mpact of frm-wde nstalled base of swtches on swtch vendor choce, whch becomes statstcally nsgnfcant. 5.3 Swtchng Cost Measurements: Instrumental Varables Model Columns (1) and (3) of Table 4 show the results of our IV model on router and swtch vendor choce. Because 1996 s the frst year n whch establshments bought swtches, we estmate the model over 1997 to We dentfy swtchng costs by examnng the change n router and swtch nstalled base between 1996 and For routers, ncreases n the nstalled base of routers (0.4080, statstcally sgnfcant) and swtches (0.5118, statstcally sgnfcant) from a vendor ncreased the lkelhood of purchasng from that vendor agan. For swtches, ncreases n the nstalled base of swtches ncrease the lkelhood of buyng from the same swtch vendor (0.5990). However, both the level and change n routers has no mpact on 20

23 swtch vendor choce. Ths may reflect the dffculty of dentfyng how router swtchng costs nfluence swtch vendor choce n a smaller sample. 5.4 Shoppng Costs and the Effects of New IT Innovaton on Swtchng Costs Table 5 presents the results of our ont router-swtch purchase model. We fnd that buyers purchasng routers and swtches together face sgnfcant shoppng costs (1.3824) after accountng for cross-product swtchng costs, supportng hypothess 4. In other words, buyers purchasng routers and swtches together are about 60% more lkely to buy from a vendor offerng both products than an otherwse dentcal vendor offerng only one. Ths result combned wth the sgnfcant cross-product swtchng cost llustrate the mportance of broad product lnes n ths market. As noted earler, new product ntroducton can have nterestng effect on the swtchng costs faced by buyers. A new IT platform may erase ncumbent advantages, resultng n lower swtchng costs. Hypothess 5 argues that swtchng costs arsng from a buyer s nstalled base of routers wll be lower f buyers take advantages of the new IT nnovatons n swtches, whch usually requres the use of routers and swtches as complementary products n the network. We examne the dfference n swtchng cost parameter estmates across model nests to dentfy the effects of new IT nnovatons on swtchng costs arsng durng router vendor choce. The results on swtchng cost estmates for each type of purchase are gven n Table 5. Column 4 of Table 5 shows the dfference n swtchng costs when routers and swtches are purchased separately versus when they are purchased smultaneously. As before, the coeffcents measure varous sources of swtchng costs, however, we are most nterested n understandng changes n router swtchng costs due to router nstalled base when a frm redesgn ther networks to takes advantage of swtches. We fnd that swtchng costs arsng from nstalled base n routers fall when there s a possble network redesgn, captured by purchasng routers and swtches together. Ths result supports Hypothess 5. Swtchng costs due to nstalled base of routers at establshment falls from to , a statstcally sgnfcant drop (at the 5 percent level) of Ths sgnfcant drop n swtchng costs greatly reduces the ncumbent advantage, as shown n Fgure 4. For example, the lkelhood that an establshment wth 3Com routers 21

24 wll purchase 3Com routers agan drops from 42% to 26% when the buyer purchases swtches concurrently wth routers. We also examned the behavor of swtchng costs arsng from frm-wde nstalled base. These fell by , a postve but statstcally nsgnfcant dfference. 6. Dscusson Swtchng costs n the market for routers and swtches: Overall, all of our hypotheses are supported (as summarzed n Table 6), suggestng that there exst sgnfcant swtchng costs of varous sources n the market of routers and swtches despte the prevalence of open standards whch attempts to ncrease nteroperablty of network equpments from dfferent vendors. What effect dd the ntroducton of swtches have on swtchng costs n the market for LAN equpment? We set out two competng hypotheses of how the ntroducton of swtchng technology mght affect swtchng costs n the LAN equpment market. One hypothess argued that swtchng technology mght free ncumbent users from swtchng costs by ntatng a platform shft to swtchbased networks. A second hypothess argued that because of the mportance of cross-product compatblty n network equpment, new swtch purchases must reman compatble wth the exstng nstalled base of routers. Our results support both vewponts. In the short run, the ntroducton of swtches dd lower swtchng costs arsng from the nstalled base of routers. We showed that establshments purchasng swtches for the frst tme often made complementary router nvestments to optmze network performance n lght of the new swtchng technology. We provded some evdence that because these new swtch nvestments forced establshments to redesgn and rebuld ther network nfrastructure, they effectvely freed them from the swtchng costs arsng from ther nstalled base. Thus, a short wndow exsted after the ntal ntroducton of swtches that reduced buyer swtchng costs for routers. In the long run, however, the mportance of compatblty n ths market effectvely ensured the swtchng costs would contnue to play a role n buyer decsons. Cross-product swtchng costs ted buyers to nvestments made n other products and other establshments wthn the organzaton. Shoppng costs drove buyers to purchase from ncumbent vendors wth broad product lnes n routers and swtches. 22

25 Thus, n the long run the fundamental ssues of compatblty n these products tended to reassert themselves. Indeed, our results suggest that new nnovatons may rase market concentraton f a prolferaton of new products forces vendors to become a one-stop shop for many dfferent varetes of networkng equpment; the fxed costs of development wll become a barrer to all but the largest vendors. These results are applcable to many IS hardware and software products, ncludng software (Brynolfsson and Kemerer 1996; Breuhan 1997), computng hardware (Bresnahan and Greensten 1999), and networkng protocols (von Burg 2001; Gawer and Cusumano 2002). However, ours s one of the frst papers to examne ths phenomenon emprcally, and the frst to examne t wthn the context of a networked good. Implcatons for Managers: Ths paper contrbutes to an exstng lterature that shows shortrun decsons can have long-run mplcatons for buyers of IT equpment. However, we have several new lessons. For buyers, our results rase a cauton flag to optmsts who beleve that open protocols wll be the soluton to mult-vendor nteroperablty problems n computer networks. Based on buyer s observed decsons, we show that compatblty ssues stll play a maor role n computer networks. Moreover, our results show that the tradtonal legacy problem n IS wll be exaggerated as nformaton systems move ncreasngly toward mult-ter clent/server platforms. Our research has tested pror assertons that new technologes may temporarly reduce buyer swtchng costs. However, our results show that ths wndow may be fleetng, and that contnued new product ntroductons n the networkng market may work to ncrease market concentraton. For sellers, our research explctly addresses the mportance of broad product lnes to vendor success n networked markets. We show that product lne compatblty has mportant mplcatons for vendor choce, and offer evdence that suggests vendors may strategcally alter the compatblty of ther products wth other vendors to acheve compettve advantage. 7. Conclusons Ths research examned the mpact of a new product ntroducton and how ths new product ntroducton nfluenced vendor choce n an envronment wth swtchng costs and network effects. 23