The Effects of Compatibility on Buyer Behavior. in the Market for Computer Networking Equipment

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1 The Effects of Compatblty on Buyer Behavor n the Market for Computer Networkng Equpment Chrstopher Forman Kellogg Graduate School of Management Northwestern Unversty c-forman@kellogg.nwu.edu May 2001 I thank Shane Greensten, Davd Besanko, Mke Mazzeo, and Ranjay Gulat for nvaluable gudance and comments. I thank the General Motors Strategy Center, Davd Besanko, and Shane Greensten for fnancal support n obtanng the data, and thank Mark Doms for helpful dscussons. Tm Ward and Harry Georgopolous provded helpful techncal expertse on computer networkng equpment. All remanng errors are my own.

2 Abstract Ths paper examnes the mportance of compatblty on buyer behavor n the market for computer networkng equpment over the perod One fndng s that frm establshments are more lkely to purchase networkng gear from an ncumbent vendor. Among some classes of networkng equpment, ncumbency affects vendor choce both when t occurs at the same establshment and/or at other establshments wthn the same frm. Another fndng s that compatblty across dfferent product lnes wthn the same vendor also nfluences vendor choce. These theses are explored n data on purchases of computer networkng equpment utlzng open standards such as the Ethernet networkng protocol, and represent the frst econometrc measurement of compatblty effects wthn products utlzng open standards. These fndngs show that there are strong economc ncentves to offer broad product lnes, and provde one potental explanaton for hgh concentraton levels n the market for computer networkng equpment.

3 1. Introducton Ths artcle examnes how product compatblty affects vendor choce n the market for routers and swtches, two major classes of computer networkng equpment. Analyzng the market for routers and swtches over , I examne whether ncumbency may affect vendor choce. In partcular, I test whether the presence of an nstalled base ncreases the lkelhood that a buyer wll purchase from a partcular vendor. As noted n Klemperer (1995) and Davd and Greensten (1990), compatblty between an exstng generaton of equpment and potental replacements can create swtchng costs for buyers who change vendors, gvng ncumbent vendors an advantage over nonncumbents. Some emprcal papers have examned the effects of vendor ncumbency n other settngs (e.g., (Greensten, 1993), (Breuhan, 1997)). Recently, several papers have examned swtchng costs and brand loyalty wthn the context of consumer behavor n electronc markets (e.g., (Brynjolfsson and Smth, 2000), (Chen and Htt, 2000)). Besdes examnng the effects of ncumbency n a new and growng market, ths paper wll make several contrbutons to ths exstng lterature. Frst, t wll show wthn the context of mult-establshment frms, ncumbency affects vendor choce both when t occurs at the same establshment and/or at other establshments at the same frm. Second, t wll examne how an exstng nstalled base n one product market can spll over and affect purchasng decsons n another. It wll also be the frst paper to show that nstalled base effects are present n markets wth so-called open standards. A major goal of the study s to explan how compatblty may have nfluenced market structure n routers and swtches. It s well known that the market for these products s qute concentrated. The world-wde market share for the top three frms n the router market ranged from 60.2% to 62.7% over my sample perod, whle the comparable fgure for the swtch market ranged from 61.9% to 72.3%. 1 Many explanatons have been provded n the popular press for the predomnance of the top three frms n these market segments, partcular Csco Systems. These alternatve hypotheses have ncluded, but have not been lmted to, dstrbuton technques and 1 Source: Dataquest Quarterly Market Watch,

4 acquston strateges (e.g., (Bunnell, 2000)). Whle these other hypotheses are lkely to be true, t s the hypothess of ths paper demand for compatblty contrbutes to the hgh concentraton rates n these markets. I test these hypotheses by estmatng a nested logt model of vendor choce. In ths model, the probablty of choosng a partcular vendor s made a functon of buyer characterstcs and the extent of prevous buyer-vendor nteracton. The model also accounts for the effects of smultaneous purchases across multple product lnes. I also examne explctly the factors affectng the choce of whether to purchase computer networkng equpment. The results show that compatblty consderatons do effect the vendor decson of buyers. These compatblty consderatons appear n two ways. Frst, a buyer s nstalled base n a partcular lne of equpment appears to effect future purchases n that equpment lne. Buyers show a tendency to show loyalty to a partcular vendor over tme. Second, compatblty appears to play a role n vendor decson when buyers are purchasng multple product lnes. Ths manfests tself n a buyer tendency to purchase routers and swtches from the same vendor. I measure the effects of compatblty across product lnes wthn a partcular vendor by examnng the behavor of buyers choosng more than one class of networkng gear. Prevous work (e.g., (Katz and Shapro, 1985)), (Besen and Saloner, 1989), (Davd and Greensten, 1990)) has shown that domnant frms may have ncentves to manpulate nterfaces and to create ncompatbltes wth complementary devces sold by rval sellers n an attempt to broaden market power. Because of the obvous anttrust mplcatons there have been a number of case studes that have examned ths phenomenon (e.g., (Fsher et al., 1983)). However, because of nsuffcent data there have been no econometrc studes to date that have drectly examned ths phenomenon. In ths paper, I wll be able to test drectly the mportance of compatblty wthn vendor product lnes. As wth most economc papers attemptng to measure the mportance of past economc behavor on current purchase decsons, ths paper s unable to measure drectly the mportance of nstalled base on product purchases. Buyers may contnue to purchase from the same vendor ether because of vendor lock-n or because that vendor s product s partcularly well suted to the buyer. Ths problem s another manfestaton of the 4

5 econometrc dentfcaton problem of dsentanglng the effects of state dependence versus unobserved heterogenety dscussed elsewhere (e.g., (Heckman, 1981)). Moreover, n the model that I use t wll be dffcult to dentfy whether the purchase of multple products from the same vendor s due to compatblty factors or unobserved factors leadng to a better match between buyer and vendor. The rest of the paper s as follows. In secton 2 I provde some background techncal nformaton on computer networkng equpment and provde some detals on market concentraton n ths market. In secton 3 I descrbe explctly how compatblty can affect vendor choce n a market wth open standards. Moreover, I descrbe the econometrc dentfcaton ssues nvolved n measurng the mportance of nstalled base and product-lne compatblty. In secton 4 I descrbe the econometrc specfcaton, and secton 5 descrbes the data. Secton 6 presents the results of the econometrc specfcaton. Secton 7 concludes. 2. The Market for Networkng Equpment 2.1 Technology In order to understand how compatblty can effect buyer decsons n the market for computer networkng equpment, one must frst have some understandng of the underlyng technology nvolved. At the lowest level of the networkng herarchy, networkng takes place wthn what s known as a local area network (LAN). LANs are used to connect small groups of users who are (usually) located physcally close to one another and who may often wsh to utlze a shared resource such as a prnter or some other perpheral. Two major technologes were used n the latter half of the 1990s to transmt data between LANs, routers and swtches. Both technologes are nodes that connect network cablng and route traffc across LANs n a network. Both technologes are also used to route traffc across the Internet. Routers were ntroduced n the 1980s by Csco Systems. Pror to the rse n popularty of swtches n 1994 and 1995, routers represented the prmary way n whch 5

6 networks were nterconnected n the 1990s. 2 Routers are used to drect packets of nformaton across a network. Because of the way n whch they work, however, routers have functonalty whch also enables them to montor and manage network traffc effcently. Routers are able to communcate wth one another n a way that allows the router to montor and optmze network traffc, determnng 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. However, ths added functonalty comes at a cost n the form of the addtonal tme t takes for routers to route packets. Throughout the 1990s, ncreasng network traffc straned the capactes of router-based networks. Sgnfcant delays, known n ndustry termnology as latency, developed as many router-based networks were unable to handle ncreasng traffc flows. Moreover, the prce of routers was very hgh relatve to the prces of other networkng hardware. Swtches were ntroduced n the md-1990s n part as a soluton to the cost and latency problems of routers. Lke routers, swtches are used to drect packets of nformaton across a network. Ther desgn often results n faster packet forwardng and lower hardware prces than routers, however wthout the added functonalty of routers. Because routers and swtches perform the same basc functon routng data packets they are sometmes used as substtute products. Swtchng technology dffused throughout the latter 1990s, as some network managers chose to adopt the new technology whle others preferred to mantan entrely router-based networks. Despte relatvely rapd dffuson of swtches, few adopters of swtchng technology abandoned routers entrely, however. Most buyers of swtches mantaned some routers n ther network, and most purchased routers concurrently wth ther swtches. The reason s that many of the network management and securty features of routers remaned necessary. In partcular, networks that reled entrely on swtchng technology often 2 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. 6

7 resulted n broadcast storms, a state n whch a message that s broadcast around a network results n ncreasngly more messages, generatng a snowball effect that can cause the entre network to fal. Usually, swtch-based networks nclude routers nterspersed perodcally to help manage network traffc flow. Routers, because of ther added functonalty, form the brans of the network around whch swtches and other networkng hardware were bult. Thus, routers and swtches are also commonly employed as complements. 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 qute dffcult. 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, tmeconsumng 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. 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 7

8 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 objectve of the NIA was to smplfy the buldng of networks, to create support for jont 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. 2.2 Market Structure The router and swtch markets n the second half of the 1990s have been characterzed by large and ncreasng market concentraton. Table 1 shows how the Bg Three vendors of 3Com, Bay Networks, and Csco Systems came ncreasngly to make vrtually all of the router sales to frms n our sample. 3 Sales made by the Bg Three rose from over 71.7% to over 88.1% of sales from 1996 to Sales by smaller vendors, represented by the Other category n Table 1, of course fell concomtantly, from 28.3% to under 11.9%. 3 Bay Networks was acqured n June, 1998 by Northern Telecom, whch then renamed tself Nortel Networks. Throughout the sample perod of ths paper Bay Networks operated an ndependent entty, so I have opted to use the name Bay Networks throughout the paper. 8

9 Concentraton n the market for swtches s lower, but dsplays many of the same characterstcs of the router market. Combned market shares for the Bg Three vendors of 3Com, Bay, and Csco ranged from 75.2% to 76.1%. Includng Cabletron, another major manufacturer of swtches, the market share of the top four frms ranges from 81.1% to 87.2%. Many explanatons could be potentally provded for the hgh concentraton levels n the market for computer networkng gear. The popular busness press (e.g. (Bunnell, 2000) has emphaszed frm-specfc capabltes as one explanaton for the hgh market concentraton. Though I do not dspute the potental of these alternatve explanatons, n ths paper I hypothesze that ncompatbltes across the products of dfferent vendors exhbted through lock-n and demand for non-hybrd systems has helped lead to ncreases n concentraton n the router market. 3. Compatblty n Networkng Equpment Ths secton descrbes how compatblty can nfluence both ndvdual vendor purchase decsons and overall market structure. It also descrbes the econometrc ssues I face n dentfyng the effects of vendor ncumbency and cross-product compatbltes. Klemperer (1995) descrbes how a buyer s desre for compatblty between exstng systems and new purchases can lead to swtchng costs n changng vendors. Such swtchng costs can cause buyers to exhbt brand loyalty and so ncrease the lkelhood of repeat purchases from ncumbent vendors. Klemperer (1995) lsts several types of such swtchng costs, although n the market for computer networkng equpment they are most lkely to arse from two sources: (1) need for compatblty wth exstng equpment; and (2) costs of learnng new brands. The presence of swtchng costs can confer sgnfcant monopoly power upon the vendor among those buyers who have prevously purchased the vendor s product. Klemperer (1995) notes that one potental outcome s for ncumbent frms to charge hgher prces than mght otherwse be the case. Roughly speakng, vendors have the ncentve to rase prce n order to explot monopoly power among those buyers over whch they have ncumbency. 9

10 Klemperer (1995) also notes the potental effects of swtchng costs n multproduct competton. If buyers value varety and prefer to purchase systems consstng of multple components and f there are swtchng costs to purchasng products from dfferent vendors, then vendors that sell sngle products only may be at a dsadvantage to those who produce a full product lne and who can enable buyers to avod swtchng costs n mult-product purchases. These models lead us to expect several patterns of buyer behavor. The frst hypothess s that we expect buyers wth an nstalled base of routers at the ste to be more lkely to purchase new routers from the ncumbent vendor. Smlarly, we expect swtch ncumbency at the ste to affect a buyer s choce of swtch vendor. The swtchng costs of learnng to manage new equpment of and ensurng nteroperablty wth the current network wll persuade many buyers to contnue wth the ncumbent vendor. Hypothess 1: Buyers face costs n changng router vendors. Incumbency wll ncrease the probablty a buyer wll purchase from a router vendor relatve to an dentcal buyer wthout ncumbency. Hypothess 2: Buyers face costs n changng vendors. Incumbency wll ncrease the probablty a buyer wll purchase from a swtch vendor relatve to an dentcal buyer wthout ncumbency. When buyers that are part of mult-establshment frms purchase networkng equpment they must be concerned not only wth the nstalled base of equpment locally at the buyer s establshment but also wth the nstalled base of networkng gear used throughout the frm. Networkng gear must be compatble not only wth local gear but must also nteroperate wth networkng equpment used throughout the frm. Installaton and management of networkng equpment may also be provded by personnel from corporate headquarters or from other establshments wthn the frm. Thus, our second hypothess s that nstalled base external to the ste but wthn the frm wll nfluence vendor choce. 10

11 Hypothess 3: Buyers face costs n choosng a router vendor dfferent from that used at other establshments throughout the same frm. Incumbency throughout the frm wll ncrease the probablty a buyer wll purchase from a router vendor relatve to an dentcal buyer wthout frm-wde ncumbency. Hypothess 4: Buyers face costs n choosng a swtch vendor dfferent from that used at other establshments throughout the same frm. Incumbency throughout the frm wll ncrease the probablty a buyer wll purchase from a swtch vendor relatve to an dentcal buyer wthout frm-wde ncumbency. The next two hypotheses relate to the effects of compatblty across products wthn the same vendor. Hypothess fve says that nstalled base n routers wll nfluence the choce of swtch vendor. Hypothess 5: Buyers face swtchng costs n choosng dfferent router and swtch vendors. Incumbency n routers wll ncrease the probablty a buyer wll purchase from a swtch vendor relatve to an dentcal buyer wthout router ncumbency. Along the same lnes, the costs of swtchng supplers should nfluence vendor choce among buyers purchasng multple products smultaneously, ndependent of nstalled base effects. Hypothess 6 says that swtchng costs should ncrease the lkelhood that buyers purchasng multple products wll do so from the same vendor. Hypothess 6: Buyers face swtchng costs n choosng dfferent router and swtch vendors. Buyers purchasng routers and swtches smultaneously wll face costs of purchasng from dfferent vendors. One way of examnng the mportance of nstalled base s to do smple unvarate analyss and examne the loyalty of buyers to vendors. Tables 3 and 4 provde some evdence of hypothess 1. Table 3 presents statstcs on loyalty rates for routers over the sample perod. A loyalty rate shows the condtonal probablty of purchasng from an 11

12 ncumbent vendor. Statstcs are calculated for each of the Bg Three vendors of 3Com, Bay Networks, and Csco. Because of sample sze restrctons, all other vendors are grouped wthn the class Other. The columns Other, Same Vendor and Other, Dfferent Vendor represent buyers that purchased from an ncumbent and nonncumbent Other vendor, respectvely. For each of 3Com, Bay Networks or Csco, the probablty of purchasng from the ncumbent vendor s qute hgh, approachng 50% for 3Com and Bay Networks and exceedng 80% for Csco systems. Loyalty rates for smaller vendors were much lower, as many buyers who purchased from such vendors swtched to Csco over the sample perod. Table 4 shows statstcs on loyalty rates for swtches. Loyalty rates are hgh n the table for each of the Bg Three as well as the Other category. Loyalty rates for the Bg Three vendors hover near 70%. A sgnfcant dfference between ths table and Table 3 s for the Other vendors: loyalty rates for other vendors are near 60% for swtches, and there s no wdespread swtchng to Csco as there was n the router tables. 4 Of course, these loyalty tables do not prove the exstence of swtchng costs and lock-n. Hgh loyalty rates may smply ndcate a good match between buyer and vendor. If a partcular vendor has repeatedly excelled n provdng systems that meet a buyer s needs, then the ncrease n condtonal probablty smply represents an unobserved preference on the part of the buyer for the vendor s dosyncratc features. The problem of determnng whether such an ncrease n the condtonal probablty s due to a change n preferences or constrants (state dependence) or smply represents unmeasured varaton n the subjects (unobserved heterogenety) has been studed extensvely (e.g., (Heckman, 1981), (Heckman, 1991)). Due to strngent data requrements, few papers n the Industral Organzaton lterature have been able to solve ths problem explctly. Israel (1999) s a notable excepton. Throughout ths paper I wll requre the mantaned assumpton that heterogenety across frms s suffcently controlled for by my regressors. 4 Although ths partally reflects hgh loyalty for Cabletron swtches. 12

13 Table 5 shows the dstrbuton of vendor choces for buyers that purchase from one router and one swtch vendor, and provdes some evdence for hypothess 4. 5 The table shows that among frms who purchase from one router vendor and one swtch vendor, roughly 46% buy from the same vendor. The second lne of each row n the table shows the probablty of purchasng from a gven swtch vendor condtonal on purchasng from the router vendor n that row. Thus, 89.3% of buyers purchasng a 3Com router also buy a 3Com swtch, whle 72.8% of stes purchasng a Bay router wll buy a Bay swtch. Interestngly, only 38.4% of stes purchasng a Csco router wll also purchase a Csco swtch, however ths fgure must be nterpreted cautously because of the large Csco market share n routers. Agan, I am unable to determne drectly from Table 5 whether compatblty ssues are drvng the purchase behavor of stes n my sample. Whle one potental hypothess s that buyers are more lkely to purchase from dentcal router and swtch vendors because of compatblty ssues, other alternatve hypotheses are possble. In partcular, t may be the case that there are unobservable factors unrelated to compatblty that are drvng a buyer to purchase from the same networkng vendors for routers and swtches. In partcular, there remans the possblty that the bundles of routers and swtches offered by some vendors are a better match for some buyers than others. For nstance, the product lne of 3Com s known to cater partcularly to smaller frms, thus there may exst the possblty that smaller buyers are more lkely to purchase both 3Com routers and swtches. In sum, there exsts some anecdotal and smple statstcal evdence that product compatblty ssues play an mportant role n the vendor choce decson. However, there may be other factors at work affectng vendor choce, some relatng to product compatblty and some not. A more formal framework s needed to account for buyer heterogenety as much as possble, as well as to explctly dentfy the assumptons needed to attrbute the behavor dentfed above as beng caused by compatblty. 5 Results from frms purchasng from multple router and swtch vendors are qualtatvely smlar, however are dffcult to dsplay n tabular form. 13

14 4. Structure of the Model In ths secton I detal the model used to descrbe the demand for routers and swtches. I use ths model to explctly examne the decsons of (1) whether or not to purchase a router or swtch and (2) condtonal on the purchase decson, the choce of vendor. The model s derved from a dscrete choce model of buyer behavor (e.g., (McFadden, 1974) or (McFadden, 1981)). The model examnes the purchase decsons of buyers over the course of one year. Buyers n ths model are assumed to be ndvdual stes wthn a frm, where a ste represents a geographc locaton wthn the frm and can be vewed as beng smlar to establshments n government statstcal data. Thus, a mantaned assumpton throughout the paper wll be that the decson-makng process for purchasng networkng equpment s decentralzed across frms n my sample. 6 All stes assocate some utlty wth a choce j, U j. Utlty takes the form of a random utlty model (e.g., (McFadden, 1974)), U = u + ε. Thus, a ste s utlty for a j j j choce s decomposed nto two components: a determnstc component u j that s a functon of ste as well as choce characterstcs and also ncludes nformaton on prevous vendor-ste nteracton. The error term of unmeasured varables. ε j s a resdual that captures the effects A choce n the model conssts of (1) a 0/1 decson of whether to purchase a router; (2) a 0/1 decson of whether to purchase a swtch; (3) f a router s purchased, a choce of router vendor; and (4) f a swtch s purchased, a choce of swtch vendor. Buyers choose a router or swtch vendor rather than a partcular model of networkng equpment because of lmtatons wth the data set. For most router observatons and all swtch observatons, the data dentfy vendor only and do not dentfy a partcular model type. Purchasers of networkng gear frequently purchase more than one unt of routers and/or swtches from a vendor. Because I am more concerned wth the ssue of vendor choce than wth the quantty of networkng equpment actually demanded, I do not 6 Ths hypothess s consstent wth that made by prevous users of ths data source, ncludng Bresnahan and Greensten (1996), Bresnahan and Greensten (1997), and Breuhan (1997). 14

15 consder the quantty decson here. Accordngly, I make the necessary assumpton that the quantty decson s separable from the purchase and vendor decsons. Because most stes n my data set purchase small quanttes of routers and swtches, ths assumpton s less problematc than t may frst seem. For example, among purchasers of routers, 37.8% of stes purchase one router, and 78.7% purchase fve of fewer. Among purchasers of swtches, 36.9% purchase one swtch whle 75.7% purchase fve or fewer. As n Goldberg (1995), I decompose a decson j nto k dsjont subsets accordng to the decson to purchase a router (r), the decson to purchase a swtch (s), the choce of router vendor (v), and the choce of swtch vendor (w), so that each choce j can be ndexed by a quadruple subscrpt (r,s,v,w). Then the utlty functon can be expressed as U = u + ε rsvw,,, rsvw,,, rsvw,,, As n Goldberg (1995) I assume that utlty s addtvely separable nto components that vary wth the decson to purchase a router, the decson to purchase a swtch, the choce of router vendor, and the choce of swtch vendor. Under these assumptons the utlty functon can be wrtten as where αβγ,,, and r rs, rsv,, rsvw,,, U = α R + β S + γ V + δ W + ε j r rs, rsv,, rsvw,,, rsvw,,, δ represent parameters to be estmated and the vectors R, S, V, and W represent varables affectng the decson to purchase a router and swtch and the decsons of router and swtch vendor, respectvely. Followng the lterature on the nested logt model (e.g., (McFadden, 1978), (McFadden, 1981)), I assume that the error term ε rsvw,,, follows a generalzed extreme value dstrbuton. I further assume that the decson process can be nested accordng to Fgure 1. A potental problem wth use of the nested logt model s that the order n whch decsons are nested determnes the error process and can affect estmaton of the coeffcents n the model. The nestngs n the model do not descrbe the order of the decson process, and nstead specfy the structure of the error terms n the model: choces wthn a branch are more smlar to one another than are choces outsde of a branch. The structure of Fgure 1 was used because routers often form the core of a network around whch other hardware s bult: thus a nestng structure whch assumed choces condtonal on a partcular router decson to be more smlar than those that ncluded a dfferent 15

16 router decson was preferred. Fgure 2 presents an alternatve nestng structure. We later use mplcatons of the generalzed extreme value dstrbuton derved by McFadden (1978) to test whether our nestng structure s consstent wth utlty maxmzaton. It wll be convenent to decompose further the vector of characterstcs V and W. Followng Greensten (1993), I decompose ths vector of characterstcs rsv,, rsvw,,, nto varables that descrbe the extent of buyer-vendor nteracton and varables measurng buyer characterstcs that may ndcate vendor preference (ndependent of prevous buyer-vendor nteracton). In partcular, we can defne γ γ γ Vrsv,, = 1 Xrsv,, + 2 v Zrs, where rsv,, X measures the extent of prevous buyer-vendor nteracton and Z rs, measures buyer characterstcs ndependent of vendor nteracton that may sgnal predlecton towards a partcular vendor. Further, I rewrte δ δ δ δ Wrsvw,,, = 1 Prsvw,,, + 2 wqrsv,, + 3 Trsvw,,,, where rsvw,,, P measures the extent of prevous buyer- vendor nteracton, Q rsv,, measures buyer characterstcs that may ndcate preferences for a partcular vendor (ndependent of prevous nteracton), and T,,, ndcates the potental benefts or costs of makng a choce that ncludes an dentcal router and swtch vendor. The vectors Xrsv,, and P rsvw,,, reflect the mpact of prevous buyer-vendor nteracton on the utlty of purchasng from a partcular router and swtch vendor, respectvely. We may expect the effects and sources of buyer-vendor nteracton to dffer across standalone stes and those that are part of a larger frm. For nstance, stes that are drectly connected to the broader network of a large frm may base ther vendor decson n part on the nstalled base of equpment throughout the frm, mplyng both that (1) frm-wde nstalled base wll affect decsons and potentally (2) ste-wde nstalled base effects may be stronger or weaker for stes whch are part of a larger frm than they are for standalone stes. To account for ths, I agan rewrte γ V = ηγ X + (1 η ) γ X + γ Z f nf rsv,, f 1 rsv,, f 1 rsv,, 2 v rs, rsvw 16

17 where η f s defned to be 1 f the ste s part of a larger frm and 0 otherwse and so allow the effects of buyer-vendor nteracton to vary dependng on whether a ste s part of a larger frm. Smlarly, δ W = ηδ P + (1 η ) δ P + δ Q + ηδ T + (1 η ) δ T where f nf f nf rsvw,,, f 1 rsvw,,, f 1 rsvw,,, 2 w rsv,, f 3 rsvw,,, f 3 rsvw,,, The jont probablty of a partcular choce j n ths model wll be P = PP P P j r sr vsr, wvsr,, P j s the jont probablty of choosng a partcular (r,s,v,w) combnaton, represents the margnal probablty of purchasng a router, P sr s the probablty of purchasng a swtch condtonal on router choce, P vsr, s the condtonal probablty of purchasng from a partcular router vendor, and P wvsr,, s the condtonal probablty of purchasng from a swtch vendor. The generalzed extreme value dstrbuton mples that gven choces (r,s,v), the condtonal probablty of makng a choce of swtch vendor P r * w takes the followng form: P wvsr *,, = exp( δ W ) (1) exp( δ ) w Cvsr,, rsvw,,, * Wrsvw,,, where C vsr,, denotes the set of choces avalable to the buyer at the node defned by (v,s,r). At the next level up, the probablty of choosng router vendor * v wll be P * vsr, = γ V + λi v Crs, * * rsv,, rsv,, Vrsv,, + λirsv,, γ (2) where I = log exp( δ W ) s the nclusve value, the expected aggregate rsv,, rsvw,,, w Crsv,, value of choce v. The coeffcent on the nclusve value, λ, measures the dssmlarty of alternatves avalable to the buyer gven dfferent choces v. McFadden (1978) has shown that the choce structure gven by Fgure 1 s consstent wth expected utlty maxmzaton f and only f the nclusve value parameter n (2) les wthn the unt nterval. 17

18 The probablty of a choce at a node n the frst or second level of Fgure 1 s smlar n form to (2). In the second level decson, the probablty of a partcular choce of whether to buy a swtch wll be equal to β S + ξi * * rs, rs, sr = β Srs, + ξirs, r Cr P (3) where the nclusve value Irs, = log γ Vrsv,, + λirsv,, v Crs,. Last, the probablty of a partcular choce at the frst stage of the decson tree n Fgure 1 s equal to α R + θi * * r r r = α Rr + θir s C P (4) and the nclusve value Ir = log β Srs, + ξirs,. s Cr I estmate the model usng sequental maxmum lkelhood. In ths method, I frst estmate model (1). I use the estmates of ths model n ths stage to calculate the nclusve values needed to estmate (2) usng maxmum lkelhood. The parameters n ths stage are used to calculate nclusve value parameters for (3), and so on. It s well known that ths method ensures consstent, but neffcent, estmates of the parameters. McFadden (1981) shows how to adjust the standard errors when usng ths procedure. 5. Data To carry out the vendor choce analyss, I use data from the Harte Hanks CI Technology Database. The CI Technology Database s a comprehensve survey of the technology usage of frms. It ncludes data on purchases of computers, networkng equpment, and other offce equpment, as well as data on phone usage and general descrptve frm data. In secton 5.1 I descrbe the sample that I use for the analyses. In secton 5.2 I descrbe the regressors. 5.1 Sample 18

19 I 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 sze, ndustry, and locaton; (2) technology purchases of computers, networkng equpment, prnters, and other offce equpment; and (3) contact nformaton on IT professonals at the ste. Harte Hanks obtans these dfferent components of the CI database at dfferent tmes of year; my sample s assembled by obtanng the most current nformaton as of December of each year. For example, the observaton for a ste n 1995 wll contan nformaton on the ste s characterstcs and technology usage as was recorded n the CI database n December A unt of observaton n the CI database s a ste. Roughly speakng, a ste refers to a partcular branch or locaton of a frm. It s smlar to the concept of establshment used by government organzatons such as the Bureau of Labor Statstcs n calculatng government statstcs. Thus, the database wll often have data on multple stes for a gven frm. To keep the analyss of manageable sze, I 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). These ndustres were selected because they are generally regarded as heavy users of nformaton technology and are thought to be heavy users of Internet servces. The sample contans data on all stes of over 100 employees from the CI database over the sample perod. Thus, the analyss wll not consder the effects of small ste behavor on the router and swtch ndustres. All stes are from the U.S. A unt of observaton n the database contans ste characterstcs and the stock of technology goods nstalled by the ste as of December of each year. To nfer purchase decsons, I calculate the change n quantty nstalled from year to year for each vendor. Unfortunately, the database does not contan relable model-level nformaton on networkng products n use at the frm. Thus, I am nether able to examne model-level purchase decsons nor am I able to track the purchase and retrement of a partcular pece of networkng equpment by the frm. 19

20 Because of the way the database was constructed, many observatons had to be dropped. The sample began wth 18,870 observatons n 1996, 22,439 n 1997, and 18,726 n Many observatons had to be dropped because a ste was not n the database n the prevous year, and so nferences on purchase quanttes could not be made. I drop observatons for whch there s no general ste-specfc nformaton on such thngs as company name and ndustry. Observatons were dropped for whch Harte Hanks dd not update ther networkng database from the prevous year, because such stes may have made purchases that I could not observe. Observatons were also dropped for whch the quantty of networkng equpment nstalled was mssng from the database. I also removed European stes whch were provded to me n the database. For many smaller vendors n the database, the number of purchase observatons was too small to estmate the parameters γ 2v and δ 2w. Thus, observatons representng purchases from smaller vendors had to be dropped. Moreover, because of the way ths experment was desgned, stes who had an nstalled base n one of these smaller vendors were dropped as well. In the end, I examned the vendor choce decson of frms that purchased routers from 3Com, Bay Networks, and Csco and that purchased swtches from 3Com, Bay Networks, Cabletron, and Csco. The fnal data set whch I use for analyss contans 8077 observatons from 1996, 8301 observatons from 1997, and 11,710 observatons n Varables In ths secton I descrbe the varables that I use n my analyss. Table 6 lsts the means, standard devatons, mnmums, and maxmums for the sample. As was dscussed n secton 4, there are four classes of varables used n the nested logt regresson. Each set of varables maps to a level n the nested logt model. I consder each group n turn. A. W rsvw,,, : Varables affectng the swtch vendor decson The varables IRTR and ILSW are dummy varables ndcatng that the ste has an nstalled base of routers and swtches from a partcular vendor. These varables wll measure the mportance of ncumbency at the ste level. If prevous buyer-vendor nteracton has an mportant effect on the vendor choce decson, then we expect that the coeffcents on these varables to be postve. As was noted above, these varables can not 20

21 drectly test the effects of tenure dependence on demand because of the potental presence of unobserved buyer heterogenety. The varables PCLSW and PCRTR are defned as the percentage of a partcular vendor s swtches and routers nstalled throughout a frm. Thus, these varables test whether frm-wde (as opposed to ste-wde) nstalled base effects are mportant. If frmwde nstalled base effects are mportant, we should expect the coeffcents on these varables to be postve, however I wll be unable to attrbute a postve sgnfcant coeffcent drectly to tenure dependence because of potental unobserved heterogenety. The varable RSCOMP measures potental effects of compatblty wthn a vendor s product lne. RSCOMP s an ndcator varable whch s one when a choce ncludes the same router and swtch vendor. If product compatblty across routers and swtches s mportant to buyers when makng a vendor choce, then we should expect the coeffcent on ths varable to be postve f there are costs to havng dfferent router and swtch vendors. I wll be unable to attrbute a postve coeffcent on RSCOMP drectly to compatblty effects, because there may be unobserved factors affectng whether a buyer chooses an dentcal router and swtch vendor. I addtonally nclude a vector of factors to account for the effects of buyer heterogenety on vendor choce. We expect that for a varety of reasons some vendors may be a better match wth certan stes. For example, the product lne of 3Com s known to be talored to the edge of the network, and s most commonly used for small frms and branch offces. Thus, smaller stes or stes from smaller frms may be more lkely to purchase 3Com equpment. To account for the effects of ste sze and network complexty on vendor choce, I nclude varables on number of network nodes (TOTNODES) and number of network protocols (TOTPROT). Because of the reduced-form nature of my dscrete choce model, t s dffcult to say a pror what we should expect the sgn of these coeffcents to be. We may expect second-tme buyers to have dfferent vendor preferences that frsttme buyers. Ths may be due, for nstance, to the fact that second-tme buyers may be more techncally sophstcated that frst-tme buyers and so more nterested n obtanng techncally superor equpment than n product compatblty. To capture the dfference n 21

22 behavor between frst-tme and second-tme buyers, I nclude the dummy varable STIME to ndcate second-tme buyers. The varable DHQ ndcates whether a ste s a frm-wde or regonal frm headquarters. Ths varable s ncluded to capture the fact that headquarters stes may be lkely to purchase and use dfferent types of networkng gear than are branches. A headquarters ste wll often be the center of a corporate network, and so wll carry a large load of network traffc. Such stes wll lkely requre hgh-end routers and swtches, and so may have dfferent vendor preferences than other stes. Because Csco s known n partcular to target the network core, we may expect such stes to be more lkely to purchase from Csco. I also nclude vendor-specfc dummes DBAY, DCAB, and DCIS (a vendor-specfc dummy for 3Com s omtted). These varables are ncluded to capture the effects of unobserved product qualty and prce for each vendor. B. V rsv,, : Varables affectng the router vendor decson A number of varables ncluded n V rsv,, were also elements n W rsvw,,,. In other words, a number of varables are ncluded at multple stages of the nested logt model. Includng varables at multple levels of the model mples those varables affect the router vendor decson n two ways. Frst, such varables affect router vendor choce through the nclusve value term, representng the aggregate expected utlty obtaned from the router decson. Second, such varables affect the router vendor choce decson drectly as elements mpactng the utlty obtaned from a partcular router vendor choce. The varable IRTR s agan a dummy varable ndcatng that the ste has an nstalled base of routers from a partcular vendor. If prevous buyer-vendor nteracton has an mportant effect on the vendor choce decson, then we expect that the coeffcent on IRTR to be postve. PCRTR s agan the percentage of a partcular vendor s swtches and routers nstalled through a frm. If frm-wde nstalled base effects are mportant, we should expect the coeffcents on ths varable to be postve. The varables TOTNODES, TOTPROT, STIME, and HQ are agan ncluded as controls. Ther nterpretaton wll be smlar as n W rsvw,,,. I also nclude the ndcator 22

23 varables DBAY and DCIS (a vendor-specfc dummy for 3Com s omtted). These varables are ncluded to capture the effects of unobserved product qualty and prce. C. S rs, : Varables affectng the decson to purchase a swtch We should expect varables on frm and network sze and network complexty to affect a frm s choce of whether to adopt swtchng equpment. Because the vast majorty of stes that acqured swtches purchased ther frst model after 1995, the varables n S rs, can be nterpreted as factors affectng the swtch adopton decson. Thus, we should expect varables commonly used n adopton studes (e.g. (Bresnahan and Greensten, 1996), (Augereau and Greensten, 2000)), such as locaton and ndustry effects, to potentally affect the decson to purchase a swtch. We should expect the lkelhood of a frm adoptng swtchng equpment to ncrease wth the sze of the network. When they were orgnally ntroduced, swtches were orgnally haled as a way of allevatng the network congeston problems nherent n large and complex networks. 7 Thus, I nclude varables on total number of network nodes at the ste (TOTNODES) and total number of data connectvty lnks to ponts outsde the ste (TOTDATA) to capture the effects of network sze on the swtch adopton decson. We also expect that the sze of a frm s nstalled base n routers and swtches to also affect the lkelhood of purchasng swtchng technology. There are two reasons for ths. Frst, a larger nstalled base of routers and swtches wll ndcate a larger network, whch wll ncrease the lkelhood of purchasng swtches. Second, swtches were commonly used as replacements for routers and hubs, thus any smple model of nvestment would suggest that a larger nstalled base of hubs and routers should ncrease the lkelhood of 7 Swtches represented an mprovement over prevous generaton nternetworkng devces, namely hubs and routers, for a number of reasons. Network congeston rses rapdly wth sze for networks that rely heavly on hubs, as hubs broadcast data packets to all nodes connected to the hub (rather than drectng the packet only on to one node, as necessary). Although routers can drect data packets to only one node and are capable of fndng the most effcent path possble for a packet of data, ther technology nvolves sgnfcant overhead that can ntroduce delays n messages sent over large networks. 23

24 swtch purchases n any gven year. 8 Accordngly, I nclude the varables INSTRTR and INSTHUB, whch represent the ste s nstalled base of routers and hubs, respectvely. As network sze grows, the ste wll requre addtonal nternetworkng devces to connect the expandng network. Stes may choose to fll ths nternetworkng need wth swtchng technology. Accordngly, I nclude varables capturng the change n network sze n the model. TOTNODES captures the change n the number of nodes at the ste. We expect frms that rely more heavly on advanced, bandwdth-ntensve networkng technologes as beng more lkely to purchase swtchng technology. The varables DHOME, DINTRANET, and DRESEARCH ndcate the exstence and/or use of a homepage, ntranet, and Internet research wthn the frm, respectvely. The varables DFETHER and DFDDI ndcate the usage of fast ethernet and FDDI technology at the ste. These varables capture the presence of more advanced networkng technology at the frm, whch wll ncrease the lkelhood of swtch adopton for two reasons. Frst, the usage of advanced networkng technologes wll mply hgh bandwdth usage whch wll ncrease the probablty of the ste adoptng new swtchng technology to route the expected heavy network traffc. Second, f the ste s an advanced user of technology, work from other adopton studes (for many example see Rogers (1995)) suggests that the ste wll be an early adopter of swtches because of a potental propensty for early adopton of nnovatve technologes. Because the data are pooled over the perod , I nclude the tme dummes D97 and D98 to capture the effects of changes n the pattern of networkng equpment purchases over tme. The varables DBANK and DSERV capture ndustry effects of frms n bankng and servce ndustres (ndustry effects from the publshng ndustry are omtted). Headquarters stes may be more lkely to be the center of a frm s network and so have partcularly heavy traffc loads. To account for ths effect, I nclude the varable DHQ, whch ndcates whether the ste s a frm-wde or regonal headquarters. 8 A true model of nvestment would account for the age of captal nstalled at the ste (e.g., (Ito, 2000)). Unfortunately, the data do not allow me to dentfy the age of networkng equpment nstalled. 24

25 D. P rsvw,,, : Varables affectng the decson to purchase a router We expect that many of the same varables that affected the swtch purchase decson to affect the router purchase decson as well. The varable INSTRTR agan ndcates the sze of the nstalled base of routers at the ste, and TOTNODES agan ndcates the change n the number of network nodes. Routers are used especally to connect dssmlar networks together, thus the greater the ste s network segmentaton and external lnkages between the ste and other networks, the more lkely s the ste to purchase routers. I nclude the varables TOTSITE (number of stes n the frm 9 ), TOTLAN (total number of network nodes at the ste), TOTPROT (total number of network protocols at the ste), and TOTDATA (total number of external data lnks from the ste) to account for these affects. Once agan, I expect frms that rely more heavly on advanced, bandwdth-ntensve networkng technologes as beng more lkely to purchase swtchng technology. The varables DHOME, DINTRANET, and DRESEARCH ndcate the exstence of a homepage, ntranet, and Internet research at the frm-level, respectvely. The varables DFETHER and DFDDI ndcate the usage of fast ethernet and FDDI technology at the ste. To account for year effects, I agan nclude the varables D97 and D98, and to account for ndustry effects I nclude the varables DBANK and DSERV. I agan nclude the varable DHQ to account for headquarters effects. 6. Results The model of networkng gear choce s estmated n four stages, each of whch corresponds to a level of the tree n Fgure 1. The model was also estmated accordng to the alternatve nestng structure shown n Fgure 2, however the nclusve value parameter for the second node (decson to purchase a router) was wth a standard error of Ths nclusve value s outsde the zero to one range consstent wth utlty 9 Actually, ths varable ndcates the number stes that the frm has n my database n that partcular year, and so undercounts the number of stes n total. 25

26 maxmzaton (McFadden, 1978), however I am just barely unable to reject at the 10% level the hypothess that the true nclusve value parameter s less than or equal to one. Based on ths evdence and because there s strong pror evdence that the error structure consstent wth Fgure 1 s the correct one for ths model, I present the results from the baselne model of Fgure The parameter estmates and standard errors of the baselne model are captured n Tables 7 10 below. To ncrease the sample sze, I pool observatons over the entre sample The estmaton results of each stage are lsted below. A. Results from Swtch Vendor Choce Model The results from the model of swtch vendor choce are presented n Table 7. I focus attenton prmarly on the varables measurng the mportance of prevous buyer-vendor nteracton and product compatblty. The presence of an ncumbent swtch vendor appears to have an mportant effect on a buyer s decson of swtch vendor, provdng support for hypothess 2. If ether a standalone ste or frm branch has an ncumbent swtch vendor (ILSW), then that ste s lkely to purchase from the same vendor agan. The coeffcents on the ste-level ncumbency varables are hgh and sgnfcant for both standalone stes and frm branches. Frm-wde ncumbency may also play a role n the swtch vendor decson, as shown by the varable PCLSW, however the effects are weaker and just barely nsgnfcant at the 10% level. Thus, the coeffcent estmates provde some support for hypothess 4. Hypothess 5 suggests that the presence of an ncumbent router vendor may also have an effect on the swtch vendor decson. Prevous nteracton wth a vendor s routers may be mportant n choosng a swtch vendor f compatblty across routers and swtches s an ssue. The effect of router vendor ncumbency at a ste (IRTR) s sgnfcant, however s less powerful than the effect of swtch ncumbency. Frm-wde router ncumbency (PCRTR) appears not to be mportant n determnng a frm s swtch vendor, however. Because frm branches are most lkely to connect to other stes and to 10 The estmaton results from the alternatve model of Fgure 2 were also fully consstent wth hypotheses

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