Switching Costs, Network Effects, and Buyer Behavior in IT Markets

Pei-yu Chen

Tepper School of Business Carnegie Mellon University pychen@andrew.cmu.edu

Chris Forman

College of Management Georgia Institute of Technology chris.forman@mgt.gatech.edu

March 2008

Abstract

This research examines how local network effects contribute to the costs of switching vendors, and how such effects differ according to the organization of the IT function. Our results indicate that the costs to ensure compatibility among local area network devices is a major source of switching costs in the markets for routers and switches, and that switching costs increase with the size of a buyer’s installed base. Moreover, we show that when IT purchase decisions are decentralized, the effect of IT vendor choice on other units within the same firm are discounted. We also study how changes in network architecture lower switching costs by reducing the importance of prior complementary investments in learning and configuration on vendor choice. While we show that the introduction of switches may have temporarily reduced switching costs for router buyers investing in switches, the costs of switching vendors remains significant.

We thank Rajiv Banker, Mark Doms, Avi Goldfarb, Shane Greenstein, Lorin Hitt, Mike Mazzeo, Sandy Slaughter, Michael Smith, and seminar participants at Carnegie Mellon, Singapore Management University, Temple University, the University of California-San Diego, the University of Minnesota, and the 2002 International Conference on Information Systems for helpful comments and suggestions. Tim Ward and Harry Georgopolous provided helpful technical expertise on computer networking equipment. We thank the General Motors Strategy Center and the NET (Networks, Electronic Commerce, and Telecommunications) Institute for financial support, and Harte Hanks Market Intelligence for providing essential data. All errors are our own.

1

1. Introduction

Switching costs play a large and increasing role in competition and strategy in information

technology markets and other information-intensive industries. Profitability in these markets is driven by

the size of the customer base and customer retention, which is at least partly determined by switching

costs. As noted by Shapiro and Varian (1999):

“You just cannot compete effectively in the information economy unless you know how to identify, measure, and understand switching costs and map strategy accordingly.” (p. 133)

Network effects also play a major role in IT-using businesses and play a fundamental role in

shaping business strategy in the IT-producing industries (Shapiro and Varian 1999; Dhar and

Sundararajan 2007). Recent theoretical work has also shown that the combination of switching costs and

network effects can have a significant impact on outcomes in IT-producing and using industries (Lee and

Mendelson 2007; Viswanathan 2005).

While switching costs and network effects co-exist in many markets, empirical research in

information systems (IS) and economics has told us relatively little about how they interact to determine

market outcomes. Prior empirical literature on switching costs has focused on the costs of learning new

products or the transaction costs of switching suppliers (Chen and Hitt 2002; Goldfarb 2006a; Greenstein

1993). Similarly, prior work has show that network effects can influence the adoption of new products or

technologies (Kauffman, McAndrews, and Wang 2000; Zhu et al. 2006). In this paper, we identify a kind

of switching cost that we believe is common in IT markets but has until now not received significant

attention in empirical research on switching costs and network effects. In particular, we demonstrate the

implications for buyer behavior of increasing switching costs due to local network effects (Sundararajan

2007).

We examine the effects of local network effects and switching costs on vendor choice in one

particular IT market: the market for routers and switches. We choose this market because it is large (in

1998 Dataquest estimated the size of the market to be $7.1 billion) and because we believe it is

representative of many other markets in which switching costs and network effects influence vendor

2

choice (e.g., the market for other business IT such as servers, wireless handhelds, and business application

software).

Our analysis of how network effects shape switching costs proceeds in three steps. First, we

demonstrate that switching costs are increasing in local network size. We show that the need for

compatibility between existing and legacy IT products can create significant barriers to switching vendors

(Shapiro and Varian, 1999) and such compatibility costs increase in the level of past investments. Thus,

there exists a local network effect around the consumption of a particular vendor’s products: the value of

purchasing from a vendor is increasing in the number of products previously purchased from that vendor.1

We next demonstrate how the organization of the IT function shapes how switching costs and

network effects shape vendor choice. In the presence of network effects, one unit’s choice within a multi-

unit organization will have implications for the costs of vendor selection for other units within the same

organization, and when decision rights of IT organization are decentralized these costs will not be fully

internalized.

Third, we examine whether investments in complementary products can temporarily reduce

switching costs by reducing the importance of prior investments associated with learning and

compatibility on vendor choice. We find evidence consistent with this assertion. However, we find that

even in this setting that switching costs continue to be significant, in particular when compatibility among

hardware in different establishments is important but establishment vendor decisions are uncoordinated.

We use mixed logit models to examine buyers’ vendor choice, which is a function of buyer

preference and the level of switching costs faced by buyers. By examining cross-sectional and time series

variation in the magnitude of the installed base within units and across units within the same organization,

we are able to identify how changes in the installed base increase switching costs through local network

effects. Following prior work in marketing and economics, we utilize the mixed logit model to control for

unobserved buyer characteristics that may influence vendor choice. As a robustness check, we develop

1 To be clear, these network effects may not reflect unpriced network externalities. That is, network effects in our setting arise from a large number of nodes within a firm or supply chain relationship (as in Kauffman et al. 2000 and Zhu et al. 2006).

3

and implement a unique method of identifying the role of switching costs and network effects by

estimating how changes in the quantity of installed base over time affect the magnitude of switching

costs. Our estimates of switching costs incorporate the intrinsic switching costs associated with

investments in any router or switch technology (technology lock-in) as well as vendor modifications that

lead to higher switching costs (vendor lock-in); we leave the separate identification of vendor and

technology lock-in to other work (e.g., Chen and Hitt 2002; Chen and Forman 2006). To estimate these

models, we analyze the vendor decisions from 1361 establishments over 1996-1998, concentrated

primarily in the finance and services sectors. Harte Hanks Market Intelligence, a commercial market

research firm that tracks use of IT in business, undertook the survey.

Our results show that switching costs play a significant role in shaping vendor choice in this

market. In addition to the fixed costs required to switch vendors such as learning and transaction costs

that are invariant to the size of a buyer’s installed base and are more commonly studied in the literature,

we show that the size of router installed base is a major determinant of a buyer’s switching costs. These

results suggest that local network effects contribute significantly to switching costs from the incumbent

vendor. Moreover, we show that when IT purchase decisions are decentralized, the effects of IT vendor

choice on other units within the same firm are discounted. Last, we demonstrate that although the

introduction of switches did temporarily lead to lower switching costs, there remained significant

compatibility costs associated with installed base at other parts of an organization that were not involved

in the purchase of new products.

We make three important contributions to the literature. First, while prior research has studied

how switching costs may influence seller choice in IT and IT-enabled markets (Chen and Hitt 2002;

Greenstein 1993; Chen and Forman 2006) and how network effects may impact new technology adoption

(Kauffman et. al. 2000; Gallaugher and Wang 2002; Zhu et al. 2006), ours is the first paper to

demonstrate how increases in buyer installed base within an organization can further increase switching

costs through compatibility and local network effects. To make sure that our results are not driven by self-

selection (or spurious state dependency), we also develop and implement a unique method based on

4

difference-in-difference approach to identify the role of switching costs and network effects on vendor

choice. This method presents another contribution to the empirical literature of switching costs and

network effects.

Second, we examine how investment in new complementary products can impact buyer switching

costs. Some authors have argued that introduction of new products may present a “window of

opportunity” for eliminating lock-in as buyers are required to make sunk costs investments in new

products that are unrelated to the old (Bresnahan and Greenstein 1999; Brynjolfsson and Kemerer 1996;

West and Dedrick 2000). In contrast to prior work, we are able to demonstrate empirically how

investments in new products influence switching costs and vendor choice. This observed change in buyer

behavior has implications for sellers of IT and for policymakers.

Third, we show that the organizing logic of the IT function can influence how switching costs and

network effects influence buyer behavior. While there has been a rich literature that has examined the

efficacy of different manners of organizing the IT function (e.g., Sambamurthy and Zmud 2000; Agarwal

and Sambamurthy 2002; Weill and Broadbent 1998; Weill, Subramani, and Broadbent 2002), our paper is

unique in demonstrating how such organization influences the role of switching costs on vendor choice. It

also has implications for the agility with which an IT organization can respond to a new technology.

1.1 Related Literature

An extensive theoretical literature has examined how exogenously given switching costs

influence pricing, entry, and firm profitability.2 The empirical literature on switching costs is much

smaller than the theory literature, due primarily to the detailed data on individual- or firm-specific

decisions required to test hypotheses. Recent contributions that measure how switching costs influence

product or vendor choice in IT and information-intensive businesses include Greenstein (1993), Breuhan

(1997), Chen and Hitt (2002), Goldfarb (2006a), and Chen and Forman (2006).

Several studies in the IS literature have examined how network effects influence IT adoption.

2 This literature is far too extensive to survey here. For excellent reviews, see Klemperer (1995) and Farrell and Klemperer (2004). For a review on switching costs in information technology markets, see Chen and Hitt (2006).

5

Probably the closest to ours are Zhu et al. (2006) and Kauffman et al. (2000). Both show that

organizations with large potential networks are faster to adopt interorganizational standards, while

Kauffman et al. (2000) shows that a large proprietary network can lower the net benefits to adopting open

standards. However, neither of these studies examines how local network effects can increase the costs of

switching vendors, as we do.

Our research is also related to the small number of papers in the IS literature that have examined

vendor lock-in, or how switching costs influence vendor choice in IT markets. Chen and Hitt (2002) find

significant switching costs at different online brokerage firms and show how systems usage, service

design and other firm level factors might affect customer switching. Our paper is also related to Chen and

Forman (2006), which shows that switching costs differ significantly across vendors in the market for

routers and switches, indicating that vendor actions influence switching costs. Our paper differs from

Chen and Hitt (2002) and Chen and Forman (2006) in several ways. First, neither of those papers shows

how local network effects can increase switching costs as we do. Second, neither explores how the

organization of the IT function influences the magnitude of switching costs. Third, in this paper we

examine how investment in new complementary products influences buyer switching costs. We show that

new product introduction lowered the switching costs arising from the need for compatibility with prior

investments, which to our knowledge has not been shown empirically in any prior work.

Our paper is also related to prior literature that argues that new product innovation can influence

buyer behavior and, in turn, market structure in related IT markets. As noted above, prior work has

theorized that technological change in complementary layers of technology infrastructure can reduce

buyer switching costs and can have significant impact on the structure of technology markets

(Brynjolfsson and Kemerer 1996; Bresnahan and Greenstein 1999). Though prior case study work has

argued that such mechanisms may have been responsible for changes in market structure in mainframes

(Bresnahan and Greenstein 1999) and personal computers (West and Dedrick 2000; Bresnahan 2001),

empirical work studying how such innovations influenced buyer behavior have been rare.

Our work is also related to literature in IS that has examined how the organization of the IT

6

function can influence its effectiveness and that of the organization (Sambamurthy and Zmud 2000;

Agarwal and Sambamurthy 2002; Weill and Broadbent 1998; Weill, Subramani, and Broadbent 2002). In

contrast to prior research in this area, we focus on how the organization of the IT function influences its

ability to manage a specific problem felt by IT organizations—switching costs—as well as an

organization’s ability to respond to new technologies.

2. Routers and Switches

We examine how prior investments in routers and switches influence a buyer’s router vendor

choice. Prior to the rise in popularity of switches, routers represented the primary way that networks were

interconnected. Routers are slower at packet forwarding than switches, but they have additional

functionality. Routers have network management and security features, allowing network managers to

identify problems and congestion within a network as well as providing protection to keep the network

safe from outside intruders. Switches were introduced in the mid-1990s to increase bandwidth and reduce

delay in networks. Switches have faster throughput than routers but less intelligence.

Over our sample, switches were used both to connect LANs and as a replacement for hubs to

create switched networks (Fitzgerald and Dennis 1996). Because of their additional features, routers have

not been supplanted by switches completely. Routers continued to be valued for their management and

security features. These features make routers better than switches at connecting LANs at the same or

different sites (Panko 2001). As a result, an efficient design of a network often involves the use of

switches and routers as complementary products, and firms purchasing switches for the first time often

have to redesign their network and make complementary investments in routers to best utilize the

functionality of switches.3

3. Theory and Hypotheses

In this section we describe our hypotheses of how switching costs and network effects influence

router and switch vendor choice. Following prior literature, network effects refer to a “circumstance in

3 Over time, layer 3 switches have been introduced that allow switches to substitute directly for routers. Layer 3 switches were not a factor over our sample period.

7

which the net value of an action (consuming a good, subscribing to a telephone service) is affected by the

number of agents taking equivalent action” (Liebowitz and Margolis 1994). In our setting, there exists a

network effect in the consumption of a particular vendor’s routers or switches: the value of consuming a

particular router is affected by how many other routers this particular router can work with; the value

comes from two sources: the value from the basic function of routers, i.e., routing, and the value from

consuming other intelligent functions of routers, such as sharing traffic information, better coordination

among routers and more efficient routing as a result of better information and coordination. Note that in

our setting network effects need not be network externalities, which refer to “a specific kind of network

effect in which the equilibrium exhibits unexploited gains from trade regarding network participation”

(Liebowitz and Margolis 1994). Network effects in our setting are local (Sundararajan 2007), they are

bounded by the size of the network in the firm and so will not be directly affected by the vendor choice

decisions of other market participants. Specifically, since routers from different vendors follow the same

TCP/IP protocols, the value provided by the basic function of routers does not depend on which vendor

the router is from; however, the value from consuming other functions of routers depend on the

“interoperability” of these higher-level functions of routers. In that sense, they are similar to the local

networks defined by firm and supply chain boundaries explored by Kauffman et al. (2000) and Zhu et al.

(2006). Further, we do not examine the role of social network effects in which social influence can lead to

technology adoption bandwagons (Strang and Macy 2001; Abrahamson and Rosenkopf 1993); network

effects in our setting are driven by the economic costs of compatibility rather than by social cohesion.

Figure 1 shows our research model. We examine a buyer’s decision to choose router and switch

vendor. Buyers in our setting are individual units within a firm, or “establishments.” Buyer decisions can

be influenced by switching costs related to prior investments at the establishment and by other

establishments within the same firm. Switching costs can be fixed and invariant to the size of the installed

base, for example the costs of learning to use a product from a new vendor. Switching costs can also

increase in the size of the installed base, as may be the case when attempting to maintain interoperability

among products within a heterogeneous network.

8

3.1 Switching costs arising from establishment investments

We first discuss the fixed costs of choosing a vendor different from the incumbent at the

establishment : Ai in our research model. There are several sources of these switching costs. First,

configuration of new routers and switches can be very difficult (Leinwand and Pinsky 2000). Despite the

prevalence of open networking protocols, vendors often employ proprietary software to run their

networking gear. Proprietary software and complicated command-line interfaces can make management

of these devices hard. Setup and configuration is also complicated, and for many buyers entails the use of

outside networking consultants.

In other words, buyers of routers and switches invest considerable sunk costs in learning how to

configure and use routers and switches. Moreover, these complementary investments can not be

transferred to other vendors. For example, knowledge learned on how to configure a Cisco router cannot

be used to configure a 3Com router. Because of these learning costs, purchasers of LAN equipment will

have lower total purchase costs if they buy from their incumbent vendor.

To be clear, these switching costs can arise from technology or vendor lock-in. We argue that

purchases of routers or switches from any vendor require complementary investments in configuration

and that these investments give rise to switching costs and technology lock-in. However, by managing the

horizontal and vertical compatibility within their product lines, vendors can manipulate switching costs

and create vendor lock-in as well (Chen and Hitt 2002; Chen and Forman 2006). In this paper, we do not

attempt to separate vendor lock-in from technology lock-in, and leave this instead for future work.

Another source of one-time switching costs may be due to psychological costs of switching, or

non-economic brand loyalty (Klemperer 1995). For example, buyers may change their own preferences

over time in favor of products they have previously purchased, a form of cognitive dissonance (Brehm

1956). Further, as Klemperer (1995) notes, such non-economic reasons may be greater if an individual

has to rationalize her choice to others, as will often be the case in a business organization. Samuelson and

Zeckhauser (1988) provide other examples of such “status quo bias.”

Third, buyers may face transaction costs of switching suppliers. These transactions costs may

9

include identifying and qualifying the vendor, writing a purchase order and sales and service agreement,

and other transaction-related costs.

Together these costs imply that establishments will prefer to purchase from the incumbent

vendor, other things equal.

H1: Buyers face switching costs that are invariant to the size of installed base from a vendor when they

choose a vendor different from the incumbent vendor used at the same establishment.

A second cost of switching away from the incumbent vendor arises due to interoperability

problems among the same type of products from different vendors. Routers are more valuable when they

are able to communicate with other routers on the same network. Because of the complexity of the

devices, time-consuming and costly configuration is sometimes needed to get hardware from different

manufacturers to communicate. These problems are sometimes exacerbated by proprietary enhancements

added by vendors to create vendor lock-in. For example, Cisco itself had claimed that there was enough

proprietary code within its Internetworking Operating System (IOS) to allow Cisco products to “work

better when they talk to each other, rather than machines made by rivals” (Gawer and Cusumano 2002).4

Over our sample period, tests in trade publications often showed that products were interoperable,

however vendor non-participation in complicated tests sometimes suggested the presence of significant

interoperability problems (Tolly 2000).

As a result, potential incompatibilities from using different vendors create a local network effect

around products from the incumbent vendor: the value of consuming a router or switch from the

incumbent is increasing in the number of products from the incumbent (Liebowtiz and Margolis 1994).

This is captured in the term B(Nij) in our research model. Switching costs will be increasing in the number

of routers from the incumbent vendor since buyers who switch vendors must ensure compatibility with all

other routers in the network. Prior literature has shown that a large network can reduce the net benefits of

switching to a new open standard: in particular, Kauffman, McAndrews, and Wang (2000) showed that

the value of adopting a new open ATM network was declining in the size of a firm’s propriety network.

4 Industry publications over this period repeatedly describe how these enhancements create incompatibilities both real and perceived (Tolly 2000; Wickre 1996; Petrosky 1996).

10

However, while prior literature has studied how network effects can impact technology adoption, it has

not demonstrated how network effects can increase the switching costs associated with vendor choice.

The identification of how network effects can influence switching costs is unique to our paper.

H2: The greater the number of products from the incumbent router vendor at the establishment, the

higher the switching costs of choosing a vendor different from the incumbent vendor at the same

establishment.

3.1 Switching costs arising from firm-wide investments

The next two hypotheses examine how prior investments at other establishments within the same

firm influence buyer switching costs. The role of such prior investments on vendor choice will depend

upon the organization of the IT function. The traditional organizing logic for IT departments has focused

on three architectures: centralized, decentralized, and federal, with the architectures differentiated in

terms of the locus of decision-making authority (Sambamurthy and Zmud 2000). Recent research has

often recommended that IT infrastructure decisions be centralized (e.g., Weill and Broadbent 2004).

However, a considerable fraction of establishments in our sample make IT investment decisions locally.

Thus, we can observe how IT decision rights interact with installed base to determine router and switch

vendor choice.

We first examine how prior investments at other establishments within the same firm create

compatibility costs when switching vendors: D(Nij) in Figure 1. When establishments are part of a multi-

establishment firm, buyers choosing a vendor different from that used throughout the rest of the firm will

face additional compatibility costs. The size of these switching costs from other establishment

investments will be greater in organizations where IT purchase decisions are centralized, because the

costs of complementary adjustments that must be made at other establishments within the firm will be

internalized.

H3: The greater the number of products from the incumbent vendor at other establishments within the

same firm, the higher the switching costs of choosing a vendor different from the incumbent.

Multi-unit firms often transfer IT capabilities within an organization (Forman, Goldfarb, and

Greenstein 2008). IT expertise in one area of a firm can be transferred to other units. In our setting,

11

lessons learned from configuration in one unit of an organization can be transferred to other units, much

as expertise learned by outsourcing firms in one project are transferred to others (Greenstein 2001).

However, transfer of such expertise is more likely when purchase decisions are centralized (Forman,

Goldfarb, and Greenstein 2008). Thus, when purchase decisions are centralized, there will exist

significant switching costs arising from changing router or switch vendors due to learning costs.

Similarly, when procurement decisions are centralized, prior investments in other establishments

may create switching costs due to buyer inertia or due to the transaction costs of switching suppliers.

H4: When purchase decisions are centralized, buyers face switching costs that are invariant to the size of

installed base when choosing a vendor different from the incumbent vendor used at the other

establishments within the same firm.

Introduction of Switches and Its Impact on Router Switching Costs

Switching costs in our sample arise from complementary sunk cost investments that increase the

value of the router or switch but which are both specific to a particular vendor. These complementary

investments create persistence in behavior over time. Such persistence in user behavior due to

complementary investments has been identified in other IT markets including mainframes and personal

computers (Greenstein 1993, Bresnahan and Greenstein 1999; Breuhan 1998; West and Dedrick 2000;

Forman 2005; Chen and Hitt 2007). In our setting, the importance of compatibility with a large networked

installed base implies that the size of these complementary investments—and the size of switching costs

they engender—may be larger than in other settings previously studied.

Previous authors have argued that indirect entry in complementary layers of an existing IT

platform can create a new platform, rendering previous sunk cost investments irrelevant and so reducing

switching costs (Bresnahan and Greenstein 1999). For example, Bresnahan and Greenstein (1999)

describe how indirect entry by minicomputers and later client/server computing lead to the migration of

many business computing environments from a vertically integrated platform controlled by a single firm

(mainframes) to a vertically disintegrated platform with divided technical leadership. Brynjolfsson and

Kemerer (1996) similarly argue that changes in computing architecture could lead to the weakening of a

12

software product’s market leadership: for example, the transition from DOS to Windows made knowledge

of the traditional Lotus interface menu less important. West and Dedrick (2000) similarly argue that the

shift to Windows undermined the PC-98 standard in Japan. While this theory has considerable theoretical

appeal as a source of potential structure change in IT markets, it has seen few statistical tests.

The introduction of switching technology in the mid-1990s provides a natural experiment that

enables us to measure whether indirect entry can lower switching costs in an environment where learning

and compatibility costs are significant. Firms adopting switched networks could improve the throughput

of their network, however the topology of switched networks is different (Fitzgerald and Dennis 1996) so

firms adopting switched technology must make new complementary investments to configure the network

and ensure interoperability of devices under the new architecture. These sunk costs were nontrivial: the

management of switched networks was challenging over our sample period, because some of the

complementary technologies had not yet been standardized (Fitzgerald and Dennis 1996). Moreover,

these complementary investments were necessary regardless of whether a new or existing router vendor is

chosen. For example, when migrating to a new network architecture, network managers may set up or

alter existing subnets, may institute virtual LANs, or may institute or modify existing network services.

While vendor efforts to maintain horizontal and vertical compatibility within product lines will continue

to encourage buyers to purchase from the same vendor due to vendor lock-in, such within product-line

compatibility will not eliminate these other sources of switching costs due to technology lock-in. On the

other hand, these switching costs due to technology lock-in are temporarily dissipated when buyers

change architectures. Thus, introduction of switches represents an example of indirect entry that has the

potential to reduce (though not completely eliminate) switching costs.

To identify how the introduction of switches influenced the costs of switching router vendor, we

examine whether firms that purchase routers and switches simultaneously will have lower switching costs

than those that purchase routers alone—that is, those who purchase routers and switches together are

more likely to use a different vendor than those who buy routers only. Buyers purchasing routers in

conjunction with switches will be forced to invest in new organizational complements that will reduce the

13

importance of prior investments in configuration and interoperability on vendor choice. In our analyses,

we focus on how investments in switches reduce switching costs associated with prior establishment

investments. We focus on establishment investments for two reasons. First, when purchase decisions are

decentralized, router switching costs will be reduced only for investments made at the local establishment,

where the IT group must make complementary investments. Since the decentralized IT group will have

little control over router investments made at other establishments, the switching costs associated with

such investments will not be reduced. When purchase decisions are centralized, investment in switches

(and the associated architectural changes) may not occur simultaneously throughout the firm. If other

establishments have not yet invested in switches, then the switching costs associated with prior

investments at other establishments will not be reduced.

H5: Switching costs from prior investments in routers at the establishment will be reduced when buyers

purchase routers in conjunction with switches.

4. Methodology

4.1 Description of Utility Model and Measurement of Switching Costs

As noted in Section 3, we consider a buyer’s vendor choice as driven by its preferences, the

quality of the vendor’s products, and the switching costs faced by the buyer. To control for unobserved

buyer preferences, we adopt the mixed logit model. The mixed logit model has been used extensively in

the economics and marketing literatures in models of buyer choice to control for unobserved buyer

preferences and recover unbiased coefficient estimates on customer loyalty (e.g., Jain, Vilcassim, and

Chintagunta 1994; Keane 1997; Goldfarb 2006a). In this section and the next we describe the model used

to test hypotheses 1-4, in section 4.3 we describe the model used to test hypothesis 5. We will frame the

model in this section in terms of router vendor choice, though the same model also applies to switches.

Following the marketing literature on discrete choice (e.g., Guadagni and Little 1983), we

examine choice of router vendor conditional on the decision to purchase a router. Our use of vendor

choice is in keeping with prior studies of IT hardware demand that use discrete choice models and where

model-level information is either not available or usable (e.g., Hendel 1999; Prince 2008), and recent

14

papers utilizing discrete choice models to analyze clickstream data where the choice set is web site rather

than the exact web site service used (e.g., Chen and Hitt 2002; Gandal 2001; Goldfarb 2006a, 2006b). The

utility of a choice in our model can be interpreted as the mean utility that a buyer would receive for

selecting a particular vendor. As described below, we model this unobserved heterogeneity through a

random vendor-establishment-time error and an unobservable establishment-vendor preference parameter.

Our focus on vendor switching costs rather than model switching costs is in keeping with the economics

literature, which has focused on cross-seller switching costs (Farrell and Klemperer 2007) because these

switching costs will on average be larger than those across models within a vendor. However, to

demonstrate that our results are not driven by unobservable differences in model type, we later provide

the results of models where we allow the effects of installed base to vary by type of incumbent router

model at the establishment (Small Office/Home Office or Low End). Our qualitative results remain the

same. 5 6 Consistent with prior work on user choice of IT hardware and most marketing literature, we

assume the choice of number of routers is independent of vendor choice.

Formally, consider the utility of buyer i for vendor choice j at time t as ( )it it i it

j j ju v β ε= + , which is

comprised of two parts. First, it contains a component ( ( )it i

jv β ) which captures the measured preference

of buyer i for a particular vendor j. ( )it i

jv β consists of not only the average attractiveness of choice j

relative to other choices (including vendor quality), but also buyer i’s inherent preferences, iβ . iβ is

unobserved for all i and varies with the true population density *( | )f β θ , where *θ is a vector that is

equal to the true parameters of the distribution. Second, the utility contains a random component ( it

jε )

which is an independently and identically distributed (iid) extreme value error term, summarizing the

contribution of unobserved variables.

5 Our data survey buyer choice of LAN routers, and exclude decisions to purchase routers built for carrying traffic on the Internet backbone, reducing the heterogeneity in our data. 6 As an example of the number of models available in this market, in the September 1999 Datapro 1999 Router Comparison Column report, 49 router products were mentioned for 3Com under 4 models: NET Builder II, NET Builder, Office Connect Remote, and Super Stack II Netbuilder. For Bay Networks, 53 products were mentioned under 5 models: AN Router, ARN Router, ASN Router, CLAM, MARLIN. For Cisco, 69 products were mentioned under 11 series: 1000, 12000, 2500, 25000, 2600, 4000, 4500, 4700, 7200, 7500, and 760.

15

We wish to model the utility a buyer i associates with a particular vendor j, conditional on prior

relationship between buyer i and vendor I. Note that in our data it is possible for establishments to have

relationships with more than one vendor, i.e., I may be a set of incumbent vendors. For example, a buyer

may have prior relationships with multiple router and switch vendors, as may other establishments within

the same firm. For ease of notation and exposition, we consider that, in any context, I indicates one

incumbent vendor rather than a set. We express this utility as:

, 1 , 1 , 1 , 1

| ( )it it i it it it i t i t i t i t it

j I j j j j E j F j E j F j ju v Z s IE s IF t NE t NFβ ε α λ ε− − − −= + = + − − − − + (4-1)

In this specification, it

jα captures buyer i’s inherent tastes for vendor j and how they change over

time. These fixed effects will control for differences in product characteristics across vendors, including

price. Zit is a set of observed customer characteristics for buyer i at time t that is fixed across choices j.

Vector jλ captures average variation in buyer preferences across vendors. , 1i t

jIE− , , 1i t

jIF− , , 1i t

jNE− , and

, 1i t

jNF− are each (2 × 1) vectors of variables that measure prior investments in routers and switches. For

each vector, the element in the first row measures prior investments in routers, while the second row

measures investments in switches. Thus, although the focus of Hypotheses 1 through 4 is on how prior

investments in the same product create switching costs, we include prior investments in other products as

controls. For example, we allow prior investments in switches to influence router vendor choice and vice-

versa. This translates to

We will use this notation further below. The estimation of the (1 × 2) vectors sE,, sF , tE and tF

that multiply these vectors measuring prior investments is our primary concern. Table 1 provides a

description of each of the variables, their relationship to the hypotheses, and predictions for sE,, sF , tE and

tF .

, 1

, 1

, 1

[1,1]

[2,1]

−

−

−

=

i t

ji t

j i t

j

IEIE

IE

measures prior investments in routers

measures prior investments in switches

16

, 1i t

jIE− and , 1i t

jIF− are vectors of dummy variables indicating the presence of prior relationships

with another router or switch vendor, either at the same establishment ( , 1i t

jIE− ) or at other establishments

within the same firm ( , 1i t

jIF− ). Variables in , 1i t

jIE− take on non-zero values when the buyer chooses a

vendor other than the incumbent at the establishment (i.e., when j I≠ ).7 There are two cases when these

variables are zero: when the buyer has no prior relationship with any vendor, and when the buyer chooses

the incumbent vendor (i.e., j=I), which are the cases when the buyer faces no switching costs. Variables

in , 1i t

jIF− take on non-zero values when the buyer chooses a vendor other than the incumbent I at other

firm establishments (i.e., when j I≠ ). sE and sF measure the size of switching costs generated from this

incumbency, i.e., how a previous relationship between buyer i and vendor I affects the buyer’s utility of

vendor choice j relative to the incumbent vendor. Note that if an establishment or organization has

installed base from several vendors, it is possible for both 1t

jIE− and 1t

jIF− to be nonzero for several j.

, 1i t

jNE− and , 1i t

jNF− are vectors of variables that aim to measure the impact of the size of prior

investment on vendor choice. Variables in , 1i t

jNE− are equal to the log of (one plus) the total number of

routers and switches with vendor I at the establishment at time t-1 when j I≠ . As before, variables in

this vector take on zero values when the buyer has no prior relationship with any vendor or when j=I,

indicating the cases where the buyer faces no switching costs. These vectors capture the marginal increase

in switching costs from having more than one router and switch with the incumbent vendor I. Similarly,

variables in , 1i t

jNF− are equal to the log of the total number of routers and switches with vendor I at time

t-1 when j I≠ , and tF captures increases in switching costs arising from increases in network size. Note

that if an establishment or organization has installed base from several vendors, it is possible for both

1t

jNE− and 1t

jNF− to be nonzero for several j.

7 So, for example, suppose that an establishment has made prior investments in Cisco routers but no prior investments in Cisco switches. Moreover, suppose the establishment has no prior interaction with other vendors.

Then, , 1i t

jIE

− = (1 0)’ for j ≠ Cisco, and , 1i t

jIE

− = (0 0)’ for j = Cisco.

17

We include vendor and vendor time fixed effects within our model. Because prices do not vary

within these fixed effects, we do not include a separate price term. This is in keeping with the modeling

used in other studies of IT demand where model and pricing information is difficult to obtain (Greenstein

1993; Kretschmer 2004, 2005). In later sections, we will show results of robustness checks where we

utilize a data set on switch prices from Doms and Forman (2005).

In keeping with much of the literature, our model examines the choice of a single router and

switch vendor. Cases of multiple vendor purchases are dropped from the sample. However, in robustness

checks below we examine the case of a model where buyers are allowed to buy from two vendors.8

4.1.1 Identifying Switching Costs

The key parameters of interest in the model above are sE,, sF , tE and tF.. Parameters sE, and sF will

capture the effects of learning, transaction costs, and inertia embedded in the parameters Ai and Ci in our

research model. It may also partially capture costs of compatibility. To see this, imagine if a buyer’s

installed base in routers increases from zero to one router. If the buyer chooses in future periods to switch

vendors, it will have to incur new complementary investments to learn how to use the new vendor and

will need to ensure compatibility with the installed base. Thus, the mean utility for choosing a new vendor

will be lower than remaining with the existing vendor, ceteris paribus. This will be reflected in a positive

coefficient on sE.

In contrast to sE, and sF which capture the effects of learning, buyer inertia, and compatibility, tE,

and tE, will directly recover the increasing costs of compatibility. To see this, now imagine in the example

above that the installed base increases from one router to two. The value of these switching costs (Ai and

Ci) in this case will be similar, if not identical, to that where the buyer had only one legacy vendor; that is,

the switching costs Ai and Ci will be unchanging with the size of the installed base. However, the costs of

maintaining interoperability with the installed base (B(Nij) and D(Nij)) will increase with the number of

devices, so tE, and tF will capture the increase in switching costs from the marginal router or switch.

8 In our analysis sample, for routers the mean number of vendors is 0.39. 64.4% have zero routers; 32.1% for 1; 3.2% have 2; and 0.22% have 3. Note that these numbers may include multiple vendors within the Other category. In our analysis sample for switches, the mean number is 0.18. 82.2% have 0, 17.4% have 1, and 0.33% have 2.

18

4.2 Model Estimation

Conditional on iβ , the probability that consumer i chooses alternative j in time t is a standard

multinomial logit: 9 exp( ( ))

( )exp( ( ))

it i

jit i

j it i

k

k

vP

v

ββ

β=∑

To estimate the contribution of establishment i to the likelihood function, we next need to

estimate the probability of each establishment’s sequence of choices. Conditional on iβ , the probability of

establishment i’s choices is the product of the multinomial logits:

( ) ( )i i it i

j

t

S Pβ β= ∏

The unconditional probability of this sequence of choices is the integral of ( )i iS β over all

possible values of iβ , which depends on the parameters of the distribution of iβ , f(). Here, we assume a

normal distribution for f().10 The unconditional probability for the sequence of choices is:

* *( ) ( ) ( | )i i i i iP S dθ β φ β θ β= ∫ (4-2)

Ultimately, the goal is to estimate *θ , the population parameters of the distribution. The log-

likelihood function is ( )( ) log ( )θ θ=∑ i

iLL P . We cannot use maximum likelihood estimation since the

integral above cannot be calculated analytically. Instead, the integral is approximated by sampling, and

we estimate the parameters by maximizing the simulated likelihood function.

4.3 Measuring the Effects of New Product Investment

In hypotheses 5, we seek to identify how investments in complementary products influence the

magnitude of switching costs. To do this, we examine whether buyers who purchase routers jointly with

switches behave differently than those purchasing routers alone, and so we must model the joint choice of

router and switch vendor. Though this model is more general than the one described in sections 4.1 and

9 This discussion of the estimation of the mixed logit model draws heavily from Revelt and Train (1998) and Train (2003). 10 Other distributions commonly used in the mixed logit model include the log normal, uniform, and triangular. We experimented with other distributions and the results remain qualitatively the same.

19

4.2 in that it allows switching costs to depend on the simultaneous purchase of complementary products,

it has two disadvantages. First, it imposes a set of assumptions on buyer’s substitution between routers

and switches. Second, the increased flexibility comes at a cost in terms of the number of parameters we

must estimate. As a result, although the results of this model are consistent with those from section 4.1,

we will use this second model primarily to test hypothesis 5.

In this more general model, conditional on the decision to purchase routers and/or switches, an

establishment faces a choice set that includes 25 choices. We divide this choice set into three subsets that

reflect the decisions to purchase routers alone, switches alone, and routers and switches together. Let

( ) { | 1,...,4}RC j j= = represent choices to purchase 3Com, Bay, Cisco, or “Other” routers in isolation,

( ) { | 5,...,9}SC j j= = represent choices to purchase 3Com, Bay, Cisco, or “Other” switches, and

( , ) { | 10,...,25}R SC j j= = represent joint purchases of routers and switches. 11 The utility function for this

model is

, 1 , 1

| ( ) ( ) ( ) ( ) ( ) ( ) ( ) ,α ω λ ε− −= + + − − +it i i it i t i t it

j I j R j R j R E R j R F R j R ju Z s IE s IF ( )Rj C∈

, 1 , 1

| ( ) ( ) ( ) ( ) ( ) ( ) ( ) ,α ω λ ε− −= + + − − +it i i it i t i t it

j I j S j S j S E S j S F S j S ju Z s IE s IF ( )Sj C∈ (4-3)

| ( ) ( ) ( , ) ( , ) ( ) ( )

, 1 , 1 , 1 , 1

( ) ( ) ( ) ( ) ( ) ( ) ( ) ( ) ,

α α ω χ λ λ

ε− − − −

= + + − + +

′ ′ ′ ′− − − − +

it i i i it it it

j I j R j S j R S R S j j R j S

i t i t i t i t it

E R j R F R j R E S j S F S j S j

u SC Z Z

s IE s IF s IE s IF ( , )R Sj C∈

We allow our vendor-specific constants to differ for routers ( ( )

i

j Rα ) and switches ( ( )

i

j Sα ).

Moreover, though we use a common vector itZ to control for buyer characteristics, we allow the

parameter estimates that measure the effects of this vector on vendor choice to differ for routers ( ( )j Rλ )

and switches ( ( )j Sλ ). However, we assume that these parameters are separable across router and switch

choices. In contrast, we allow the influence of prior investments to vary across routers and switches (e.g.,

( )E Rs and ( )E Ss measure the impact of prior establishment investment decisions on router and switch

11 So, for example, Choices in CR,S) will be of the form (3Com Router, 3Com Switch), (3Com Router, Bay Switch), (3Com Router, Cisco Switch), etc.

20

vendor choice, respectively), but we also allow the influence of prior investments (our switching cost

parameters) to vary depending on whether a buyer purchases routers and switches alone or together: for

example, ( ) ( )E R E Rs s′≠ in general and the same is true for the other switching costs parameters. Hypothesis

5 asserts that ( ) ( )[1,1] [1,1]′>E R E Rs s . In this model, because of the very large number of parameters, we do

not attempt to measure separately the effect of network size on buyer behavior.

Hypotheses 1-4 suggest that there is an advantage to purchasing routers and switches from the

same vendor due to switching costs related to learning, compatibility, and transaction costs. Buyers

purchasing routers and switches simultaneously can similarly save on such costs by purchasing from the

same vendor. The costs involved in purchasing from multiple vendors are labeled as “shopping costs” by

Klemperer (1992) and should be controlled for. In our model, the variable it

jSC indicates whether a

buyer is subject to shopping costs arising from using an additional vendor. A buyer faces additional

shopping costs (i.e, 1it

jSC = ) if and only if it purchases both routers and switches at the same time and

purchases them from different vendors. We assert that ( , ) 0R Sχ > .

In order to accommodate closer substitution among choices in each of our three groups—routers

only, switches only, and routers plus switches—we include an additional random parameter ( )ω i

j l that is

nonzero whenl

j C∈ . Each ( )ω i

j l is normally distributed, independent across choices, but constant across

time within establishments. This specification gives rise to correlation in the error terms among choices

∈l

j C .12 By adding these ( )

i

lω , we engender closer substitution between choices within the same l

C than

those in different l

C . As before, it

jε is a random iid extreme value term. Model (4-3) is estimated using

simulated maximum likelihood

5. Data

5.1 Sample

12 If ( )η ω ε= +it i it

j j l j, then if , ∈

lj k C

2

( ) ( ) ( )cov( , ) ( )( )η η ω ε ω ε σ= + + =it it i it i it

j k j l j j l j lE , where 2

( )σ l is the variance of

( )ω i

j l . See Train (2003) for further details.

21

We obtained data on technology usage from the CI Technology Database (hereafter CI database)

over the period 1995-1998. The CI database contains data on (1) observation characteristics such as

establishment size, industry, and location and (2) technology purchases of computers, networking

equipment, printers, and other office equipment. The CI database has been widely used in studies of

business value (Brynjolfsson and Hitt 2003; Dewan, Shi, and Gurbaxani 2007) and IT investment

(Forman 2005; Forman, Goldfarb, and Greenstein 2005, 2008; Hitt 1999). Forman, Goldfarb, and

Greenstein (2005) compare the CI database to Census data and show it is broadly representative; in

Appendix Table 1 we compare our sample of the CI Databsae with County Business Patterns data over

the same period and demonstrate that our sample is biased in favor of large organizations. Thus,

establishments in our sample may have preferences for vendors in our sample who tend to support large

organizations, this will be reflected in the mean values for vendor dummies in our data.

The unit of observation in our sample is an establishment-year. Thus, our sample will often have

data on multiple establishments for a given firm. To keep the analysis of manageable size, we focus on

industries that are generally regarded as heavy users of information technology13 and establishments of

over 100 employees from the CI database over the sample period. All establishments are from the U.S.

The total number of establishments in our raw data sample is 29,071. The distribution of vendor

purchases is also representative of the market; in Appendix tables 3 and 4 we compare the distributions of

unit sales in our sample to a market survey by Dataquest (a division of IDC) over 1996-1998. Overall, in

our sample we observe purchases from 23 different router vendors and 24 switch vendors. The

distribution of unit sales in our sample is broadly similar to that in the Dataquest survey.

Harte Hanks survey establishments on these components at different times of year; we assemble

our sample by obtaining the most current information as of December of each year. For example, the

observation for an establishment in 1996 will contain information on the establishment’s characteristics

and technology usage as was recorded in the CI database in December 1996.

13 We obtained data from the CI database on SIC codes 60-67, 73, 87, and 27. These SIC codes correspond to the industrial groupings on Finance, Insurance, and Real Estate (60-67); Business Services (73); Engineering, Accounting, Research, Management, and Related Services (87); and Printing and Publishing (27).

22

To infer vendor decisions, we calculate the change in quantity installed from year to year from

each vendor at each establishment. We drop observations in which buyers purchase from more than one

router or switch vendor (8.2% and 4.1% of observations in our router and switch estimation sample,

respectively, include purchases from multiple vendors). Among those establishments that purchase

routers, the median number of routers purchased is 2, the mean is 4.99, the minimum is 1 and the

maximum is 100 (for establishments). Among those firms that purchase routers, the median number of

routers purchased is 3, the mean is 7.31, the minimum is 1 and the maximum is 188. Among those

establishments that purchase switches, the median number of switches purchased is 2, the mean is 7.33,

the minimum is 1 and the maximum is 420 (for establishments). Among those firms that purchase

switches, the median number of switches purchased is 3, the mean is 7.67, the minimum is 1 and the

maximum is 453. Our discrete choice models include vendor dummies and variables that control for

buyer heterogeneity: these variables are listed in table 2 and will be discussed more in Section 5.2.

In our analysis sample, the minimum time since last survey is 0 months (15.3% of observations)

and the average time is 5.6 months. The maximum time is 29 months. When the time between surveys is

long, two things may occur. First, if the survey time extends for more than two Decembers it may appear

that no purchase has been made when a purchase has occurred. However, this is unlikely to bias our

switching cost estimates since we examine vendor choice conditional on the decision to purchase. The

other potential bias is that the observed purchase may be greater than the actual for a given year. If the

observed purchase includes two vendors, then this will decrease the number of observations in our

sample. As noted above, we conduct robustness checks with models that allow buyers to purchase from

multiple vendors and our results remain qualitatively similar.

Because they do not vary by choice, buyer controls must be interacted with vendor dummies to be

identified in the model. Thus, our identification strategy requires us to observe a large number of

purchases from each vendor for the establishment controls to be identified. We examined the vendor

choice decisions of firms that purchased routers and switches from 3Com, Bay Networks, and Cisco,

which represent the dominant firms in the market for routers and switches. These vendors account for

23

92.48% of router purchases and 78.12% of switch purchases in our data for 1998 (Appendix Table 2 and

3). Purchases from vendors other than these three were classified as a fourth decision labeled Other.

Further, buyers who previously purchased from one of the Other vendors are classified as having an

installed base in the Other vendor: note that this may lead to an attenuation (downward) bias in the

measurement of switching costs since buyers may purchase from multiple vendors within the Other

category. We further drop establishments that are not in the database in consecutive years, that are

missing fields, and that were located in Europe. Last, the majority of establishment-year observations in

our sample purchase neither routers nor switches, and as noted above we also exclude such observations,

since our focus is on switching costs, which are relevant only when a buyer has made purchases. The

final analysis data set for router purchases contains 482 observations from 1996, 431 observations from

1997, and 448 observations in 1998. The final analysis data set for switches includes 218 observations in

1996, 269 observations in 1997, and 413 observations in 1998.

5.2 Variables

We described the construction of the variables in the vectors , 1i t

jIE− , , 1i t

jIF− , , 1i t

jNE− , , 1i t

jNF− , and

it

jSC in section 4. (We divide , 1i t

jNE− and , 1i t

jNF− by 10 to obtain appropriate scaling to achieve

convergence in our nonlinear model, however all marginal effects in the paper correct for this

transformation.) In this section we discuss the construction of the control variables. The vector itZ

controls for establishment characteristics that may influence the value of each vendor. Table 2 provides a

list of these variables and their descriptive statistics using the establishment as the unit of observation. We

also include two-digit industry dummies that indicate whether the establishment is in Finance, Insurance,

and Real Estate (Standard Industrial Classification (SIC) codes 60-67) or Business Services (SIC 73 and

87). An industry dummy for firms engaged in printing and publishing activities (SIC 27) is the omitted

category.14 To control for establishment size, we include the log of the number of employees at the

14 We have also experimented with using three-digit SIC dummies. The results are qualitatively the same. When estimating model (4-3), we substitute the SIC dummies for a dummy for the finance, insurance, and real estate and a

24

establishment. To control for differences in purchase behavior among single establishment and multi-

establishment firms, we include a dummy variable that is one when the establishment is part of a multi-

establishment firm. To control for differences in vendor preferences for branch offices versus firm

headquarters, we also include a dummy that is equal to one when an establishment is a branch office.

Last, to control for differences in IT infrastructure that may influence vendor choice, we also include

variables controlling for the total number of data lines and a dummy indicating that there are large-scale

computing applications (mainframes, minicomputers, or servers) installed at the establishment.15 Year

dummies are also included to control for changes in buyers’ average valuation of vendors over time. We

have also experimented with a variety of other establishment-level controls and the qualitative results

remain similar.

6. Results

6.1 Baseline Model

Tables 3a and 3b provides estimates for our baseline model (4-1) for routers and switches. For

brevity and because we are interested in understanding how investment in switches influences router

switching costs, we will focus our analyses on vendor choice in routers, however we provide switch

results for completeness. Also for brevity, the table shows only the parameter estimates for sE, sF, tE, and

tF. Our focus is on how prior investments in the same product influence switching costs—for example,

how prior investments in routers influence the costs of switching router vendors—however we include

cross-product effects for comparison purposes. For each model in both tables, the first column represents

how prior investments in routers influence vendor choice, while the second column represents the

influence of switch investments on vendor choice. We use simulations to quantify the economic impact of

switching costs on vendor choice. Because in hypothesis 5 we test the impact of an architecture change

(proxied by simultaneous purchases of switches) on the costs associated with switching router vendors,

dummy for business services. Again, this is because of the large number of parameters needed to estimate this model. 15 We have experimented with a broad array of controls other than those listed in tables 3a and 3b, including number of network protocols, number of external data lines, headquarters controls, and types of internet technologies in use. The results remain qualitatively the same.

25

the focus of our analyses will be the switching costs associated with router vendor choice. However, the

qualitative results of hypotheses 1 through 4 remain similar whether we use routers or switches. Table 4

describes the results of simulations that quantify the impact of a 0/1 change in the dummy variables

indicating the presence of an installed base and a change in the log of total installed base from 0 (log(1))

to 1.032 (log(2.801))—the mean value of the installed base of routers on router vendor choice. These

marginal effects are computed by calculating the change in probability for each establishment, and then

averaging these changes across the sample.16 They show how changes in installed base influence vendor

choice, controlling for vendor quality and buyer preferences. In this way, they illustrate whether a buyer

will continue to purchase from the incumbent vendor even when another vendor would offer it higher

utility in the absence of switching costs.

Columns 1 and 2 of Table 3a detail the results from model (1) that show there are significant

costs of switching from an establishment’s incumbent router vendor. Specifically, prior vendor interaction

with a vendor at the same establishment and at other firm establishments creates significant switching

costs (coefficient estimate (CE) 0.9551 for router incumbency at establishment level and 0.2694 at other

firm establishments, though the latter is statistically insignificant). These switching costs decrease the

likelihood of purchasing from a different vendor, or equivalently, increase the likelihood of purchasing

from the incumbent vendor. Table 4 shows that a 0/1 change in vendor interaction at the same

establishment and at other establishments of the same firm increase the probability of purchasing from

Cisco by 14.58 and 4.23 percentage points, respectively. Moreover, Table 3a column (1) shows that

increases in router installed base (or router network size) at the establishment (CE 5.9870) and at other

firm establishments (CE 4.0863) also leads to significant increases in switching costs: an increase in the

number of routers at the same establishment and at other firm establishments from zero to its mean value

increases the probability of purchasing from Cisco by 5.65 and 4.08 percentage points, respectively.17

16 For example, the entry in the first row and first column indicates the change in the market share for Cisco when each establishment in the sample changes from not having to having a Cisco router installed at the establishment. 17 These marginal effects are calculated by subtracting the difference in the change in probabilities from going from no routers and switches to turning the fixed cost of switching on (e.g., 14.58 in column 1 of Table 4) from the

26

These results are robust to the inclusion of variables measuring prior interaction with switch vendor.

Overall, all three models show that both learning and compatibility influence switching costs, while

increases in installed base also increases switching costs, providing significant support for hypotheses 1

through 3.

Although their parameter estimates are not associated with separate testable hypotheses, models

(2) and (3) of Table 3a also provides some evidence of the presence of cross-product switching costs of

choosing a router vendor different from the incumbent switch vendor. The results in models (2) and (3) of

table 3b are largely a mirror of those in table 3a, indicating that prior establishment interaction with the

same router vendor at the establishment will influence switch vendor choice, however in some cases the

estimates are statistically insignificant.

Hypotheses 4 asserts that the organization of the IT function will shape how prior investments at

other establishments within the same organization will shape switching costs. In particular, when IT

purchase decisions are centralized, a buyer may face switching costs through learning, transaction, and

compatibility costs due to other units’ investments when choosing a vendor different from other units’

incumbent vendor. However, if IT purchasing decisions are decentralized then only compatibility costs

with other units will influence buyer behavior. This gives rise to a testable implication: when purchasing

decisions are centralized both the fixed (sF) and network (tF) costs of switching will be positive and

significant; however, when purchasing decisions are decentralized only the network (tF) costs of

switching will influence vendor choice.

To test this hypothesis, we use a question from the Harte Hanks CI database survey that asks “Are

non-PC computer system purchasing decisions made locally or at parent organization?” (Harte Hanks

2000). In our router sample, 78.6% of establishments answer yes to this question. The model we estimate

is identical to the one specified in equation (4-1), except we allow sE, sF, tE, and tF to vary depending on

whether router purchase decisions are made at the establishment or whether they are centralized within

difference in probability from going from no routers and switches to turning both the fixed and variable costs of switching on (e.g., 20.23 in column 1 of Table 4). For example, 5.65=20.23-14.58.

27

the firm.18 Our results are consistent with hypothesis 4. Table 5, column (2) shows that when purchase

decisions are made locally, the parameter sF is statistically insignificant from zero (CE -0.0758),

indicating that other establishments’ vendor choice has no effect on an establishment’s vendor choice,

while the parameter tF shows that compatibility with other establishments plays a significant role on buyer

behavior (CE 3.3411, significant at the 5% level). In contrast, column (4) shows that when purchase

decisions are centralized, both sF (CE 0.961, significant at 5% level) and tF (CE 5.5547, significant at 1%

level) are positive and significant, suggesting the importance of firm-wide installed base on an

establishment’s decision. In fact, a comparison of columns (3) and (4) shows that when purchase

decisions are centralized, firm-wide installed base plays a more significant role on buyer behavior than

does establishment installed base.

6.2 Robustness Checks

In this section we explore alternative hypotheses for our results. For brevity and because of our

focus on router switching costs, in these models we focus primarily on the router choice models.

Self-Selection

One potential alternative hypothesis is self-selection: buyers display persistence in their behavior

over time not due to switching costs but rather because buyers self-select into the vendor that best fit their

needs. Formally, this would translate in our model into an unobservable buyer-specific error, constant

over time, that is correlated with our measures of installed base. In short, this is a problem of identifying

true state dependence from spurious state dependence (Heckman 1981). We follow prior papers in

economics and marketing by incorporating a buyer-specific random error term into our mixed logit model

to control for this unobserved buyer heterogeneity (e.g., Jain, Vilcassim, and Chintagunta 1994; Keane

1997; Goldfarb 2006a).

To further investigate the robustness of our results, we extend our model and utilize the time

variation in buyers’ installed base to identify switching costs. We first present a formal discussion of our

18 We also ran regressions that extended model (3). However, this model required the measurement of a large number of parameters, and ultimately had many insignificant estimates. These results are available from the authors upon request.

28

identification strategy and then the intuition.

Following the base model described in Section 4.1, suppose Xit is the vector that captures all

exogenous buyer characteristics, observed or unobserved, that determine the utility buyer i gets from

choice j, i.e., it

ju . In general, the Zit in equation (4-1) will be a subset of X

it, it itZ X⊂ . jβ captures

variation in buyer tastes across vendors along with Xit. Notation otherwise follows that presented in

Section 4.1 Thus, the new model is:

, 1 , 1 , 1 1

| α β ε− − − −= + − − − − +it i it i t i t i t t it

j I j j E j F j E j F j ju X s IE s IF t NE t NF

By construction, it

j Xβ controls for all buyer heterogeneity. Therefore, positive sE, sF, tE, and tF

indicates true switching costs. Unfortunately, in practice, it is impossible to account for all buyer

heterogeneity given that only a subset of Xit, Zit where it itZ X⊂ , can be observed. When only a subset of

Xit is observed, positive values for sE, sF, tE, and tF may simply reflect the effects from uncontrolled

buyer heterogeneity. In other words, a bias is created because we are unable to control for all sources of

buyers heterogeneity, and our results reflect buyers self-selecting into their “best” vendor. However, as

shown in Greene (2002), as long as the installed base variables , 1i t

jIE− , , 1i t

jIF− , , 1i t

jNE− , and 1t

jNF− are

orthogonal to Zit, then even if it itZ X⊂ , then the coefficient estimates on these variables will still indicate

true switching cost. The challenge is to construct variables that measure the installed base but which are

orthogonal to Z. Fortunately, , 1i t

jNE− and 1t

jNF− are accumulated variables that may change over time, so

we can take advantage of this property to resolve this issue.

Suppose at some time t=1, ,0i

jIE , ,0i

jIF , ,0i

jNE , and ,0i

jNF are related to one or a set of unobserved

variables in Wit where W

it=X

it-Z

it, and there is no real switching cost (i.e., s=0). So the true model is

1 1 1 ,0 ,0 ,0 ,0 1

| α λ γ ε= + + − − − − +i i i i i i i i i

j I j j j E j F j E j F j ju Z W s IE s IF t NE t NF

But since W is unobserved, the model we actually estimate is:

1 1 ,0 ,0 ,0 ,0 1

| α λ ε= + − − − − +ɶ ɶɶ ɶi i i i i i i i

j I j j E j F j E j F j ju Z s IE s IF t NE t NF

29

In this case, since ,0cov( , ) 0i it

jNE W ≠ we have E E

t t≠ɶ . In other words, E

tɶ includes the effects of

spurious state dependence. Similar results hold for the parameters , , and E F F

s s tɶɶ ɶ . We may be able to solve

this problem by measuring how changes in installed base influence switching costs, however. At time t,

we can write the model as

1 1 ,0 ,0 ,0 , 1 ,0 ,0 , 1 ,0 1

| ( ) ( )α λ ε− −= + − − − − − − − − +ɶ ɶɶ ɶi i i i i i i t i i i t i i

j I j j E j F j E j E j j F j F j j ju Z s IE s IF t NE t NE NE t NF t NF NF

If 0 , 1 ,0cov( , ) 0it i i t i

j jW W NE NE−− − = , then

Et will measure true switching costs. In other words, we

utilize changes over time in the installed base to measure how compatibility and network effects influence

switching costs.

The intuition behind this identification strategy is similar to that for a linear panel data model. We

are concerned that there may be unobserved variables that are fixed over time and that are correlated with

our measures of prior IT investments, such as buyer preferences for a particular vendor. So long as these

changes in the size of the installed base over time are uncorrelated with buyer unobservables that may

influence their vendor choice, we will be able to recover unbiased estimates of switching costs. This

identification strategy, which has not been used in prior work on switching costs, represents an additional

contribution of our work.

Because 1996 is the first year in which establishments bought switches, we estimate the model

over 1997 to 1998, using 1996 and 1997 installed base data. Identification of true tE and tF is through

changes in the installed base between these latter two years. Table 6 shows the results of these

regressions. Increases in installed base from the router vendor at the establishment are shown to have a

significant effect on router vendor choices—this result is robust across all models. Moreover, increases in

installed base from the switch vendor at the establishment have a significant effect on router choice.

Increases in the installed base at other establishments increase the likelihood that the establishment will

continue to purchase from the incumbent, however these results are statistically insignificant, whether

these increases are from router or switch vendors. This latter result may be because there is less variation

in the data to identify the influence of other establishment changes since not every firm has multiple

30

establishments (Table 2 shows that only 63.1% of establishments in our data are from multi-establishment

firms).

Supply Side Behavior

Another potential concern may be that the parameters tE and tF may reflect unobserved supply

side behavior. In particular, vendors may offer lower prices to large firms with large potential for future

sales or to may offer quantity discounts for those who purchase large quantities and who had purchased

from the vendor in the past. Such discounts may be correlated with network size and would also engender

persistence in behavior, potentially leading to biased estimates of tE and tF.

To demonstrate that our results are not driven by unobserved vendor behavior, we run a series of

robustness checks where we examine the sensitivity of our estimates to settings where buyers share

similar characteristics and therefore are unlikely to receive significantly different vendor treatments and

where vendors are less likely to deviate from posted prices and typical service contracts. First, we

examine establishments that report using only Small Office/Home Office (SOHO) or Low End routers.

This is the classification used by industry consultants such as Datapro and Dataquest to identify the low

end of the router market. To identify these establishments, we use the 49.0% of our establishments that

report some information about their installed base other than vendor name. These 49.0% often do not

report exact models, and do not necessarily report additional information for the entire installed base.

However, these establishments report enough information for us to identify whether their installed base

contains primarily SOHO or Low End routers. We then use information from Datapro Comparison

Column Reports and product descriptions from Datapro on CD-ROM to classify the installed base

reported by the establishments. We identify establishments who purchase only SOHO or Low End routers

(11.3% of the sample) and examine whether the results are different for this group from the rest of the

sample.

Second, we examine how results differ for establishments that are in the bottom third in number

of network nodes. Such establishments have smaller networks and represent smaller potential markets for

router vendors, and so vendors will have less incentive to discount. Third, we identify establishments who

31

purchase only one router (36.1% of the sample) and compare the results to those who purchase two or

more routers in the establishment-year under observation. Establishments purchasing only one router are

unlikely to receive quantity discounts from the vendor.

Because our primary interest is in measuring the effects of compatibility and network effects on

switching costs, we re-estimate a version of model (1) of Table 3a in which we compute separate

parameters for sE, sF, tE, and tF for establishments in the “small” group (those with low end routers, are in

the bottom third in total nodes, and purchase only one router) and for “baseline” establishments.19 Results

are included in columns 1 through 6 of Appendix Table 4. While we generally lose significance on the

parameters sE and sF, the estimates for tE and tF remain qualitatively similar as in the baseline model.

Thus, these robustness checks validate our hypothesis that network effects influence switching costs.

Buyer Inertia

Another potential concern is that our estimates represent inertia in procurement patterns. In other

words, establishments with a large number of routers may also be from a larger organization. Large

organizations may be too rigid to change procurement arrangements, and so our results may reflect not

network effects but rather unobserved differences in procurement practices correlated with network size.

To explore this hypothesis, with re-estimate a version of model (1) of Table 3a in which we compute

separate parameters for sE, sF, tE, and tF for small organizations (those in which the number of employees

is in the bottom third of the distribution). These results are included in columns 7 and 8 of Appendix

Table 4. Again, estimates for tE and tF remain qualitatively similar as in the baseline model or are even

larger.

Choice of Multiple Vendors

Another potential concern is that by dropping vendors who purchase from two or more vendors,

we may be biasing our switching costs estimates upward. To address this concern, we re-estimate a model

19 We have also re-estimated model (2) of Table 3a which we do not include due to space considerations. It

gives qualitatively similar results to the baseline model and is available upon request. We do not re-estimate the full

model (3) because of the excessive number of parameters required.

32

that allows vendors to choose two vendors. In this model, the utility for purchasing from one vendor is as

follows:

, 1 , 1

| ( 1) ( 1) ( ) ( 1) ( ) ( 1) ( ) ,it i i it i t i t it

j I j R j R j R E R j R F R j R ju Z s IE s IFα ω λ ε− −= + + − − +

where ( 1)

i

j Rω is a random parameter indicating the unobserved utility the buyer obtains by purchasing

from only one vendor. When vendors purchase from two router vendors, we assume that they designate a

primary and secondary vendor. We identify the primary vendor as the one with the highest quantity

purchased. We assume that the utility obtained is separable into the utility from the first vendor plus the

utility from the second vendor.

, 1 , 1

| ( 1) ( ) ( 1) ( ) ( 1) ( )

, 1 , 1

( 2) ( ) ( 2) ( ) ( 2) ( ) ( 2)

it i it i t i t

j I j R j R E R j R F R j R

i it i t i t i it

j R j R E R j R F R j R j R j

u Z s IE s IF

Z s IE s IF

α λ

α λ ω ε

− −

− −

= + − − +

+ − − + +

The parameter ( 2)

i

j Rω captures unobserved buyer level utility (or disutility) from purchasing from

multiple vendors. For identification purposes (we have few observations in which buyers purchase from

two vendors) we assume that the effect of buyer observables on vendor utility is identical for the first and

second vendor, though we do allow our switching cost parameters to vary across first vendor and second

vendor router since the routers may be put to different purposes.

Appendix Table 5 shows the results of estimating these models for routers.20 The results for first

router choice are qualitatively similar to that in our baseline analyses. The results for second router choice

are also similar to those for first choice, however in some cases significance is lost because we are

identifying off a much smaller number of purchases.

Missing Prices

Another potential concern is the lack of price data in our models. Because our model includes

vendor and vendor-time dummies that incorporate the average value of all product characteristics for a

vendor-time (including price), price coefficients are not separately identified in our models (i.e., their

20 We estimated a similar model for switches, which provided qualitatively similar results, but which we

suppress for brevity.

33

effect cannot be measured separately from that of our vendor and vendor-time dummies), however

traditionally models of utility do incorporate price within them. Unfortunately, it is very difficult to obtain

price data for routers and switches. For multi-protocol routers in particular, because the final price often

depends upon add-on modules, traditional sources of pricing data such as catalogs and magazines are

ineffective (Doms and Forman 2005). Over our sample period, the use of such modules in switches was

less frequent, so it is somewhat easier to obtain pricing data for switches. We re-estimate our models

using the switch price data from Doms and Forman (2005), to our knowledge the only study of router and

switch prices in the IS and economics literatures. Because our discrete choice model uses vendor choice

rather than model choice, we compute average quality-adjusted prices for each vendor by running the

hedonic regression in Doms and Forman (2005). We then plug the average quality-adjusted prices for

each vendor into our vendor choice model. The results of this model, shown in Appendix Table 6, are

qualitatively similar to our baseline model.

Multi-Vendor Network Environment

Another question is how heterogeneity in the installed base might influence switching costs. For

example, buyers with multi-vendor network infrastructures may have higher compatibility costs because

of the difficulty of integrating new router and switches into a complex network infrastructure. However,

such buyers may also have lower learning costs because they have internal capabilities that make it easier

for them to adapt to a vendor change. In Appendix Table 7 we examine how differences in internal firm

infrastructure influence switching costs in greater detail. We do this by interacting our measures of

incumbent vendor with a dummy that indicates the presence of multiple router vendors at the

establishment. We find that, as expected, multi-vendor buyers do have lower learning costs. While we

also find that such buyers have higher compatibility costs, the results are not statistically significant.

Growth versus Replacement [** given that the quantities are determined once a year in December, if the

router is for replacement, then it wouldn’t have been captured in our data, right? If that’s the case, then it

can actually lead to an underestimation of our switching costs. The inclusion of replacements should

strengthen our results. **]

34

Another potential question is whether the magnitude of switching costs are different when routers

are added for growth compared to when they are added to replace existing routers. Unfortunately, as

noted above, our measure of vendor choice is based upon changes in the number of routers at the

establishment. Thus, there is no direct way of showing whether routers are purchased to replace existing

routers or whether they are added for growth.

One thing we are able to do however is to examine whether the effects of switching costs are

different when the establishment is growing rapidly. These results are included in Appendix Table 8. We

compute a dummy variable that indicates whether the growth in the total number of network nodes at the

establishment is above the 75% percentile. We then interact this variable with our measures of incumbent

vendor. Our qualitative results remain the same.

6.3 Effects of Architectural Change on Switching Costs

Table 7 presents the results of our joint router-switch purchase model. As before, we present only

the estimates of our switching cost parameters. Column (1) shows how switching costs influence vendor

choice when buyers chose routers only, column (2) shows switching costs when buyers chose switches

only, and column (3) shows switching costs when buyers chose routers and switches together. Column (4)

of Table 7 shows the difference in switching costs when routers and switches are purchased separately

versus when they are purchased simultaneously.

The switching costs from prior router investments at the same establishment are greater when

buyers purchase routers alone (CE 1.6439) than when buyers purchase routers with switches (CE 1.2243);

this difference is significant at the 5% level. This suggests that the switching costs associated with prior

investments in routers are reduced when establishments make architectural changes to their network.

However, the switching costs associated with prior router investments at other firm establishments do not

change when simultaneously purchasing switches. As noted in the discussion prior to Hypothesis 5; this

result may reflect lack of coordination in vendor choice decisions among establishments in decentralized

IT organizations or differences in the timing of router and switch investment across establishments that

35

use centralized IT functions. When we reran the model for decentralized IT organizations, switching costs

associated with other establishments increased when simultaneously purchasing routers and switches,

however the difference was not statistically significant (CE 0.2702 v. 0.5753). Overall, these results

suggest that technology lock-in (but not vendor lock-in) related to an establishment’s local investments

are reduced when the establishment purchases switches and routers together, in support of hypothesis 5.

However, because of a lack of coordination among establishments, decentralized IT organizations will

continue to experience considerable switching costs associated with prior investments made by other

establishments within the same firm.

7. Discussion and Conclusion

In this paper we demonstrate that increases in the size of installed base from the incumbent

vendor can lead to significant increases in switching costs for IT buyers due to local network effects.

These switching costs are in addition to those such as learning and transaction costs that are invariant to

the size of a firm’s installed base. We demonstrated that the effect on buyer choice can be significant, and

given the increasing importance of network infrastructure, our results suggest that prior estimates of the

magnitude of switching costs for IT products may be too low. Our results also suggest that the

organization of the IT function may have significant implications for how switching costs and network

effects influence IT vendor choice. In particular, we show that when IT purchase decisions are

decentralized, the effect of IT vendor choice on other units within the same firm are discounted. In

addition, we demonstrate that changes in network architecture arising from investment in new products in

complementary layers of the business IT platform did lead to a temporary reduction in switching costs

and technology (but not vendor) lock-in. Thus, our results are consistent with prior assertions that indirect

entry—or equivalently, changes in IT architecture—can lead to changes in the structure of IT markets

(Bresnahan and Greenstein 1999; Brynjolfsson and Kemerer 1996; West and Dedrick 2000). Last, we

provide a unique robustness test that estimates how changes in the quantity of installed base over time

affect the magnitude of switching costs, which allows us to correct for spurious state dependency.

36

Our results have several implications for buyers of IT infrastructure. First of all, they suggest that

short-run decisions can have long-run implications for buyers of IT equipment. Second, another

implication of this research is that firms with the largest, most complicated networks will also have the

highest switching costs. While such users may have the greatest levels of IT sophistication (Raymond and

Paré 1992), they will also have the largest costs of ensuring interoperability between new and legacy

systems. Thus, we contribute to prior case study literature that has begun to demonstrate greater

infrastructure development costs for large firms (Weill and Broadbent 1998). Last, we provide further

evidence on the value of centralizing IT infrastructure decisions. In particular, we provide quantitative

evidence that decentralized IT organizations may not fully internalize the costs of choosing a new router

or switch vendor.

Our results advance existing literature that has sought to understand how technological

innovations in complementary layers of IT infrastructure can engender changes in market structure

(Bresnahan and Greenstein 1999; Brynjolfsson and Kemerer 1996; West and Dedrick 2000). Prior work

in this area has used case studies of market evolution in mainframes, PCs, and application software to

provide evidence in support of their hypotheses. However, because they describe market-level

phenomena, direct empirical tests of these hypotheses have been difficult. We provide a separate and

complementary approach that uses buyer behavior to suggest implications for new product introduction.

A necessary (though not necessarily sufficient) condition for market structure change is that new

innovations reduce the importance of prior sunk cost investments or network effects on vendor choice. By

showing how buyer behavior changes with investment in a complementary product, our research provides

evidence in support of this necessary condition. However, we demonstrated that these switching costs

were still nonzero, and they tied router buyers to investments made in other products and other

establishments within the organization. Thus, our results show that although new product introductions

may provide a window of lower switching costs, in the long run the fundamental issues of compatibility

and learning costs will continue to assert themselves. These results have implications for sellers of IT

hardware and software, as they are illustrative of how introduction of complementary innovations may

37

influence demand for existing products.

Our findings have other implications for sellers. In particular, our results that there exist

significant switching costs arising from prior investments in complementary products and that buyers

save by purchasing routers and switches at the same time from the same vendor have an interesting

strategic implication: they suggest that a vendor with broader product line may have an advantage over a

vendor with a smaller set of products in attracting buyers, even if the products from the two vendors are

identical.

Our results suggest several potential avenues for future research. For one, our research focuses on

identifying switching costs, and does not speak to the implications of switching costs for firm behavior.

One extension would be to examine how the existence of switching costs affects the product line

decisions of each of these competitors. Further, future work should examine whether the existence of

switching costs gives rise to suboptimally low entry or changes in pricing behavior, as theory suggests.

Recent work has made progress on how both switching costs and network effects influence pricing

behavior (Stango 2002; Knittel and Stango 2008), but more work needs to be done.

In addition to the implications of our results for seller behavior, there are several direct extensions

to our model that would be valuable. For one, understanding how switching costs vary for early versus

late adopters of a new technology (such as switches) would be valuable. One aspect of the buyer-vendor

relationship left unexplored in this paper is the role of third-party network managers and IT outsourcing

on vendor choice. IT outsourcing may strengthen the effects of incumbency if third-party vendors are

associated with particular vendors; alternatively, third-party network managers or designers may be able

to help their clients overcome switching costs. The framework and data used here are well-suited to

address these questions.

References

Abrahamson, E., L. Rosenkopf. 1993. Institutional and competitive bandwagons: Using mathematical modeling as a tool to explore innovation diffusion. The Academy of Management Review 18(3): 487-517.

Agarwal, R., V. Sambamurthy. 2002. Principles and Models for Organizing the IT Function. MIS

Quarterly Executive 1(1): 1-16.

38

Brehm., J.W. 1956. Post-Decision Changes in the Desirability of Alternatives. Journal of Abnormal and

Social Psychology 52: 384-389.

Bresnahan, T. 2001. The Economics of the Microsoft Case. Working Paper, Department of Economics, Stanford University.

Bresnahan, T., S. Greenstein. 1999. Technological Competition and the Structure of the Computer Industry. Journal of Industrial Economics 47(1): 1-39.

Breuhan, A. 1997. Innovation and the Persistence of Technological Lock-In. Ph.D. diss., Stanford University, Palo Alto.

Brynjolfsson, E., L. Hitt. 2003. Computing Productivity: Firm-Level Evidence. Review of Economics and

Statistics 85(4): 793-808.

Brynjolfsson, E., Kemerer, C. 1996. Network Externalities in Microcomputer Software: An Econometric Analysis of the Spreadsheet Market. Management Science 42: 1627-1647.

Chen, P.-Y., C. Forman. 2006. Can Vendors Influence Switching Costs and Compatibility in an Environment with Open Standards? MIS Quarterly 30(Special Issue): 541-562.

Chen, P.-Y., L. M. Hitt. 2002. Measuring Switching Costs and the Determinants of Customer Retention in Internet-Enabled Businesses: A Study of the Online Brokerage Industry. Information Systems Research

13(3): 255-274.

Chen, P.-Y., L. M. Hitt. 2006. Information Technology and Switching Costs. In Handbook of Information

Systems, Volume 1: Handbook of Economics and Information Systems, (ed.) Terrence Hendershott. Amsterdam: Elsevier, pp. 437-470.

Dewan, S., C. Shi, V. Gurbaxani. 2007. Investigating the Risk-Return Relationship of Information Technology Investment: Firm-Level Empirical Analysis. Management Science 53(12): 1829-1842.

Dhar, V., A. Sundararajan. 2007. Information Technologies in Business: A Blueprint for Education and Research. Information Systems Research 18(2): 125-141.

Doms, M., C. Forman. 2005. Prices for local area network equipment. Information Economics and Policy

17: 365-388.

Farrell, J., P. Klemperer. 2007. Coordination and Lock-in: Competition with Switching Costs and Network Effects. In Handbook of Industrial Organization, Volume 3, (eds.) Mark Armstrong and Robert H. Porter. Amsterdam: North Holland-Elsevier, p. 1970-1994.

Fitzgerald, J. and A. Dennis. 1996. Business Data Communications and Networking. New York: John Wiley & Sons.

Forman, C. 2005. The Corporate Digital Divide: Determinants of Internet Adoption. Management Science

51(4): 641-654.

Forman, C., A. Goldfarb, and S. Greenstein. 2005. How did location affect adoption of the commercial Internet? Global village vs. urban leadership. Journal of Urban Economics 58: 389-420.

Forman, C., A. Goldfarb, and S. Greenstein. 2008. Understanding the Inputs into Innovation: Do Cities Substitute for Internal Firm Resources. Journal of Economics and Management Strategy. Forthcoming.

Gallaugher, J.M., Y.-M. Wang. 2002. Understanding network effects in software markets: Evidence from web server pricing. MIS Quarterly 26(4): 303-327.

Gandal, N. 2001. The Dynamics of Competition in the Internet Search Engine Market. International

Journal of Industrial Organization 19: 1103-1117.

39

Gawer, A., M. Cusumano 2002. Platform Leadership: How Intel, Microsoft, and Cisco Drive Industry

Innovation. Boston: Harvard University Press.

Goldfarb, A. 2006a. State Dependence at Internet Portals. Journal of Economics and Management

Strategy 15(2): 317-352.

Golfdarb, A. 2006b. The Medium-Term Effects of Unavailability. Quantitative Marketing and Economics

4(2): 143-171.

Greene, W. 2002. Econometric Analysis, 5th Edition. Upper Saddle River: Prentice-Hall.

Greenstein, S. M. 1993. Did installed base give an incumbent any (measurable) advantages in federal computer procurement? RAND Journal of Economics 24: 19-39.

Greenstein, S. M. 2001. Technological Mediation and Commercial Development in the Early Internet Access Market. California Management Review 43(2): 75-94.

Guadagi, P. M. and J. D. Little. 1983. A Logit Model of Brand Choice Calibrated on Scanner Data. Marketing Science. 2(3): 203-238.

Harte Hanks. 2000. Relational Database Format. Harte Hanks Market Intelligence database.

Heckman, J. J. 1981. Heterogeneity and State Dependence. In Studies in Labor Markets, (ed.) Sherwin Rosen. Chicago: The University of Chicago Press.

Hendel, I. 1999. Estimating Multi-Discrete Choice Models: An Application to Computerization Returns. Review of Economic Studies 66: 423-446.

Hitt, L. 1999. Information Technology and Firm Boundaries: Evidence from Panel Data. Information

Systems Research 10(2): 134-149.

Jain, D., N. Vilcassim, P. Chintagunta. 1994. A Random-Coefficients Logit Brand-Choice Model Applied to Panel Data. Journal of Business and Economic Statistics 12: 317-328.

Kauffman, R., J. McAndrews, and Y.-M. Wang. 2000. Opening the “Black Box” of Network Externalities in Network Adoption. Information Systems Research 11(1): 61-82.

Keane, M. P. 1997. Modeling Heterogeneity and State Dependence in Consumer Choice Behavior. Journal of Business and Economic Statistics 15: 310-327.

Klemperer, P. 1992. Equilibrium Product Lines: Competing Head-to-Head May be Less Competitive. American Economic Review 82: 740-755.

Klemperer, P. 1995. Competition when Consumers have Switching Costs: An Overview with Applications to Industrial Organization, Macroeconomics, and International Trade. Review of Economic

Studies 62: 515-539.

Knittel, C., V. Stango. 2008. How Does Incompatibility Affect Prices? Evidence from ATMs. Journal of

Industrial Economics, Forthcoming.

Kretschmer, T. 2004. Upgrading and Niche Usage of PC Operating Systems. International Journal of

Industrial Organization 22(8-9): 1155-1182.

Kretschmer, T. 2005. Competing technologies in the database systems market. NET Institute Working Paper #05-17.

Lee, D., H. Mendelson. 2007. Adoption of Information Technology Under Network Effects. Information

Systems Research 18(4): 395-413.

Leinwald, A., B. Pinsky. 2000. Cisco Router Configuration. Cisco Press.

40

Liebowitz, S.J., S.E. Margolis. 1994. Network Externality: An Uncommon Tragedy. The Journal of

Economic Perspectives. 8(2): 133-150.

Panko, R. 2001. Business Data Communications and Networking. Upper Saddle River: Prentice-Hall.

Petrosky, M. 1996. Cisco pursues a vision of being all things to all users. Network World Fusion.

Prince, J. 2008. Repeat Purchase Amid Rapid Quality Improvement: Structural Estimation of Demand for Personal Computers. Journal of Economics and Management Strategy 17(1): 1-33.

Raymond, L., G. Paré. 1992. Measurement of Information Technology Sophistication in Small Manufacturing Businesses. Information Resources Management Journal 5(2): 4-16.

Revelt, D., K. Train. 1998. Mixed Logit with Repeated Choices: Households’ Choice of Appliance Efficiency Level. Review of Economics and Statistics 80(4): 647-657.

Sambamurthy, V., R. W. Zmud. 2000. Research Commentary: The Organizing Logic for an Enterprise’s IT Activities in the Digital Era—A Prognosis of Practice and a Call for Research. Information Systems

Research 11(2): 105-114.

Samuelson, W., R. Zeckhauser. 1988. Status Quo Bias in Decision Making. Journal of Risk and

Uncertainty 1: 7-59.

Shapiro, C., H. Varian. 1999. Information Rules: A Strategic Guide to the Network Economy. Boston: Harvard University Press.

Stango, V. 2002. Pricing with Consumer Switching Costs: Evidence from the Credit Card Market. Journal of Industrial Economics 50(4): 475-492.

Strang, D., M. W. Macy. 2001. In search of excellence: Fads, success stories, and adaptive emulation. The

American Journal of Sociology 107(1): 147-182.

Sundararajan, A. 2007. Local Network Effects and Complex Network Structure. The B.E. Journal of

Theoretical Economics 7(1) (Contributions): Article 46. Available at: http://www.bepress.com/bejte/vol7/iss1/art46.

Tolly, K. 2000. Lack of proven switch interoperability hurts aggressors. Network Word, September 18, 2000.

Train, K. 2003. Discrete Choice Methods with Simulation. Cambridge: Cambridge University Press.

Viswanathan, S. 2005. Competing Across Technology-Differentiated Channels: The Impact of Network Externalities and Switching Costs. Management Science 51(3): 483-496.

Waltner, C. 2006. Services Shine for Cisco Channel Partners. News@Cisco, March 15, 2006. Available at http://newsroom.cisco.com/dlls/2006/hd_031506.html.

Weill, P., M. Broadbent. 1998. Leveraging the New IT Infrastructure: How Market Leaders Capitalize on

Information Technology. Boston: Harvard Business School Press.

Weill, P., M. Subramani, and M. Broadbent. 2002. Building IT Infrastructure for Strategic Agility. MIT

Sloan Management Review 44(1): 57-65.

West, J., J. Dedrick. 2000. Innovation and Control in Standards Architectures: The Rise and Fall of Japan’s PC-98. Information Systems Research 11(2): 197-216.

Wickre, P. 1996. Stick to true standards-based solutions. Network World Fusion, July 22, 1996.

Zhu, K., K. L. Kraemer, V. Gurbaxani, and S. Xu. 2006. Migration to Open-Standard Interorganizational Systems: Network Effects, Switching Costs, and Path Dependency. MIS Quarterly 30(Special Issue): 515-539.

41

Router Vendor Choice at time t

Costs of switching away from j

Fixed Costs of Choosing a Vendor Different from j at Establishment

Ai

Costs Changing With Installed Base of Vendor j at Establishment

B(Nij)

Fixed Costs of Choosing a Vendor Different from j throughout the Organization

Ci

Costs Changing with Installed Base of Vendor j throughout Firm

D(Nij)

Switching Costs Incurred When Infrastructure Decisions are decentralized.

Switching Costs Incurred When Infrastructure Decisions are centralized.

• Learning to Use New Vendor

• Transaction Costs of Switching Suppliers

• Buyer Inertia

• Costs of maintaining compatibility with other devices

• Costs of maintaining compatibility with other devices

• Learning to Use New Vendor

• Transaction Costs of Switching Suppliers

• Buyer Inertia

Figure 1: Research Model

H1

H2 H3H4

42

Table 1: Variable Constructs for Router Choice

Variable Hypoth

Tested

Construction/Identification Prediction

Dummy indicating vendor incumbency at establishment

1 =1 for vendor choices different from incumbent vendor at establishment

0E

s >

Log of total installed base from vendor at establishment

2 = log of total routers at establishment from incumbent vendors I when buyer makes choice j I≠

0E

t >

Log of total installed base from router vendor at other firm establishments

3 = log of total routers from incumbent vendors I at other firm establishments when buyer makes choice j I≠

0F

t >

Dummy indicating vendor incumbency at establishment

4 =1 for vendor choices different from incumbent vendor at other establishments in the same firm

0F

s > for

centralized IT

Comparison of dummy indicating vendor incumbency at establishment

5 =1 for vendor choices different from incumbent vendor at establishment

( )

( )

[1,1]

[1,1]

E R

E R

s

s

>

′

Table 2a: Description of Control Variables—Routers

Variable Description Mean Std. Dev.

Min. Max

Dummy indicating SIC 60 0.1315 0.3381 0 1 Dummy indicating SIC 61 0.0566 0.2311 0 1 Dummy indicating SIC 62 0.0448 0.2070 0 1 Dummy indicating SIC 63 0.1653 0.3716 0 1 Dummy indicating SIC 64 0.0411 0.1987 0 1 Dummy indicating SIC 65 0.0191 0.1369 0 1 Dummy indicating SIC 67 0.0463 0.2102 0 1 Dummy indicating SIC 73 0.1631 0.3696 0 1 Dummy indicating SIC 87 0.1514 0.3585 0 1 Log of number of employees* 0.6441 0.0866 0.4605 0.9741 Dummy indicating multi-establishment organization 0.6591 0.4742 0 1 Dummy indicating branch of larger corporation 0.1007 0.3010 0 1 Dummy indicating large-scale computing applications at establishment

0.6885 0.4633 0 1

Log of total number of data lines at establishment* 0.2407 0.1890 0 0.8926 Dummy indicating 1997 0.3167 0.4654 0 1 Dummy indicating 1998 0.3292 0.4701 0 1

Note: Unit of observation in this table is an establishment. To identify these variables in the mixed logit model, these variables are interacted with vendor dummies. Number of observations is 1361. *Indicates data have been divided by 10.

43

Table 2b: Description of Control Variables—Switches

Variable Description Mean Std. Dev.

Min. Max

Dummy indicating SIC 60 0.0778 0.2679 0 1 Dummy indicating SIC 61 0.0556 0.2291 0 1 Dummy indicating SIC 62 0.0411 0.1986 0 1 Dummy indicating SIC 63 0.1289 0.3351 0 1 Dummy indicating SIC 64 0.0378 0.1907 0 1 Dummy indicating SIC 65 0.0256 0.1578 0 1 Dummy indicating SIC 67 0.0444 0.2061 0 1 Dummy indicating SIC 73 0.1489 0.3560 0 1 Dummy indicating SIC 87 0.2089 0.4066 0 1 Log of number of employees* 0.6481 0.0861 0.4605 0.9210 Dummy indicating multi-establishment organization 0.6311 0.4826 0 1 Dummy indicating branch of larger corporation 0.1367 0.3435 0 1 Dummy indicating large-scale computing applications at establishment

0.7156 0.4512 0 1

Log of total number of data lines at establishment* 0.2364 0.1837 0 0.8926 Dummy indicating 1997 0.2989 0.4578 0 1 Dummy indicating 1998 0.4589 0.4984 0 1

Note: Unit of observation in this table is an establishment. To identify these variables in the mixed logit model, these variables are interacted with vendor dummies. Number of observations is 900. *Indicates data have been divided by 10.

Table 3a: Mixed Logit Results of Router Choice (Dependent variable: router vendor choice)

Model/Type of prior investment

(1) (2) (3)

Router Switch Router Switch Router Switch

Dummy indicating installed base of 0.9551** . . . 1.6027** 1.1757** 0.9959** 0.1936 vendor at establishment (0.2792) . . . (0.1471) (0.2834) (0.2799) (0.5871) Log of total installed base from 5.9780** . . . . . . . . . 4.6926* 7.2841+ vendor at establishment (2.0362) . . . . . . . . . (2.0388) (3.8356) Dummy indicating installed base of 0.2694 . . . 0.9706** 0.3082 0.2017 0.3934

vendor at other firm establishments (0.2473) . . . (0.1755) (0.3238) (0.2509) (0.4414) Log of total installed base from 4.0863** . . . . . . . . . 4.4158** -1.6120 vendor at other firm establishments (1.0387) . . . . . . . . . (1.0807) (1.9156)

N 1361 1361 1361

Log Likelihood -1137.854 -1141.466 -1127.603 Pseudo R-Sq 0.1342 0.1315 0.1420 Notes: This table reports the mixed logit results of three empirical models, numbered (1) through (3) in the second row of the table. Model 1 measures the impact of prior router investment on router vendor choice. Model 2 and 3 measures the impact of prior router and switch investments on router vendor choice. All models include establishment controls. Estimates are maximum simulated likelihood with asymptotic standard errors in parentheses. Coefficients on brand dummies are normally distributed and independent across establishments and choices, but constant for establishments over time. Pseudo R-squared is calculated by comparing the likelihood function to one that includes constant terms only. +Indicates significance at 10% level. *Indicates significance at 5% level. **Indicates significance at 1% level.

44

Table 3b: Mixed Logit Results of Switch Choice (Dependent variable: switch vendor choice)

Model/Type of prior investment

(1) (2) (3)

Router Switch Router Switch Router Switch

Dummy indicating installed base of . . . 1.1247** 0.8135** 1.8550** 0.1191 1.0523*

vendor at establishment . . . (0.4302) (0.1523) (0.2032) (0.2391) (0.4038) Log of total installed base from . . . 7.0157* . . . . . . 5.3801** 5.9113+ vendor at establishment . . . (3.3358) . . . . . . (1.6047) (3.2626) Dummy indicating installed base of . . . -0.1067 0.2013 0.5733* 0.2118 -0.1846

vendor at other firm establishments . . . (0.4031) (0.2099) (0.2825) (0.3124) (0.4196) Log of total installed base from . . . 5.2168* . . . . . . -0.0413 5.3963* vendor at other firm establishments . . . (2.1976) . . . . . . (1.3652) (2.3021)

N 900 900 900 Log Likelihood -1095.296 -1080.278 -1068.450 Pseudo R-Sq 0.1199 0.1341 0.1415 Notes: This table reports the mixed logit results of three empirical models, numbered (1) through (3) in the second row of the table. Model 1 measures the impact of prior switch investment on switch vendor choice. Model 2 and 3 measures the impact of prior switch and router investments on router vendor choice. All models include establishment controls. Estimates are maximum simulated likelihood with asymptotic standard errors in parentheses. Coefficients on brand dummies are normally distributed and independent across establishments and choices, but constant for establishments over time. Pseudo R-squared is calculated by comparing the likelihood function to one that includes constant terms only. +Indicates significance at 10% level. *Indicates significance at 5% level. **Indicates significance at 1% level.

Table 4: Marginal Effects of a 0/1 Change in Cisco Installed Base

On the Probability of Choosing Cisco (Percent)

Model/Type of prior investment

(1) (2) (3)

Router Switch Router Switch Router Switch

Dummy indicating installed base of 14.58 . . . 20.08 18.10 14.84 3.71 vendor at establishment Log of total installed base from 20.23 . . . . . . . . . 19.28 15.40 vendor at establishment Dummy indicating installed base of 4.23 . . . 12.82 5.64 3.16 7.21

vendor at other firm establishments Log of total installed base from 8.31 . . . . . . . . . 7.61 4.46 vendor at other firm establishments Notes: Marginal effects are calculated from simulations using estimates of Table 3a. Each cell shows that marginal effect on the probability of choosing Cisco of a change in Cisco installed base for the variable described in the row. The marginal effects of fixed switching costs (Ai and Ci) in rows 1 and 3 are computed assuming all other variables=0; the marginal effects of switching costs arising from compatibility (B(Nij) and D(Nij)) in rows 2 and 4 are computed assuming the dummies in rows 1 and 2 are equal to 1. Numbers (1), (2), and (3) above data correspond to model numbers in Table 3; for each model, we report marginal effects for prior investments in routers and switches.

45

Table 5: Mixed Logit Results of Router Choice Local/Parent IT Decision Split

Type of prior investment/decision type

Local Decisions Firm Decisions

Vendor choice Establishment

(1)

Vendor choice Other

Establishments (2)

Vendor choice Establishment

(3)

Vendor choice Other

Establishments (4)

Dummy indicating installed base of 1.2102** -0.0758 0.6201 0.9671* router vendor at establishment (0.3177) (0.3243) (0.6553) (0.4921)

Log of total router installed base from vendor 5.5941* 3.3411* 1.4529 5.5547** (2.1955) (1.3679) (4.6376) (1.8396)

N 1361 Log Likelihood -982.842 Pseudo R-Sq 0.1528

Notes: 1. This table reports the mixed logit results of an extension of Model (2) in Table 3a that allow switching cost parameters to vary based on whether purchase decisions are centralized (firm decision) or not (local decision). 2. Details of estimation are as in Table 3. +Indicates significance at 10% level. *Indicates significance at 5% level. **Indicates significance at 1% level.

Table 6: Effects of Increases of Installed Base on Switching Costs

Model/Type of prior investment

(1) (2) (3)

Router Switch Router Switch Router Switch

Dummy indicating installed base of 1.4455 . . . 1.4712** -0.5373 1.6055 -0.6420 vendor at establishment (0.5283)** . . . (0.5385) (1.5689) (0.5545) (1.5760)

Log of total installed base from 6.0616 . . . 5.0034 9.0716 4.0969 9.7925 vendor at establishment in Year 1996 (4.2417) . . . (4.1896) (13.4125) (4.0051) (13.7499)

Log of difference in total installed base 7.0492* . . . 6.8015* 6.6903* 6.8376* 6.7849*

from vendor at establishment (2.9777) . . . (2.9082) (3.1906) (2.9433) (3.2616)

Dummy indicating installed base of . . . . . . . . . . . . 0.8856* 0.6526

vendor at other firm establishments . . . . . . . . . . . . (0.4618) (0.8869)

Log of total installed base from . . . . . . . . . . . . 0.0878 -2.2889 vendor at other firm estab. in 1996 . . . . . . . . . . . . (2.0324) (4.5719)

Log of difference in total installed base . . . . . . . . . . . . 2.3862 -0.3554

from vendor at other establishments . . . . . . . . . . . . (2.2994) (2.3545)

N 704 704 704 Log Likelihood -561.4786 -558.8625 -552.9471 Pseudo R-Sq 0.1356 0.1397 0.1488

Notes: 1. This table reports three empirical models based on the strategy in section 6.2 to control for unobserved buyer-vendor match that are constant over time. Model 1 measures how prior router investment influence router vendor choice. Models 2 and 3 consider prior investments in both routers and switches and their impacts on router vendor choice. Parameter estimates highlighted in bold are our focus. 2. Estimates are maximum simulated likelihood with asymptotic standard errors in parentheses. Coefficients on brand dummies are normally distributed and independent across establishments and choices, but constant for establishments over time. Pseudo R-squared is calculated by comparing the likelihood function to one that includes constant terms only. Model is estimated over the period 1997-1998. +Indicates significance at 10% level.

*Indicates significance at 5% level. **Indicates significance at 1% level.

46

Table 7: Mixed Logit Model of Router/Switch Vendor Choice

Routers Only (1)

Switches Only (2)

Routers & Switches

(3)

Difference Btw (1) & (3)

(4)

Dummy indicating installed base of 1.6439** 1.2243** 0.4197* router vendor at establishment (router choice)

(0.1554) (0.1720) (0.1955)

Dummy indicating installed base of 0.7663** 0.7833** -0.0170 router vendor at other firm establishments (router choice)

(0.1754) (0.2140) (0.2107)

Dummy indicating installed base of 1.1046** 0.0779 switch vendor at establishment (router choice)

(0.2286) (0.2581)

Dummy indicating installed base of 0.4681 0.1715 switch vendor at other firm establishment (router choice)

(0.2870) (0.3579)

Dummy indicating installed base of 0.0240 0.5651** router vendor at establishment (switch choice)

(0.1388) (0.1447)

Dummy indicating installed base of 0.9998** 0.3447+ router vendor at other firm establishments (switch choice)

(0.2131) (0.1832)

Dummy indicating installed base of 1.8803** 1.8874** switch vendor at establishment (switch choice)

(0.2549) (0.2779)

Dummy indicating installed base of 0.0835 0.0304 switch vendor at other firm establishment (switch choice)

(0.3035) (0.3210)

Same router and switch vendor 1.5622** (0.0854) N 1333 Log Likelihood -3803.146 Pseudo R-squared 0.0662

Notes: 1. This table reports the results of a mixed logit model, which separate buyer choices into three subsets: (1) purchasing routers only, (2) switches only and (3) joint purchases of routers and switches. 2.Estimates are maximum simulated likelihood with asymptotic standard errors in parentheses. Coefficients on brand dummies are normally distributed and independent across establishments and choices, but constant for establishments over time. Random error terms added in each nest with variances constrained to be identical across nests. Pseudo R-squared is calculated by comparing the likelihood function to one that includes constant terms only. +Indicates significance at 10% level. *Indicates significance at 5% level. **Indicates significance at 1% level.

1

Appendix Table 1

Harte Hanks Sample Versus the Census of Business Establishments

Analysis Sample Complete Sample Census

# of Establishments with over 100 employees 1361 29071 96,699

% MSA 96.5% 94.8% 85.9%

% > 500 Employees Given Have 100 Employees

62.7% 16.2% 11.6%

% SIC 27 18.1% 17.4% 4.1%

% SIC 60 13.5% 11.2% 22.0%

% SIC 61 5.7% 3.0% 2.6%

% SIC 62 4.5% 3.2% 2.4%

% SIC 63 16.5% 10.0% 2.1%

% SIC 64 4.1% 4.2% 3.2%

% SIC 65 1.9% 2.9% 12.8%

% SIC 67 4.6% 2.9% 1.8%

% SIC 73 16.3% 27.6% 29.4%

% SIC87 15.1% 17.7% 19.7%

2

Appendix Table 2: Comparison of Distribution of Sales of Routers

1996 Sample (1)

1996 Total (2)

1997 Sample (3)

1997 Total (4)

1998 Sample (5)

1998 Total (6)

3Com 4.86% 6.66% 4.80% 5.78% 3.62% 5.10%

Bay Networks 15.51% 9.03% 12.45% 6.53% 14.57% 7.47%

Cisco 66.38% 46.98% 73.72% 47.89% 74.29% 48.02%

Other 13.25% 37.33% 9.03% 39.80% 7.52% 39.41% Columns (1), (3), and (5) show the percentage of total unit sales sold by vendor in our sample. Columns (2), (4), and (6) show the percentage of unit sales from the Dataquest Quarterly market reports, summed over the quarters within the year in question.

Appendix Table 3: Comparison of Distribution of Sales of Switches

1996 Sample (1)

1996 Total (2)

1997 Sample (3)

1997 Total (4)

1998 Sample (5)

1998 Total (6)

3Com 11.42% 23.37% 28.23% 23.95% 18.76% 21.05%

Bay Networks 16.12% 8.71% 12.02% 12.31% 20.14% 13.07%

Cisco 58.93% 29.81% 43.00% 32.00% 39.22% 38.14%

Other 13.53% 38.11% 16.75% 31.74% 21.88% 27.74% Columns (1), (3), and (5) show the percentage of total unit sales sold by vendor in our sample. Columns (2), (4), and (6) show the percentage of unit sales from the Dataquest Quarterly market reports, summed over the quarters within the year in question.

3

Appendix Table 4: Mixed Logit Results of Router Choice (Dependent variable: router vendor

choice)Examination of Alternative Hypothesis of Supply Side Behavior Model

Type Total Nodes Quantity Purchased Total Employment

(1) (2) (3) (4) (5) (6) (7) (8)

1.6100** 1.3956** 1.4663** 1.3271** 1.0965** 0.9550** 1.5304** 1.3254** Dummy indicating installed base of vendor at establishment – Baseline

(0.2270) (0.2356) (0.3250) (0.3267) (0.3475) (0.3500) (0.3518) (0.3545)

0.4298 0.3204 -0.0138 -0.0258 0.5807 0.6016 0.3119 0.3690 Dummy indicating installed base of vendor at establishment – Small

(0.5571) (0.5799) (0.6472) (0.6434) (0.5276) (0.5261) (0.4758) (0.4788)

4.3391** 4.6974** 3.8886+ 4.2329* 4.8376* 5.3061 * 3.3836 4.1242+ Log of total installed base from vendor at establishment – Baseline

(1.5710) (1.6331) (2.1195) (2.1481) (2.2402) (2.2731) (2.2957) (2.3312)

9.4198* 9.0959+ 12.2340+ 11.6349+ 12.5500** 11.5206* 10.1856* 9.4992* Log of total installed base from vendor at establishment – Small

(4.7423) (4.8396) (6.6331) (6.5383) (5.0046) (5.0265) (4.1839) (4.1961)

0.4519* 0.3178 0.0717 0.1243 Dummy indicating installed base of vendor at other establishments – Base

(0.2118) (0.2963) (0.3159) (0.2946)

-0.4346 0.0939 0.6150 0.5914 Dummy indicating installed base of vendor at other establishments – Small

(0.8741) (0.4513) (0.4045) (0.4523)

7.9048** 4.0900** 4.7208** 4.6468** Log of total installed base from vendor at other establishments – Base

(0.9151) (1.2204) (1.2436) (1.2274)

11.1476** . . . 4.1026 * . . . 2.6236 2.5218 Log of total installed base from vendor at other establishments – Small

(4.1182) . . . (1.9000) . . . (1.8205) (1.8954)

N 1361 1361 1361 1361 1361 1361 1361 1361 Log Likelihood -1646.415 -1510.703 -1160.143 -1135.284 -1161.522 -1136.194 -1160.678 -1135.931 Pseudo R-Sq 0.1273 0.1992 0.1173 0.1362 0.1162 0.1355 0.1169 0.1357

Notes: Models 1 and 2 measures the robustness of our results to small routers only. Model 3 and 4 examines if our results are different in establishments with a small number of network nodes (in the bottom third of the distribution). Models 5 and 6 look for differences between establishments that purchase only one router versus those who purchase more than one. Models 7 and 8 examine establishments with a small number of employees (in the bottom third of the distribution. All models include establishment controls. Estimates are maximum simulated likelihood with asymptotic standard errors in parentheses. Coefficients on brand dummies are normally distributed and independent across establishments and choices, but constant for establishments over time. Pseudo R-squared is calculated by comparing the likelihood function to one that includes constant terms only. +Indicates significance at 10% level. *Indicates significance at 5% level. **Indicates significance at 1% level.

4

Appendix Table 5: Mixed Logit Results of Router Choice (Dependent variable: router vendor choice)

These are results when allow two routers to be chosen

(1) (2) (3)

Router1 Router 2 Router 1 Router 2 Switch 1 Switch 2 Router 1 Router 2 Switch 1 Switch 2

Dummy indicating installed base of 1.4791** 3.0326** 1.8512** 3.0684** 1.0653** 0.3559* 1.5024** 3.0172** 0.4175 0.2483 vendor at establishment (0.2365) (0.4341) (0.1327) (0.2543) (0.2491) (0.3811) (0.2366) (0.4431) (0.4832) (0.7027) Log of total installed base from 3.5801* 0.6821 . . . . . . . . . . . . 2.7621+ 0.3799 4.4694 0.6411 vendor at establishment (1.5846) (2.8068) . . . . . . . . . . . . (1.5897) (2.8530) (3.0303) (4.3076) Dummy indicating installed base of 1.0512** 3.5479** 0.8827** 3.3413** 0.9598* -0.1953 1.0960** 3.7238** 1.3785+ -1.0316

vendor at other firm establishments (0.3088) (0.7352) (0.1963) (0.3982) (0.4325) (0.6646) (0.3106) (0.7754) (0.7658) (1.2804) Log of total installed base from -1.4986 -1.6577 . . . . . . . . . . . . -1.5753 -2.7197 -3.0273 5.5567 vendor at other firm establishments (1.8078) (3.8949) . . . . . . . . . . . . (1.8209) (4.2907) (4.3346) (7.0745) N 1517 1517 1517 Log Likelihood -2378.763 -2369.868 -2366.080 Notes: This table reports the mixed logit results of three empirical models, numbered (1) through (3) in the second row of the table. As in Table 3a, Model 1 measures the impact of prior router investment on router vendor choice. Models 2 and 3 measures the impact of prior router and switch investments on router vendor choice. Router 1 and Router 2 indicate the effect of installed base of routers on first and second router choice; switch 1 and switch 2 can be interpreted similarly. All models include establishment controls. Estimates are maximum simulated likelihood with asymptotic standard errors in parentheses. Coefficients on brand dummies are normally distributed and independent across establishments and choices, but constant for establishments over time. +Indicates significance at 10% level. *Indicates significance at 5% level. **Indicates significance at 1% level.

5

Appendix Table 6: Multinomial Logit Results of Switch Choice (Dependent variable: switch vendor

choice), Excludes Vendor Fixed Effects but Includes Price

Model/Type of prior investment

(1) (2) (3)

Router Switch Router Switch Router Switch

Dummy indicating installed base of . . . 1.2722** 0.7694** 1.9225** 0.0640** 1.2781** vendor at establishment . . . (0.4072) (0.1201) (0.1886) (0.2170) (0.4011) Log of total installed base from . . . 6.2862* . . . . . . 5.4002* 4.7744 vendor at establishment . . . (3.1992) . . . . . . (1.3973) (3.0826) Dummy indicating installed base of . . . -0.2554 0.2322 0.4595* 0.1318 -0.2729

vendor at other firm establishments . . . (0.3824) (0.1924) (0.2562) (0.2852) (0.3916) Log of total installed base from . . . 5.4951** . . . . . . 0.5605 5.1313* vendor at other firm establishments . . . (2.0421) . . . . . . (1.2509) (2.1030) Quality-Adjusted Price . . . 8.3825 . . . 9.1606 . . . 11.7720+ . . . (6.5114) . . . (6.5452) . . . (6.6275)

N 900 900 900 Log Likelihood -1115.455 -1099.322 -1085.456 Pseudo R-Sq 0.1037 0.1167 0.1278 Notes: This table reports the multinomial logit results of three empirical models, numbered (1) through (3) in the second row of the table. Models exclude vendor fixed effects but include quality-adjusted price. Model 1 measures the impact of prior router investment on router vendor choice. Model 2 and 3 measures the impact of prior router and switch investments on router vendor choice. All models include establishment controls. Estimates are maximum likelihood with asymptotic standard errors in parentheses. Pseudo R-squared is calculated by comparing the likelihood function to one that includes constant terms only. +Indicates significance at 10% level. *Indicates significance at 5% level. **Indicates significance at 1% level

6

Appendix Table 7

Effects of Heterogeneous Environment (Dependent variable: router vendor choice) Model/Type of prior investment

(1) (2)

Router Switch Router Switch

Dummy indicating installed base of 1.4674** . . . 1.8455** 1.5083** vendor at establishment (0.3933) . . . (0.1875) (0.3864) Dummy indicating installed base of -0.9819+ . . . -0.5120+ -0.6862 vendor at establishment*Multi-Vendor (0.5741) . . . (0.3024) (0.5621) Log of total installed base from 4.2169 . . . . . . . . . vendor at establishment (2.8776) . . . . . . . . . Log of total installed base from 3.2034 . . . . . . . . . vendor at establishment*Multi-Vendor (3.9835) . . . . . . . . .

N 1361 1361 Log Likelihood -1160.702 -1156.388 Pseudo R-Sq 0.1168 0.1201

Notes: This table reports the mixed logit results of three empirical models, numbered (1) and (2) in the second row of the table. Models examine how baseline results change in a multi-vendor environment. Model 1 measures the impact of prior router investment on router vendor choice. Model 2 measures the impact of prior router and switch investments on router vendor choice. All models include establishment controls. Estimates are maximum simulated likelihood with asymptotic standard errors in parentheses. Coefficients on brand dummies are normally distributed and independent across establishments and choices, but constant for establishments over time. Random error terms added in each nest with variances constrained to be identical across nests. Pseudo R-squared is calculated by comparing the likelihood function to one that includes constant terms only. +Indicates significance at 10% level. *Indicates significance at 5% level. **Indicates significance at 1% level.

7

Appendix Table 8

Explores Differences in Switching Costs for High Growth Locations (dependent variable: router

vendor choice

Model/Type of prior investment

(1) (2)

Router Switch Router Switch

Dummy indicating installed base of 1.0022** . . . 1.5660** 1.1066** vendor at establishment (0.2963) . . . (0.1578) (0.3065) Dummy indicating installed base of 0.6475 . . . 0.6489 0.6897 vendor at establishment*High Growth in (0.8645) . . . (0.4263) (0.8291) Total Nodes Log of total installed base from 5.5972** . . . . . . . . . vendor at establishment (2.1607) . . . . . . . . . Log of total installed base from 0.3910 . . . . . . . . . vendor at establishment*High Growth in (5.5919) . . . . . . . . . Total Nodes

N 1361 1361 Log Likelihood -1161.947 -1157.176 Pseudo R-Sq 0.1159 0.1195

Notes: This table reports the mixed logit results of three empirical models, numbered (1) through (2) in the second row of the table. These models explore if results are different when number of nodes at the establishment is growing rapidly. “High Growth in Total Nodes” dummy turned on when growth is above the 75% percentile. Model 1 measures the impact of prior router investment on router vendor choice. Model 2 measures the impact of prior router and switch investments on router vendor choice. All models include establishment controls. Estimates are maximum simulated likelihood with asymptotic standard errors in parentheses. Coefficients on brand dummies are normally distributed and independent across establishments and choices, but constant for establishments over time. Random error terms added in each nest with variances constrained to be identical across nests. Pseudo R-squared is calculated by comparing the likelihood function to one that includes constant terms only. +Indicates significance at 10% level. *Indicates significance at 5% level. **Indicates significance at 1% level.

8