network effects and the adoption of new techno

Strategic Management JournalStrat. Mgmt. J.,19: 1045–1062 (1998)

NETWORK EFFECTS AND THE ADOPTION OF NEWTECHNOLOGY: EVIDENCE FROM THE U.S.TELECOMMUNICATIONS INDUSTRY

SUMIT K. MAJUMDAR1,* AND S. VENKATARAMAN2

1The Management School, Imperial College of Science, Technology and Medicine,London, U.K.2The Darden School, University of Virginia, Charlottesville, Virginia, U.S.A

This paper examines variations in the adoption of new technology by firms operating in anetwork-based industry: telecommunications. These variations are explained as a function ofthree network effects: the first is the conversion effect, driven by operations-related increasingreturns to scale; the second is the consumption effect, driven by demand-side increasing returnsto scale; the third is an imitative effect. We expect the conversion effect to be felt morestrongly during earlier phases of a technology’s evolution, while a strong consumption effectis felt throughout. The imitative effect is also expected to be felt throughout. These hypothesesare examined with respect to electronic switching adoption in the local operating sector of theU.S. telecommunications industry. An analysis of the variations in adoption levels of the 40largest firms over a period lasting from 1973 to 1987 supports our expectations, except forthe imitative effect. 1998 John Wiley & Sons, Ltd.

INTRODUCTION

Many contemporary authors have stressed theimportance of network effects as a determinantof firms’ technology strategy (Antonelli, 1991;David, 1992; Farrell and Saloner, 1986; Katz andShapiro, 1986).1 Understanding how these effectsinfluence technology adoption patterns is crucialfor managers and public policy-makers. Yet, littletheoretical and even less empirical work has beendone at the level of the firm on the issue of how

Key words: network effects; technology adoptionstrategy; telecommunications industry* Correspondence to: Sumit K. Majumdar, The ManagementSchool, Imperial College of Science, Technology and Medi-cine, 53 Princes Gate, Exhibition Road, London SW7 2PG,UK.1The term network is specifically used in this study to referto physical facilities, such as those of telecommunicationssystems, electric utilities, and transportation systems whichare built upon an array of heterogeneous yet interrelatedtechnical components. A key feature of such facilities is inter-connectivity.

CCC 0143–2095/98/111045–18 $17.50 Received 6 October 1995 1998 John Wiley & Sons, Ltd. Final revision received 2 December 1997

the presence of network effects impacts tech-nology choice (Katz and Shapiro, 1994).2

There are three categories of questions in thenetworks literature. First, there are questions withrespect to technology adoption decisions, or whyconsumers purchase a given system. Second, thereare product selection decisions, or what forcesdetermine consumers’ choices among rival incom-

2Empirical studies specifically looking at how network effectsinfluence technology adoption are almost nonexistent. Theonly published empirical studies of how network effects affecttechnology adoption behavior that we are aware of are byGandal (1994) and Saloner and Shepherd (1995). Gandal(1994) examines the computer spreadsheet programs marketand finds that customers are willing to pay a substantialpremium for products that are compatible with the Lotusplatform and for spreadsheets that offer links to external databases, and a smaller premium for spreadsheets which offerlesser network coverage. This is a study in the compatibilitygenre. Saloner and Shepherd (1995) undertake firm-levelanalysis and find that banks with a dense network of branchesmore rapidly adopt automated teller machines. This is afinding commensurate with how consumption effects drivetechnology adoption decisions, a theoretical issue dealt withlater in the paper. It is the only empirical study which hasspecifically looked at how network effects influence firm-leveladoption behavior.

1046 S. K. Majumdar and S. Venkataraman

patible products. Third, there are compatibilitydecisions as to which firms will seek compati-bility and which firms will not (Katz and Shapiro,1994). This study explores the question of whyfirms in a network industry, telecommunications,as purchasers of electronic switching technologyadopt the technology at issue. It falls within thefirst category of research questions in the litera-ture. The study contributes to the literature byproviding theoretical synthesis and evidence withrespect to how network effects impact on firms’technology choices.

The paper unfolds as follows. In the nextsection of the paper we outline conceptual issues,develop the theoretical framework and articulatethe testable hypotheses. The following sectioncontains details of our empirical research; it alsoincludes a description of the context within whichour study is embedded. We present the resultsthat we obtain in the section that follows. Finally,we discuss the implications of the evidence thatwe obtain. We also suggest other further researchissues than may be investigated.

THEORY AND HYPOTHESES

A primary network characteristic is some formof increasing returns (David and Bunn, 1988).Network effects arise when there is inter-dependence between different components ormembers of an economic system (Hirschman,1958; Marshall, 1920; Young, 1928). The natureof inter-dependencies is such that a change inone of the components of the system might causechange in the behavior or performance of othermembers of the system. A fundamental change,such as a change in technology, in the presenceof such interdependencies is attractive to a firmonly if there are significant economic incentivesto the firm adopting the change. The ability toexploit increasing returns is a primary economicincentive for firms adopting new technologiesin network industries (Arthur, 1996). Increasingreturns denote that greater than proportional unitincreases in output are generated for proportionalincreases in the unit of input, reflecting the mar-ginal productivity with which resources aredeployed (Stigler, 1958).

Size or scale effects are the primary driversgenerating these increasing returns. The primaryimpact of size is the realization of technical

1998 John Wiley & Sons, Ltd. Strat. Mgmt J.,19: 1045–1062 (1998)

economies, both from the standpoint of having alarge infrastructure as well as enjoying a largeroutput base to spread costs over. The literaturesuggests that the value to a firm of converting toa new technology is a function of two types ofsize effects (Antonelli, 1991). The first effectarises because of operations-related increasingreturns that firms enjoy in converting from onesystem to another (David, 1985). This is theconversion effect. The second effect arisesbecause of the market-related increasing returnsfirms can enjoy (Rohlfs, 1974; Saloner andShepherd, 1995). This is the consumption effect.It is a firm-level effect which arises where afirm’s customers are interconnected. A third effectarises because of interfirm information flows thatare induced by imitation pressures between firms(Markus, 1992). This arises where firms are inter-connected within an overall industry infrastruc-ture. To the extent that firms face differences inconditions generating these effects, there arelikely to be variations in the adoption levels ofnew technology between firms.

Conversion effect

The conversion effect arises when there areincreasing returns involved in moving from anolder technology to a newer one. In networkindustries where the cost of replacing an installedbase is very high a change in any componentmight require expensive changes throughout thewhole network. In such cases the absolute aswell as the relative size of the physical networkcontrolled by the firm is important. As the sizeof the physical network increases, the marginalcosts of replacing and operating the installed baseof switches gets progressively lower. At smallsizes, the technical and administrative costs ofconverting from an old to a new technology haveto be spread over a smaller installed base. Thismakes the average cost of each component beingreplaced high. Conversely, with a larger networkthe technological and administrative costs of con-version can be spread over a larger installed base.Therefore, marginal costs are reduced (Antonelli,1992; Arthur, 1996).

The size of the physical network is moreimportant in the earlier years than in the lateryears in explaining levels of technology adoption.The unit cost of a new technology is at itshighest in the earlier years. With time technology

Network Effects and New Technology Adoption 1047

manufactuers are able to achieve scale and learn-ing economies. This reduces the unit cost of thenew technology to the adopting firm. In the earlyyears of technology evolution, adopters withlarger physical networks also have the ability towrite off costs quickly should the experience turnsour (Arthur, 1996).

Hypothesis 1: The size of the physical net-work will be positively related to the level ofadoption during the earlier years of the evolu-tion of the new technology.

Consumption effect

This effect exists when there is demand inter-dependence among customers. The welfare ofusers is dependent upon other users in the net-work (Littlechild, 1975; Rohlfs, 1974). This effectis enhanced by the density and composition ofcustomers in the network. The more dense andvaried the network, the greater the value that anindividual customer gains by being on the net-work (Allen, 1988). If there is high networkdensity and variety, a customer’s ability to con-duct more or varied transactions with the set ofnetwork customers is enhanced. The increase innetwork functionality can provide customers withgreater utility.

The density and variety of user population ina network also implies a larger potential market.This makes the provision of new products orservices, which are feasible with the new tech-nology, economical because of increasing returnsto scale. This ability to generate new revenuestreams, in turn, makes the adoption of the newtechnology attractive. The availability of new ser-vices to serve a larger potential market providesincentives for new customers to join the network.For example, Saloner and Shepherd (1995) ana-lyze automated teller machine (ATM) adoptionby banks and find that banks with a greaterbranch density are more rapid adopters. Theyconclude that a consumption effect is in oper-ation, inducing banks to be more rapid adopters,because a customer has greater opportunites touse the ATM when the machines are more wide-spread.

The density and variety of the user populationis expected to be positive and significant at alltimes. Its impact relative to the impact of physicalnetwork size, however, is likely to be greater


during later phases in the diffusion of a newtechnology. This is because we expect the impactof the size of the physical network to decay withtime, while we expect the impact of the densityand variety of the user population to persistover time.

Hypothesis 2: The density and variety of theuser population will be positively related tolevels of new technology adoption at all times.

Hypothesis 2a: The relative impact of physi-cal network size on the levels of technologyadoption will be less than the impact of thedensity and variety of the user population inthe later stages of the evolution of a new tech-nology.

Imitation effect

In the innovation diffusion literature the spreadof a new technology is often equated with animitation process at work (Davies, 1979; Mans-field, 1961). Apart from studies in the literatureon technology adoption and diffusion, otherresearchers carrying out empirical work(Mahajan, Bettis, and Sharma, 1988) have foundfirm-level behavior to be consistent with the imi-tation hypothesis. The imitative effect is notedwhen firms model their behavior after firms per-ceived to be similar (David and Greenstein, 1990;Williamson, 1965). The effect is salient in indus-tries where firms share a common infrastructure,such as railroads or telecommunications. Becauseof interconnectivity between firms and the stan-dardized nature of equipment and proceduresused, numerous channels exist for the quick dis-semination of information of each others’ experi-ences. Thus, there are increasing returns to theinterfirm spread of information (Markus, 1992).

Imitative behavior is assumed to economize onthe costs of decision making in a world of uncer-tainty and ambiguity (DiMaggio and Powell,1983; Jensen, 1982; March and Olsen, 1976).New technologies present managers with uncer-tainty about their viability, making imitativebehavior attractive. When managers face a newtechnology with uncertain trade-offs, imitationpresents a solution with little risk. Porter (1980)argues that under such conditions a firm may bebetter off making a mimetic move, even if themove turns out to be wrong, because if a manager


diverges from the industry trend and is thenproved wrong there are negative consequences.An unwise imitative move, however, is apt tobe overlooked.

Hypothesis 3: The imitative effect will bepositively related to levels of new technologyadoption at all times.

EMPIRICAL ANALYSIS

Context

The research hypotheses are examined in thecontext of the U.S. telecommunications industrywith respect to a new technology introduced inthe 1960s: electronic switching. Electronic switch-ing represented a significant improvement overthe prevailing electromechanical switching tech-nology, and firms in the industry gradually beganto convert in the 1970s.

Switches are the key resources that help toenergize a telephone network and enable cus-tomers to communicate. The extent to whichelectromechanical or electronic technology is usedis critical in making a network modern andefficient (Green, 1992). Specifically, while elec-tronic switches are indeed compatible with theexisting electromechanical technology, there areincentives to convert to the new technologybecause of the possibilities of enhancing operatingefficiencies and customer satisfaction. Estimatesfrom the Bell Telephone and GTE Laboratorieshave suggested a reduction in the annual mainte-nance and operating cost by 6–7 percent of thepurchase cost of a switch (Flamm, 1989).Additionally, the transition from electromechani-cal to electronic switching has led to the creationof an ‘intelligent’ network which has allowednumerous value-added services to be offered tocustomers—for example, pay telephone creditcard calls, 800 calls and other services whichaccess central data bases (Brock, 1994).

The unit of analysis is the local operatingcompany. There are over 1500 local operatingcompanies in the United States. Approximately50 of these companies are large, with annualrevenues of over $100 million as reported forthe year ending 31 December 1987. These 50companies account for almost 99 percent of alllocal operating company telephone revenues inthe United States, which amounted to about $70


billion for 1987. Of these 50 firms, there are 21with annual revenues of more than $1 billionor more.

The firms are all the erstwhile Bell operatingcompanies, independents such as Southern NewEngland Telephone, Cincinnati Bell and Roches-ter Telephone Company and operating companiesbelonging to the GTE, CENTEL, CONTEL, andUnited Telecommunications groups. The finalnumber of companies we use is 40. We couldnot obtain complete data on 10 firms for theyears included in this study. These 10 firms aredropped from the study. Their absence does notbias the results since the 40 firms studied operatealmost 99 percent of the telephone lines in theUnited States. We have a consistent panel of 40firms over all the time periods we analyze. Thesefirms make up almost the whole population oflocal operating companies in the United States.For 1987, the smallest of the 40 firms had rev-enues approximating $100 million. Annual dataon firms are obtained from the Federal Communi-cations Commission, and other data from theannual Statistical Abstract of the United Statespublished by the Bureau of the Census.

Description of years

The basic premise of our study is that differencesin incentives explain variations in adoption levelsbetween firms. We believe that our hypothesesshould be supported in spite of any changes inthe regulatory environment within this industry.To ensure the validity of our hypotheses, wechoose 5 years in the 1970s and the 1980s whenthere were changes in the regulatory environment.Based on past research (Majumdar, 1995) theyears chosen for analysis are 1973, 1978, 1981,1984, and 1987. In choosing these 5 years wealso ensure that adoption levels have made dis-crete but significant jumps, rising from a low of5 percent in 1973 to 56 percent in 1987.

In 1971 and 1972 three procompetition-orientedregulatory decisions were taken. By 1973 thesedecisions would have cumulatively made animpact on the firms within the industry. Thesedecisions were the 1971 ‘Specialized CommonCarriers’ decision which allowed the developmentof separate new channels that were competitivewith the long-distance channels operated by thecompanies, the 1971 ‘Computer Inquiry I’decision which led to the development of value-


added networks, and the 1972 ‘Open Skies’decision which led to the establishment of satellitechannels which were competitive with the exist-ing wire channels.

In 1978 price competition first entered the tele-communications industry. This was as a result ofMicrowave Communications Inc.’s (MCI’s) fightto be allowed to offer switched services, resultingin court reversal of an FCC ruling barring MCIfrom making such offerings. This ruling virtuallyopened up major intercity markets to competition.FCC responded by lowering regulatory barriersto entry. 1981 marks a turning point. In 1981 theFCC issued its order in ‘Computer Inquiry II.’This decision is important because it defined mar-ket structure in terms of services rather thantechnology (Trauth, Trauth, and Huffman, 1983).During that year the Reagan administration wasinstalled. Soon after inauguration, the AntitrustDivision of the Department of Justice resurrecteda dormant antitrust suit against AT&T.

1984 is another key year. The divestiture byAT&T of its 22 operating companies took place.With the reorganization of the industry and theelimination of many regulatory barriers to compe-tition, the necessary conditions for the develop-ment of a competitive environment were created.We use 1987 as the final year for the study,because in the 3 years since the break-up ofAT&T the industry has seen significant develop-ments in long-distance and local competition(Bolter, McConnaughey, and Kelsey, 1990).

Dependent variable

The level of new switching technology adoptedby firms is the dependent variable and is meas-ured by the percentage of electronic to totalswitches, ADOPT, of a firm at each point intime. There are a number of multistate operatingcompanies. The network of such companies liesacross several states, and the adoption variableis at the aggregate network level. State-specificadoption data are not available from the datasources for the period of the study.

An examination of the histogram for ADOPTdoes not reveal non-normality; hence, anuntransformed version of the variable is used.Transforming a normally distributed variableusing natural logs introduces an element of dis-persion into the data which becomes non-normally distributed. Nevertheless, to test for the


robustness of our models to alternate variablespecifications, we also run a model in which thenatural log transformation of ADOPT, LnADOPT, is used as the dependent variable. Theseresults are not reported.

Explanatory variables

Conversion effect

These effects of physical network size are cap-tured in the telecommunications industry contextby the use of two proxy measures. The firstmeasure is the absolute size of the network infra-structure, SIZE. This is computed as the naturallog of total miles of wire possessed by eachcompany. Telephone network size can beexpressed in the form of total miles of wire. Thisis the primary network size dimension. It showshow much coverage each company has attained.An analysis of the relevant statistics as well as thehistogram of the data show that the distribution ofthis variable is skewed. Therefore, a transfor-mation of the variable is necessary and the naturallog of total miles of wire is used to measureSIZE rather than absolute miles of wire. Thistransformation makes the distribution of the vari-able log-normal. The practice of using naturallogs for transforming size variables is consistentwith the literature (Gooding and Wagner, 1985).

A company can increase or decrease its net-work size by adding more or less wire. Whileother forms of transmission technologies, such asmicrowave networks, have become prominent inthe 1980s, these alternatives are used by long-distance carriers and not by local operating com-panies. Local companies predominantly use land-based and not terrrestrial network coverage, giventheir relatively fixed operating territories. Hence,the larger the SIZE measure, the greater the sizeof the network possessed by a firm. In a network,switches provide the engine of communicationsby energizing the connection of different parts ofthe network to each other. This enables voiceand information transfers to take place. Given alarger network, with changes in switching tech-nology taking place increasing returns to scaleare feasible in various conversion tasks such ashousekeeping and systems maintenance whenfirms adopt new technologies.

The second proxy measure to account for con-version effects is a firm’s share of the total


switches in a given local market: SHARE. Thevariable is computed as the percentage of a firm’sinstalled base of switches in its operating arearelative to the total number of all switches, inclus-ive of those of all other operating companiespresent as well in this operating area. The FCCStatistics of Communications Common Carrierscontains data by state as to the total number ofswitches installed in each state. The structure ofthe U.S. local telephone industry is, however,varied. There are a number of telephone com-panies which have only one telephone companymandated to operate in that state. A good exampleis Maryland, where only the Chesapeake andPotomac Telephone Company of Maryland isallowed to operate. There are a number of com-panies, such as the Ohio Bell Telephone Com-pany, which operate in only one state; but in thatstate a number of other operating companies arealso allowed to operate. For example, in OhioGTE is also allowed to operate. For single-statecompanies such as Ohio Bell, their share of thetotal state-level switches is relatively straightfor-ward to calculate.

There are a number of multistate operatingcompanies. For multistate firms we do not havefirm-wise state-level data on the distribution oftheir switches. In a few cases, these operatingcompanies have the only mandate for providinglocal telephone services in all of the states thatthey operate in. For example, New England Tele-phone Company operates in Maine, Massachu-setts, New Hampshire, Rhode Island, and Ver-mont. In these states, New England TelephoneCompany is the only local services supplier. Forfirms such as these, SHARE is measured with-out error.

A number of multistate operating companiesprovide services in states where there are otherproviders of local services. For these firms,SHARE is calculated as the ratio of their totalswitches across states relative to the total of allthe switches in all of the states that they operatein. A potential measurement error that may ariseis that a company may have extremely marginaloperations in one state and yet be listed asoperating in that state. The SHARE calculationis based on a simple averaging process whichdoes not take into account the state-wise distri-bution of switches by company. Thus, SHAREmay be understated for these companies. Thismeasurement problem, however, is not serious


because from the data a number of companiescan be identified as one-state companies, thoughlisted as operating in two states. New York Tele-phone Company is a good example. For thesefirms, the primary state of operation is taken forcalculating the denominator of the SHARE vari-able.

Since operating territories are demarcated fortelephone companies, the greater the relativeinstalled base of switches, the greater is the rela-tive size of the physical network controlled.While each local operating company is a localmonopoly for its demarcated franchise area,SHARE measures the market power of each localoperating company within the overall regionalterritory. The use of such a measure to controlfor market power effects is in consonance withextant literature (Gabel, 1994; Shepherd, 1983).

The greater the relative size, the higher is theincentive to exploit conversion effects becausethere is a relatively larger number of locked-incustomers who provide the means to write-offconversion costs. Nevertheless, in the literaturemarket power beyond a point has a negativeinfluence on behavior because inertia sets in(Leibenstein, 1976; Scherer and Ross, 1990).Hence, the relationship between market powerand adoption behavior need not be linear. Tocontrol for probable nonlinearities, a squared termfor SHARE, SHARE2, is also introduced.

Introducing the squared term for a variable,along with that variable itself, introduces a poten-tial multicollinearity problem. This problem issolved by normalizing SHARE in its mean devi-ation form and then squaring the resulting term.The resulting collinearity between SHARE andSHARE2 is thereby reduced. The correlationbetween the original variable and its mean devi-ation variant is 1. The regression estimates forthe squared term, when either the original variableor its mean deviation variant is squared and usedas regressors, are also identical. What the meandeviation transformation of a variable and itssubsequent squaring does is to introduce someorthogonality between a variable and its squaredterm. This reduces multicollinearity.

Consumption effect

Two proxies are used to capture the density andvariety of the user network in the telecommuni-cations industry. The issue of density is a function


of the urban characteristics facing each firm. Cus-tomers in large metropolitan areas are heavierusers of communications services (Langdale,1982; Meyeret al., 1980). Per capita telephoneexpenditures are greater in metropolitan areasgiven that a greater number of interconnectionsare likely to be available; the studies provideevidence to this effect, and they also show thatpatterns of communication usage exhibit thegreatest density in urban areas. Again, the exist-ence of such traffic patterns and density effectspoints to the availability of lucrative markets forthe introduction of new services (Langdale, 1982).

To capture the importance of density we calcu-late a proxy measure, termed METRO, as follows.For each state in the United States we calculatethe percentage of its urban population to thetotal state population. Data from theStatisticalAbstract of the United Statespublished by theBureau of the Census (Annual) are used. Wearrive at an index for each state. Some telephonecompanies have operations in one state, whileothers have multistate operations. Thereafter, foreach firm we compute a simple average of stateindices for all the states that it operates in. Thisindex denotes the configuration of the urbanspaceeconomy for each firm.

The Statistical Abstract contains details of theurban–rural population mix in each state. Forsingle-state firms or multi-state firms operating instates which do not have other local operatingcompanies mandated to provide telephone ser-vices, there are no measurement errors with theMETRO variable because specific state data areused. But with other multi-state companies ameasurement problem may arise because thesecompanies can operate in parts of states whichare mostly rural or mostly urban. Such data areavailable in private company-level maps; thesemaps show in which part of the state each com-pany operates in, but we do not have access tothese maps. County-specific data can be obtainedand matched to the maps to gauge the true urban-rural mix. This we cannot do. Therefore, thepossibility exists of over- or understating theurbanization proportion for some of the multi-state companies.

A measure, BUSINESS, calculated as the per-centage of business lines to the total lines oper-ated by each firm, is used to proxy for customervariety. BUSINESS explicitly measures the busi-ness–residential customer mix of each firm. It is


one possible proxy measure capturing variety,though in the local telephone industry context themost relevant. Studies have stressed the impor-tance of business customers in generatingrevenues for telephone companies (Meyeret al.,1980; Wenders, 1988). The studies highlight theimportance of business customers. They also sug-gest that if a telephone company has a greaterconcentration of these business–customer lines inits network it will also have a higher possibilityof generating revenues from the provision ofvalue-added services, which have greater profitpotential since the prices of these services areunregulated.

Imitation effect

The variable IMITATION is constructed as fol-lows: for each firm a ratio is constructed with theproportion of the electronic switches possessed byits neighboring firm(s) as the numerator, and theaverage proportion of electronic switches in thesector as a whole as the denominator. For single-state firms the comparator neighboring firm(s)may belong to the same territory, if multiplefirms are allowed to participate in that territory.In that case the largest comparator firm’s ratio isused. If no other firms are allowed to participatein that state, the firm participating in the nextterritory is used as the comparator if only onefirm is allowed to participate in the next territory.If multiple firms are allowed to participate in thenext territory, then the largest firm participatingin that territory is used as the comparator firm.Because we do not have explicit state-by-stateoperating data for each company, aggregate rev-enue is used as a measure of size.

The procedure described above is followed forcompanies with multiple operations in each state.In states with one other operator it is that largestneighbor. In states with multiple operators it isthe largest neighbor. If the imitation hypothesisis valid, then the greater the ratio is, a firm inquestion will have a relatively greater change inits installed base of switches. The measure used ismore precise than measures used in past literaturewhich attempted to capture imitation effects byusing the total number of past adoptions of tech-nology as a proxy (Levin, Levin, and Meisel,1987).

Again, there are potential measurement errorsin computing this variable. For single-state


operating companies a bias may arise in imputinga firm’s choice of neighbor and territory. Wehave assumed that the Chesapeake and PotomacTelephone Company of Maryland considers theChesapeake and Potomac Telephone Company,which operates in Washington, DC alone, as itsneighbor and given geographical contiguityWashington, DC as the neighboring territory. TheChesapeake and Potomac Telephone Company ofMaryland may well consider the Chesapeake andPotomac Telephone Company of Virginia as itsneighboring comparator form.

For states with multicompany operations com-panies it is reasonable to assume that, givendemographic considerations, firms consideranother firm in that state as the comparator.Again, we have assumed that a firm like GTENorth would look to what Northwest Bell doesto base its own actions on, since both firmsoperate in more or less the same states. GTENorth may well look to what its other siblings,such as GTE Southwest, which is also a multi-state operating company, do to base its actionson. Therefore, the construction of this variableis subject to researchers’ biases because of theassumptions made. These biases can lead to errorsin how the IMITATION variable is measured andwhat it is actually proxying for.

Controls

Variables have to be introduced to control forother factors that influence technology adoption.Their choice is consistent with theory and indus-try considerations.

Toll market effects

An industry-related control variable for the years1984 and 1987 is also introduced into the analy-sis. Since the 1984 divestiture, operating com-panies have had to allow equal access to long-distance operators, who can then deliver long-distance messages to local-company customers.In many states, the local company has state-wide long-distance monopoly rights, but in someimportant states the long-distance market wasmade fully competitive. In these states with com-petitive toll markets the incumbent local operatingcompany is the primary in-state long-distancesupplier. Other long-distance carriers can, how-


ever, enter the sector if they wish to and industrywriters have suggested that entry threat by long-distance operators into local companies’ long-distance operations areas induces new technologyadoption (Wenders, 1988).

While network effects generate the key incen-tives for adoption of new technology, we controlfor the effect of possible competitive pressuresfrom potential market entry. The measure is com-puted as the percentage of a local company’slong-distance toll revenues, TOLL, that are gener-ated in markets where entry is allowed by regula-tory authorities to other long-distance carriers.This measure is relevant, as mentioned, only from1984 onwards, and is used for analysis of the1984 and 1987 cross-sections.

Growth effects

The effect of increasing demand in growth mar-kets is an important inducement towards newtechnology investment. This line of reasoninghas found empirical support (Schmookler, 1966;Thirtle and Ruttan, 1987). Where firms are facedwith profitable opportunities in a growing market,incentives to deploy new technology are high inspite of what may be other simultaneous determi-nants of deployment. Past growth in calling vol-ume is used to proxy for future demand. There-fore, a variable, GROWTH, is introduced as acontrol. The variable is constructed as follows:for each state the percentage growth in total callsis computed between 2 years, say 1984 and 1987;thereafter, for each firm an average percentagegrowth rate of total calls is computed as theaverage of the percentage growth rates for all thestates it operates in.

The state-level data on total calls are obtainedfrom the FCCStatistics of Communications Com-mon Carriers. The greater the past growth ofbusiness volume, as measured by the number ofcalls in its operating area, the greater the induce-ments of investing in new technology. Since thefirst year for analysis in the study is 1973, thevariable for 1973 is constructed using the growthin calls that has taken place between 1971 and1973. Again, for some multi-state companiesthere is the possibility of measurement errorbecause these companies may be operating inparts of states where growth in calling volumehas not taken place. Our data sources are notrefined enough to tease out such intrastate details.


Performance and size interaction effects

An alternative factor that can impact variationsin adoption levels among firms is the level offinancial resources available to firms. Larger firmshave a bigger pool of resources (Nutter, 1956),such as greater capital, to experiment with. Ifsuch experiments are successful, firms canenhance their power over their environment(Pfeffer and Salancik, 1978). If adoption is suc-cessful, there are also larger absolute gains tobe made by the larger firm (Galbraith, 1952;Mohr, 1969).

To test for a performance and size interactioneffect which picks up firms’ resource richness, avariable EFFICIENCY is created. To do so, theSIZE variable, as previously defined, is interactedwith a performance measure. The performancemeasure is created as follows: for each firm ofthe periods, a measure of their relative ability togenerate venues with their given base of assetsand resources is computed using a performancemeasurement technique called data envelopmentanalysis (DEA).

The performance statistic obtained is a measureof relative efficiency between firms. Banker,Charnes, and Cooper (1984) (BCC), Charnesetal. (1985) (CCGSS) and Charnes, Cooper, andRhodes (1978) (CCR) develop an efficiency mea-sure where the ratio of the weighted outputs toweighted inputs of each observation in a data setis maximized. For each observation a measure ofhow efficient it is in converting a set of multipleinputs jointly and simultaneously into a set ofmultiple outputs is calculated. The best firmsscore 1 on a scale of 0 to 1. For the inefficientfirms the differences between their scores and 1gives an idea of the performance improvementpossible. DEA optimizes for each observation, inplace of the aggregation of data and the singleoptimization performed in statistical regressions.The DEA algorithms take each observation’s idio-syncrasies into account in the computation ofefficiency, unlike in regression-based techniqueswhere efficiency parameters are calculated basedon averaging process (Seiford and Thrall, 1990).Within the DEA frameworks firms can conserveinputs; then, the algorithm evaluates minimal useof inputs, with outputs generated kept constant;or, given a finite stock of inputs available, firmscan maximize outputs. Each algorithm capturesdifferent aspects of firms’ behavior.


For the 40 companies evaluated, the BCC out-put-maximizing algorithm is used to calculateperformance parameters for each observation.Three outputs—(1) local revenues, (2) toll rev-enues, and (3) access and miscellaneousrevenues—and three inputs—(1) number ofswitches, (2) number of lines, and (3) number ofemployees—are used in the computations. Giveninputs, the algorithm evaluates the relative capa-bilities of firms in generating the three types ofoutputs. The use of these inputs and outputsvariables is consistent with extant literature(Banker, Chang, and Majumdar, 1993; Green,1992; Majumdar, 1995; Skoog, 1980). The datafor carrying out the computations are all obtainedfrom the FCCStatistics of Communications Com-mon Carriers for the various years. See Seiford(1996) for details and expositions of DEA.

Estimation

To explain variations in levels of new technologyadoption, we estimate a series of additiveregression models, given that each type of net-work effect is independent of the other. We usea comparative approach, whereby results for fivecross-sections are evaluated. The approach is use-ful, because while diffusion rates are increasingover time the impact of each of the independentvariables may be changing. While we analyzevariations in adoption levels at points in time,intrafirm diffusion is also captured since overtime the level of electronic switching within eachfirm is rising. We use five distinct years between1973 and 1987 as the periods to study. Therationale for the choice of years has already beendescribed. These years are chosen because theyare years when milestone events occurred in thehistory of the industry.

The firm-level variable of interest is the per-centage of electronic switches to total switches.It is a continuous variable which helps highlightthe quality of the installed base of a carrier.Thereafter, to explain the variations in this vari-able, independent variables for each of these firmsfor the five time periods are obtained and cross-sectionally matched. We estimate regressions foreach time period. Thereby, we are able to ascer-tain changes in the explanatory power of differentnetwork effects and independent variables overtime.

We initially estimate OLS models with the


Table 1.Panel (A): Descriptive statistics and correlations for 1973

Variable Mean S.D. 1 2 3 4 5 6 7 8 9

1. ADOPT 5.21 6.02 1.002. SIZE 9.22 1.31 0.29 1.003. SHARE 0.00 37.28 0.39 0.54 1.004. SHARE2 1355.30 771.05 −0.21 −0.38 −0.31 1.005. METRO 73.38 17.18 0.25 −0.00 0.02 0.28 1.006. BUSINESS 26.99 7.39 0.28 −0.15 0.07 0.27 0.32 1.007. IMITATION 1.36 1.34 0.20 0.04 0.24 0.11 0.29 0.01 1.008. GROWTH 15.06 15.49 −0.18 −0.23 0.11 0.07 0.03 0.07−0.27 1.009. EFFICIENCY 5.12 1.34 0.02 0.41 0.19 0.09 0.38 0.40−0.12 −0.01 1.00

Panel (B): Description statistics and correlations for 1978

Variable Mean S.D. 1 2 3 4 5 6 7 8 9

1. ADOPT 17.86 13.89 1.002. SIZE 9.49 1.29 0.50 1.003. SHARE 0.00 37.50 0.61 0.53 1.004. SHARE2 1371.22 806.97 −0.40 −0.46 −0.38 1.005. METRO 71.79 16.62 0.19 −0.06 −0.00 0.30 1.006. BUSINESS 26.03 5.26 0.26 −0.18 0.18 0.18 0.34 1.007. IMITATION 1.25 0.22 0.22 0.01 0.13 0.12 0.22−0.04 1.008. GROWTH 10.87 4.14 −0.02 0.08 0.13 0.06 0.14−0.06 −0.15 1.009. EFFICIENCY 7.24 1.56 0.45 0.85 0.47−0.31 0.24 0.01 −0.10 0.19 1.00

Panel (C): Description statistics and correlations for 1981

Variable Mean S.D. 1 2 3 4 5 6 7 8 9

1. ADOPT 32.35 18.51 1.002. SIZE 9.65 1.28 0.51 1.003. SHARE 0.00 38.02 0.66 0.54 1.004. SHARE2 1409.41 850.43 −0.49 −0.48 −0.52 1.005. METRO 71.79 16.62 0.15 −0.07 −0.01 0.26 1.006. BUSINESS 25.94 5.17 0.29 −0.16 0.28 0.04 0.26 1.007. IMITATION 1.14 0.53 0.15 0.00 0.05 0.06 0.18−0.02 1.008. GROWTH 12.20 4.06 −0.11 0.18 0.13 0.10 0.10−0.08 −0.18 1.009. EFFICIENCY 8.86 1.87 0.43 0.91 0.44−0.39 0.05 −0.18 −0.10 0.23 1.00

given data. In cross-sectional research such asours, however, heteroscedasticity may be a prob-lem. In the presence of heteroscedasticity theestimates may be inefficient, and we test for thepresence of heteroscedasticity in the data for eachof the years using the standard Glejser (1969)test. If the test reveals heteroscedasticity for anyof the observations we reestimate the OLS modelsfor year 1973 using a heteroscedasticity-consistent


covariance matrix (White, 1980) and these het-eroscedasticity-consistent results are reported.

RESULTS

Correlations

Tables 1 and 2 present the means, standard devi-ations, and correlations for the variables. The


Table 2.Panel (A): Descriptive statistics and correlations for 1984

Variable Mean S.D. 1 2 3 4 5 6 7 8 9 10

1. ADOPT 41.71 19.83 1.002. SIZE 9.72 1.27 0.26 1.003. SHARE 0.00 34.79 0.58 0.47 1.004. SHARE2 1180.31 737.83 −0.13 −0.48 −0.11 1.005. METRO 73.09 17.18 0.18−0.03 0.05 0.16 1.006. BUSINESS 25.07 7.39 0.65−0.23 0.51 −0.01 0.32 1.007. IMITATION 1.04 0.42 −0.06 0.17 0.02 0.00 0.18−0.20 1.008. TOLL 43.25 44.19 −0.34 0.06 −0.28 −0.12 0.08 −0.21 −0.11 1.009. GROWTH 9.45 5.02 0.22−0.08 0.15 0.19 0.36 0.27 0.20−0.25 1.00

10. EFFICIENCY 8.76 1.51 0.06 0.77 0.25−0.14 −0.02 −0.02 −0.05 0.25 −0.29 1.00

Panel (B): Description statistics and correlations for 1987

Variable Mean S.D. 1 2 3 4 5 6 7 8 9 10

1. ADOPT 56.16 24.63 1.002. SIZE 9.79 1.27 0.12 1.003. SHARE 0.00 32.66 0.26 0.40 1.004. SHARE2 1040.24 860.99 0.14−0.33 0.25 1.005. METRO 73.59 16.72 0.24−0.02 0.02 0.23 1.006. BUSINESS 26.79 8.22 0.48 0.23 0.53 0.15 0.32 1.007. IMITATION 1.05 0.47 0.36 0.06 0.28 0.21 0.15 0.35 1.008. TOLL 43.92 44.19 −0.07 0.07 −0.30 −0.20 0.05 −0.24 −0.02 1.009. GROWTH 17.83 0.47 0.13 0.17 0.22 0.22 0.28 0.05−0.17 −0.06 1.00

10. EFFICIENCY 8.99 1.62 0.09 0.87 0.39−0.23 −0.02 0.31 −0.14 0.10 0.22 1.00

tables show that the average adoption level,ADOPT, increased from a low of 5.45 percentin 1973 to a high of 56.16 percent in 1987.The corresponding standard deviations are 6.10percent and 24.63 percent. The data distributionsshow a substantial growth in the adoption levelover time. Differences also exist among firms inthe adoption levels during each of these years.Although the diffusion of electronic switches inthe industry has increased over time, there arewide variations in adoption levels at differentpoints in time. This is brought out when thestandard deviations in the dependent variable overtime are examined.

Interquartile deviation data are not reported inthe tables because of space constraints, but are:1973, 8.41 percent; 1978, 24.87 percent; 1981,33.46 percent; 1984, 26.21 percent; and 1987,38.47 percent. These data highlight the extent ofinterfirm variations in the diffusion of electronicswitching. In all years, the interquartile deviation


is greater than the standard deviation. The meanvalues and the variation in the predictor variablesare stable over time. These patterns in the meansand variation are significant. As a result, inter-pretation of the changes in the power of eachvariable over time to explain differences in adop-tion levels is unlikely to be confounded by ran-dom changes in the values of the predictor vari-ables.

Tables 1 and 2 also present the correlationsamong the variables for each of the five timeperiods under investigation. Consistent withexpectations, the conversion effect, measured bySIZE and SHARE, is correlated with adoptionlevels in the direction predicted for the earlieryears relative to later years. The transformationof SHARE and SHARE2 reduces the multi-collinearity between the two variables. The corre-lation without the transformation would have beenaround 0.95, if not more; the negative sign forSHARE2 indicates the existence of an inverted


U-shaped relationship. The consumption effect isrelated in a more complex manner with adoptionlevels. The correlation for METRO first increasesbut then declines. BUSINESS is less correlatedwith adoption levels in 1973, but the correlationcoefficient rises by 1984. Similar patterns areobserved for IMITATION, the variable whichcaptures the imitative effect.

Of the control variables, the variable TOLLintroduced for 1984 and 1987 shows a negativebut sharply declining correlation with ADOPTover time. GROWTH shows a negative corre-lation for the years 1973 to 1981, which turnspositive for 1984 and 1987. The correlation ofEFFICIENCY is positively correlated withADOPT throughout the period, but materially soonly for 1981 and 1984. It is, however, highlycorrelated with SIZE from 1978 onwards through1987. Because of the resulting multicollinearityproblems SIZE and EFFICIENCY cannot be si-multaneously used as explanatory variables inregression models.

The data indicate that larger firms are becomingmore efficient over time in the U.S. telecommuni-cations industry. If larger firms were to be lessefficient than smaller firms in generating outputwith their stock of resources then the resultingcorrelations between SIZE and EFFICIENCYwould be very much smaller. The fact they arenot indicates thatx-inefficiencies, often charac-terizing large firms’ performance (Leibenstein,1976), are disappearing. This is a preliminaryindication of changes in other aspects of behaviorand performance of U.S. telecommunicationsfirms over time which require further detailed in-depth analysis and reasearch.

Regression results

Table 3 presents the OLS estimation results ofthe regression analyses for the five periods 1973to 1987. The dependent variable used is ADOPT.Control variables are included. In Table 3 twocolumns of results, (A) and (B), are reported foreach year. The first column, (A), includes theSIZE variable; the second column, (B), includesthe results with the EFFICIENCY variable, butSIZE is excluded. We do this because SIZE andEFFICIENCY are very highly correlated. Amodel is also estimated where the dependentvariable is ln ADOPT. Both model estimates aresimilar. Only the results with ADOPT are


reported. Additionally, only the data for 1973 areheteroscedastic based on the Glejser (1969) testfor heteroscedasticity. Reestimation of the 1973data yields more efficient estimates correctedfor heteroscedasticity.3

Hypothesis 1 states that a positive and signifi-cant relationship exists between the physical net-work size effect and new technology adoption.We find that there is a positive and significantrelationship between absolute size and relativesize of the physical network, with new technologyadoption taking place in the earlier years of adop-tion. The absolute size of the network variable(SIZE) is significant for the years 1978 and 1981,as shown by column (A) results. The effect ofthe relative size variable (SHARE) persists into1984, as shown by columns (A) and (B). Neithervariable is, however, significant in 1987. Hence,the physical network size effect remains strongin earlier years but wears off with time, with noappreciable effects remaining by 1987.

Hypothesis 2 states that the density and varietyof the user population will be positive and sig-nificant throughout the adoption period. The twovariables capturing the consumption effect areeach significant at different times during theadoption period. We use the regression results ofcolumn (A) for drawing conclusions. We do thisbecause the correlation between SIZE andMETRO, and between SIZE and BUSINESS, areless than the correlation between EFFICIENCYand METRO, and between EFFICIENCY andBUSINESS. Columns (A) and (B), however,show broadly similar results.

The results offer support for our hypothesis.Of the two variables used to proxy consumptioneffects, at least one of them, namely customervariety (BUSINESS), is positive and significantthroughout the period studied. The other variable,namely urban density (METRO), has a strongerimpact in the earlier years, 1973, 1978 and 1981,but its impact is no longer significant in 1984and 1987. When both variables are reviewed

3Some of our explanatory variables can have measurementerrors. In the presence of errors, OLS estimates are potentiallybiased, inconsistent, and inefficient. To correct for these,several authors (Gujarati, 1986; Kennedy, 1985; Maddala,1977) suggest the use of weighted regressions. We also usethe least absolute value estimation approach in estimating ourmodels, which is a weighted regression approach (Maddala,1977). The LAV estimates are, in fact, stronger than the OLSresults and provide material support for our hypotheses.


Table 3. Model results: Dependent variable ADOPT

Variable 1973a 1973 1978 1978 1981 1981 1984 1984 1987 1987(A) (B) (A) (B) (A) (B) (A) (B) (A) (B)

Constant −4.572 0.529 −36.372 −14.291 −35.875 −17.614 20.293 20.401 1.890 17.910(0.51) (0.11) (1.71) (0.98) (1.23) (0.79) (0.72) (0.89) (0.04) (0.56)

Physicalnetwork sizeSIZE 0.183 3.002** 3.503* −0.581 0.232

(0.211) (1.85) (1.60) (0.23) (0.06)SHARE 0.047 0.066*** 0.103** 0.122** 0.151** 0.167** 0.154* 0.153**−0.035 −0.006

(1.22) (2.46) (1.77) (2.07) (1.86) (2.10) (1.66) (1.74) (0.22) (0.04)SHARE2 −0.001* −0.002* −0.005* −0.005** −0.006**−0.006***−0.005 −0.005 0.000 −0.000

(1.44) (1.43) (1.97) (2.16) (1.86) (1.97) (1.20) (1.26) (0.08) (0.17)

User densityand varietyMETRO 0.074** 0.121** 0.135 0.100 0.169 0.140 0.004 0.013 0.041 0.029

(1.66) (2.41) (1.20) (0.79) (1.21) (0.96) (0.02) (0.07) (0.15) (0.11)BUSINESS 0.222 0.287* 0.702** 0.596* 0.791* 0.774* 1.097***1.072*** 1.239** 1.298**

(0.98) (1.50) (1.91) (1.62) (1.63) (1.57) (2.80) (2.75) (2.03) (2.12)

ImitationIMITATION 0.142 −0.427 3.435* 3.87* 5.150 6.050* 6.644 6.228 12.132* 12.459*

(0.24) (0.75) (1.52) (1.62) (1.23) (1.41) (0.98) (0.90) (1.38) (1.42)

ControlsTOLL −0.094* −0.967* 0.025 0.025

(1.52) (1.61) (0.27) (0.27)GROWTH −0.078* −0.105** 0.011 −0.033 0.374 0.381 0.187 0.182 0.083 0.101

(1.63) (2.99) (0.03) (0.08) (0.66) (0.66) (0.34) (0.33) (0.72) (0.87)EFFICIENCY −1.475** 1.710 1.970* −0.589 −1.54

(1.74) (1.25) (1.36) (0.31) (0.55)

Adj. R2 0.200 0.281 0.478 0.447 0.496 0.486 0.450 0.451 0.111 0.119

F 2.38 3.18 6.05 5.50 6.49 6.26 4.99 5.00 1.61 1.66

*** p , 0.01; **p , 0.05; *p , 0.10aThe results for 1973 are robust to corrections for heteroscedasticity.

together, the consumption effect that these vari-ables proxy for is significant and positivethroughout the period studied, although the effectof one of the variables wears off with time.

In Hypothesis 2a we state that physical networksize will matter less in the later stages of atechnology’s evolution than user density and var-iety. We evaluate this statement by reviewing thepartial coefficient of determinant (R2) values forthe variables capturing physical network size, anduser density and variety for each year. The partialR2 values denote the amount of theR2 that wouldbe reduced if the variables were to be eliminatedfrom the regression and are derived from theOLS estimates given in Table 3. PartialR2 values


are aggregated for the variables capturing physicalnetwork size and user density and variety. Theproportion of the total explainable variance thatis actually explained by these separate effects ineach of the time periods is computed. In calculat-ing the proportion for the physical network sizeeffect, the partialR2 values for only SHAREare used, since SHARE2 is introduced into theregression as a test for functional form. Theseproportions are reported in Table 4.

Table 4 shows that while during 1973 physicalnetwork size effect accounts for 6 percent of thevariance explained and user density and varietyaccounts for 10 percent, this pattern is reversedin 1978 when the physical network size effect


Table 4. Impact of different effects over time

EFFECT Proportion of Proportion of Proportion of Proportion of Proportion ofTotal Variance Total Variance Total Variance Total Variance Total Variance

1973 1978 1981 1984 1987

CONVERSION 0.062 0.185 0.172 0.083 0.002CONSUMPTION 0.131 0.145 0.121 0.202 0.120IMITATION 0.001 0.068 0.045 0.030 0.058

accounts for 19 percent of the variance explainedas compared to 15 percent accounted for by theuser density and variety effect. The patterns for1978 are repeated in 1981 when the user densityand variety effect explains a lesser proportion ofthe variance at 12 percent than the physical net-work size effect which explains 18 percent. After1981 the pattern reverses, with user density andvariety being more important explanatory vari-ables, and differences between the two effectsthen increase progressively with each year. By1987 most of the variance is explained by theuser density and variety effect. The results areconsistent with our expectations where duringlater years user density and variety account for alarger proportion of the variance relative to physi-cal network size. The imitative effect, IMI-TATION, is positive and significant, albeit at the10 percent level, only in 1978 and 1987. Theseresults provide partial support for Hypothesis 3.There are possible measurement errors in theIMITATION variable and these errors may be,in part, responsible for these findings.

DISCUSSION AND IMPLICATIONS

Context-related issues

The results broadly support our expectations. Ouranalyses, however, reveal several interesting pat-terns which are consistent with the forces thatare important in network industries and meritdiscussion. First, operations-related increasingreturns to scale provide strong incentives to con-vert to a new technology from an older one.These effects are pronounced in the earlier stagesof the diffusion of the technology, and wearoff towards later stages. Differences in physicalnetwork size-related incentives facing firms help


explain differences in technology choices whenthe attributes of the technology are still notclearly known. Under such conditions, firms withlarger physical networks have the economicincentives and the capacity to bear the risks ofconverting to a new technology. Hence, in earliertime periods, firms with larger networks are morelikely than firms with smaller networks to beadopters. As technological capabilities get to beknown, all firms convert to the new technology.Thus, differences in physical network size areless important in inducing differences in adoptionlevels as the technology matures.

A related issue is whether larger firms havemore cash to invest in new technologies. There-fore, the impact of size should be separated fromresource richness. Unlike SIZE, which has a sig-nificant and positive relationship with ADOPTfor the years 1978 and 1981, EFFICIENCY issignificant and positive only for 1981. Admit-tedly, the greater availability of financialresources among larger firms, rather than physicalnetwork size, can account for the fact that SIZEpositively impacts adoption; however, when SIZEand a measure of performance is interacted, theresults for SIZE and the size–performance inter-action term, EFFICIENCY, are similar in signand significance only for the year 1981.

Signals that markets were to be opened beganto emerge very strongly in the year 1981 withthe installation of the procompetition Reaganadministration and a new set of Department ofJustice officials who revived an antitrust suitagainst AT&T. Given this exogenous event, it islikely that during this year the firms exploitedtheir increasingly superior performance in gener-ating resources to make investments in the newtechnology so as to have the capabilities to oper-ate in the changing environment.


The above findings imply that though the largerfirms do also have superior resource-generationcapabilities, their ability to use such resources forinvestment purposes is not what is necessarilydriving technology adoption. On one hand, thenegative results found for 1984 and 1987 implythat relatively superior performance may act toretard investments, and large firms may be takingthe situation for granted. Also, in the postdivesti-ture era after 1984, the consumption effect doesstill retain its primacy in explaining variations intechnology adoption, after controlling for size aswell as performance effects.

Second, although market-related increasingreturns to scale are significant in explaining thedifferences in adoption levels, the effect of urbandensity, METRO, is more important than theeffect of customer variety, BUSINESS, in anearlier year while the opposite is true in lateryears. One reason why this is so is that densitymay imply congestion. With network infrastruc-ture quality improving as a result of new tech-nology diffusion, congestion effects lessenbecause network capacity expands while trans-mission quality improves. While network densityimplies that revenue-generating services can bebunched together so that firms may gain market-ing scale economies, with improving networkfunctionality the provision of services can beundertaken on a distributed basis. This is a con-temporary trend whose impact is likely to befurther noticed as firms switch from copper wiretechnology to fiber-optics and satellite technologyfor transmission links, reducing the impact ofcongestion as a significant factor to be consideredwhile designing a telecommunications network(Green, 1992).

The customer variety variable, BUSINESS, ispositive and significant in earlier as well as lateryears but particularly strong in the later years. Apossible reason why this is so is that it takestime before the full implications of the new tech-nology are appreciated, and value-added productsare developed using the new technology as aplatform. Since business customers are critical ingenerating revenues for telephone companies,their roles become salient only when new andvalue-added products become feasible using thenew technology. Due to the lag between thearrival of a new technology and the developmentof new and value-added products using the tech-nology, the impact that business customers may


have in influencing new technology adoption maybe delayed.

The IMITATION variable is subjected toresearcher bias because we have imputed thatfirms behave by taking the actions of other speci-fic firms into account. The basis of this imputationare assumptions we make as to which thosespecific firms are. These assumptions can be atodds with reality. Therefore, the imitation resultshave to be treated with caution. Nevertheless, wefind that IMITATION is positive and significantfor 3 of the 5 years: 1978, 1981 (Model B), and1987. While firms do imitate each other, withinthe industry the firms that we study may actuallybe imitating firms other than the neighboringfirms that we treat as compatriot firms. Finer-grained interview and survey-based analyses,which obviate many of the measurement prob-lems, can help in reaching detailed conclusionson the imitation hypothesis in the future.

Theoretical issues and future research

Our study provides insights into the propensityfor technology adoption in the presence of net-work effects. Dominant explanations for newtechnology adoption are the factor-saving hypoth-esis or the market growth hypothesis (Thirtle andRuttan, 1987). To the extent a new technologyallows the firm to substitute a more efficientfactor for a less efficient one, there are incentivesfor firms to adopt the new technology. FollowingHicks (1932) and Samuelson (1965), this cost-saving bias with respect to a new technology hasbeen emphasized by authors interested in thesupply side of new technology adoption. Simi-larly, others (Schmookler, 1966) have emphasizedthe role of demand-induced incentives as spurringnew technology adoption. A growing marketspurs the search for more effective new technol-ogies, thus inducing firms to search for and adoptnew technologies in order to take advantage ofdemand growth. These approaches overlook dif-ferences which exist between firms in the extentof new technology adoption (Ginsberg and Ven-katraman, 1992). Further, they emphasize one setof explanations and ignore the other in empiri-cally analyzing behavior. Our study emphasizesfirm-level differences and examines how theseaccount for variations in levels of adoption of anew technology.

A feature of this study is a comparative nature.


Technology adoption studies mostly use a cross-sectional approach. But, the relative influence ofdifferent pressures on technology adoption are notstable over time. Instead they vary with changingenvironmental conditions and the competitive ter-rain (Tolbert and Zucker, 1983). In the contextof an evolving technology an assumption of stablerelationships between adoption levels and induce-ments can be unrealistic, since knowledge abouta technology and the sophistication of the tech-nology itself changes over time. Therefore, therelative pressures exerted by each independentvariable can wax and wane. Our findings showthat inducements generated by conversion or con-sumption effects have time trends. Specifically,in the context of new technology adoption in anetwork environment, the conversion effect ismore prominent early on, while the consumptioneffect is prominent throughout.

The positive imitative effect at the end of theadoption period raises a strategic issue for firmsin network industries. By virtue of being membersof a network industry, firms are connected to eachother to form a common industry infrastructure.Therefore, individual firms can face competitivethreats from firms operating in distant parts ofthe network infrastructure or from firms closestto themselves in the network. This issue becomesgermane for the contemporary telecommuni-cations industry context. The opening up of thelocal services market to other players heightensthe possibilities of intense competition in severallocal markets. A question is: which firms arelikely to be the key new competitors in specificlocal markets? Are they the neighboring localmarket players, or are they local exchange com-panies from other distant parts of the telecom-munications network? Given that physical infra-structure is a key element of local telephoneservices, it is likely that neighboring localexchange companies may start entering eachothers’ territories. To the extent that firms closestin the network space adopt a new strategy ortechnology neighboring firms can feel imitativepressures to adopt a similar strategy or tech-nology. Thereby, they can acquire the abilities toretaliate against competitive threats if necessary.This imitative behavior can be modeled as a two-stage procedure since the adoption choice of afirm and its neighbor(s) can arise from commonenvironment-related causes.

This reasoning is consistent with the notion of


spatial competion (Hotelling, 1929), which isvery useful in understanding and analyzing con-temporary competitive dynamics in a networkindustry context. The notion is defined as addressmodels of competition in the contemporary indus-trial organization literature (Eaton and Lipsey,1989). The principal argument underlying thesemodels of competition is that firms do not com-pete across all product spaces, but in narrowlydefined and well-differentiated regions. Thisdimension of localized competitive behaviorwithin a network industry calls for theoreticaland empirical analysis by strategy researchers.The telecommunications industry provides onecontext for such research. The computer hardwareand software industries provide other contexts.

While firm-level factors are recognized asimportant in explaining technology adoption pat-terns, evidence is needed on the role that insti-tutional influences play. A key influence in thetelecommunications industry is the regulatoryregime. In the United States, after 1987 severalstate regulatory bodies as well as the FCC havemoved from a rate-of-return-based system toincentive regulation based on price-caps. There isa natural experiment in progress with changestaking place in regulatory regimens in differentstates at different points in time. A change toincentive regulation enhances an efficiency orien-tation within firms because profits are influencedby the cost savings attained since prices arecapped. Hence, a change to incentive regulationcan influence new technology investments whichenhance the efficiency of firms. Therefore, follow-up research is required to understand how farkey changes in the institutional environmentinfluence the technology adoption decisions offirms.

In closing, we suggest that this study beextended along other dimensions. For instance, itwill be interesting to evaluate if some firms arehabitually more innovative than others; in otherwords, do certain firms have a systematicallygreater propensity to adopt new innovations inde-pendent of incentives? To follow up, one caninvestigate the effects of the experience withinitial adoption on subsequent adoption rates ofthe firm; that is, what is the effect of learningon future strategic behavior of firms? Thereafter,one can investigate the nature of performancedifferences between early adopters and late adop-ters of a new technology.


ACKNOWLEDGEMENTS

Discussions with or comments from CristianoAntonelli, Paul David, Nicholas Economides,Gerald Faulhaber, David Gabel, Don Lamberton,Padmanabhan Srinagesh, Jeffrey Rohlfs, and par-ticipants at the 22nd Telecommunications PolicyResearch Conference, Solomons, MD areacknowledged. We are grateful to three anony-mous referees for their detailed and thoughtfulcomments which have considerably improved thepaper and to Lindsay Evans for her help. Theusual caveat applies. S. K. Majumdar acknowl-edges financial support from the University ofMichigan Business School, while S. Venkatara-man has benefited from a grant from the Sol C.Snider Entrepreneurial Center of the WhartonSchool, University of Pennsylvania.

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