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Who Will Be the Adopters of 3G MobileComputing Devices? A Probit Estimation

of Mobile Telecom Diffusion

Jonathan Wareham & Armando Levy

Version of record first published: 18 Nov 2009

To cite this article: Jonathan Wareham & Armando Levy (2002): Who Will Be the Adopters of 3GMobile Computing Devices? A Probit Estimation of Mobile Telecom Diffusion, Journal of Organizational

Computing and Electronic Commerce, 12:2, 161-174

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Who Will Be the Adopters of 3G MobileComputing Devices? A Probit Estimationof Mobile Telecom Diffusion

Jonathan WarehamDepartment of Computer Information Systems

Georgia State University

Armando LevyDepartment of EconomicsNorth Carolina State University

We characterize the role of the diffusion of information in the demand (adoption) ofmobile telephones in the United States. Different strategies for identificationof the pa-rameters of the diffusion process are discussed. Using survey data from 1994 and 1998and a probit model of mobile phone adoption, we estimate the rate of diffusion andbounds for the long-run market shares for specific socioeconomic market segments.Implications for the diffusion of 3G mobile computing devices are explored.

3G mobile computing, telecommunications, diffusion, statistical analysis

1. INTRODUCTION: THE EMERGENCE OF 3G MOBILECOMPUTING DEVICES

Recent industry forecasts suggest that by the year 2004, more than 40% of all e-com-merce transactions will be initiated from a handheld device [1]. From its sibling, themobile telephone, mobile computing has grown out of the current capabilitieswithin wireless telecommunications to encompass not only voice-based communi-

cation but also active and passive versions of transaction management, digital con-tent delivery, and telemetry services [2]. The current wireless networking stan-dards, including time division multiple access, code division multiple access, andglobal systems for mobile communications, transmit at 9.6 to 19.2 kb/s and, al-though the growth in the mobile telecom network has been impressive by manymeasures, these speeds are significantly slower than most stationary applicationsand are therefore insufficient for functions requiring digital content delivery.

Anticipating the growth of mobile computing, third-generation (3G) wirelesstechnology standards are being negotiated through the International Telecommu-

JOURNAL OF ORGANIZATIONAL COMPUTINGAND ELECTRONIC COMMERCE 12(2), 161174 (2002)

Correspondence andrequests forreprints should be sent to Jonathan Wareham, Department of Com-puter Information Systems, Georgia State University, P.O. Box 4015, Atlanta, GA 303024015. E-mail:[email protected]


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nication Union and should be finalized and commercially deployable by the year2003. Third-generation standards will not only permit greater bandwidths from384 kb/s to 2 mb/s but will also embrace multimedia transmission, integrating

voice, data, and two-way video transmission.Despite the euphoric predictions from industry analysts, however, some skepti-

cism exists as to both the speed and character of such forecasts. In simple terms,concerns have been raised not only about the adoption rates, but also the viabilityof applications that do not yet exist, but ones we will not be able to live without. Inresponse, mobile commerce (m-commerce) advocates argue that the history of theInternet is an answer in search of a question [3], yet critics maintain that: (a) con-sumers remain unconvinced about the wireless Web, (b) wireless e-commerce con-tent is limited, (c) the U.S. mobile payment structure complicates m-commercedeployment, (d) the capabilities of handheld computing devices have been com-

pletely overestimated, and (e) the U.S. wireless telecommunications industry islagging behind the technology [46].The differences between mobile telecommunications and mobile computing are

not insubstantial. However, as one is arguably the parent of the other in terms oftechnology and end-user application, valuable insights from the diffusion of mo-bile telecommunications can guide managerial decision making about the growthand dispersion of mobile computing products.

To this end, we conducted a study of the diffusion patterns of mobile telecom-munications based on pooled cross-section survey samples of households con-ducted by Taylor Nelson Sofres. The first sample was gathered in 1994 andcontains information from 8700 households, and the second sample was gathered

in 1998 and contains information from more than 16 000 households. The data con-tain a variety of information about demographics: including income, location andconstitution of household, profession, gender, race, age, and education. The mainresearch question posed in this study is what socioeconomic factors are determina-tive to the diffusion of mobile telecommunications, and how can these findings canbe extended to 3G mobile computing? To address these issues, we have organizedthis article as follows.

In Section 2 we survey predominant practices within the econometric methodsapplied in theestimation of diffusionpatterns anddelineatethetechniques weusedinour study.Webegin the analysis inSection 3 byestimatingdiffusion rates and up-

perboundsonthepenetrationlevelsacrossthepopulationbasedongeographicareaand income levels. Although the significance found in regard to both income andsize of metropolitan area is illuminating, we continue the analysis by dropping theassumptionthat thediffusionprocess is thesame acrossmarkets andfocusonsocio-economicheterogeneity.Wedeterminethesocioeconomicfactorsthatexplainstatis-tical variation in adoption rates for cellular phones and further use these factors toidentify two distinctive groups with different diffusion rates as well as long-termmarket shares. Given this pattern of distinguishing diffusion speedsand saturationlevels,inSection4weexploreprospectsforthediffusionof3Gmobilecomputing.

2. DIFFUSION MODELS

The diffusion of technology and products has been approached from many differ-ent perspectives, including sociology [7], geography [8, 9], and marketing and con-

162 WAREHAM AND LEVY


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sumer behavior [10]. Twocommonpresumptionscanbe identifiedwithinthediffu-sion literature [11, 12]:

1. The intensity at which usage or ownership of a new technology spreadsacross an economy changes over time [13, 14], and

2. Plotting the usage or ownership of a new technology against time yields anS-shaped curve, frequently called a sigmoid or logistic curve [13, 15].

In the tradition of Schumpeter [16], who regarded the diffusion process of majorinnovations as the driving force behind the economy, most modern work on diffu-sion theory is based on the epidemic approach, which is, as implied, based on theo-ries modeling the spread of diseases. Grilliches [17] and Mansfield [13] pioneeredthe approach that views diffusion as resulting from the spread of information. Thisapproach is based on two assumptions: (a) A potential user will adopt the technol-

ogy on learning of its existence, and (b) information on the existence of the technol-ogy is spread by direct contact between the potential user and current user [18].According to Mansfield [14], the number of nonusers adopting in a particular timeperiod should increase as the proportion of users in the industry population in-creases because, as more information and experience accumulate, it becomes less ofa risk to begin using the technology.

The epidemic approach has been criticized on a number of grounds. The mostfrequently cited include [18]:

Risk is reduced only as the number of users increases, ignoring other channelsof information transmission, such as advertising [19].

Potential adopters are viewed as passive recipients rather than active seekersof information [20].

Technology is assumed to not change over time, ignoring improvements inflexibility, performance, and relative expense [21, 22].

The population of potential adapters is assumed to be homogeneous [15]. Relative profitability from adoption is assumed not to change along the diffu-

sion path [12], and positive externalities are ignored.

Another area of debate is the common practice of modeling technology adop-tion as an irreversible, all-or-nothing decision. As there is no obvious justification

why technology adoption is a static, categorical decision, the practice is most likelya result of the data collection pragmatics. Although the decision to acquire a mobiletelecommunications device can be registered in a binary response survey with rea-sonable confidence and consistent intuition, the data tell us little about variance inthe level or type of use. Although this may be a moderate deficiency when analyz-ing mobile telephones, which are more or less homogenous in function, the prob-lem is exacerbated by the heterogeneity of 3G mobile computingdevices, for whicha telephone is no longer just a telephone but rather constitutes any bundle of func-tions offered by telephones, personal data assistants, and so on.

2.1 Diffusion Estimation

The most widespread way of estimating models of diffusion on pooled cross-sec-tion or aggregated time-series data is to use a two-stage procedure that was first in-

A PROBIT ESTIMATION OF MOBILE TELECOM DIFFUSION 163


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troduced by Grilliches [17] in his seminal study on hybrid corn. In the first stage, alogistic,or some other S-shaped curve, is imposed onthedata ona proportion of theadopters. In the second stage linear regression is used to explain the slope coeffi-

cient of the fitted curves representing diffusion speed, in terms of various exoge-nous factors.

In addition, some studies have applied a one-stage estimation procedure in ag-gregate models of diffusion. In this instance, the parameters of a nonlinear diffu-sion curve, both the diffusion speed and saturation levels, are made functions of aset of economic variables, traditionally macro-economic variables. The model is es-timated in one step with nonlinear estimation procedures or maximum likelihoodmethods. Economic variables are then used to determine both the penetration lev-els and rate of diffusion, mainly in the context of new product technologies [19, 10].

The assumption of population homogeneity has only recently been addressed in

the marketing literature [23], where an approach was developed that explicitlyconsiders the determinants of the individual adoption decision. One importantvariable is time, and how temporal conditions correlate with one or more key char-acteristics that are assumed to be crucially important in determining responsive-ness to the technology. Differences in adoption timing happen because potentialadopters differ from each other. This approach has been called probit [15] or rank[19] because the empirical application makes use of probit models and ranks firmsby their significant characteristics.

Our study represents a hybrid of these two perspectives. We began by estimat-ing diffusion rates and upper bounds on the penetration levels across the popula-tion based on geographic area and income levels similar to a one-stage aggregate

diffusion model. Although the significance found in regard to both income andsize of metropolitan area was illuminating, we dropped the assumption that thediffusion process is the same across markets and focused on socioeconomic hetero-geneity, as is common in probit [15] or rank [19] studies. We used socioeconomicfactors that explain statistical variation in adoption rates for cellular phones toidentify groups with different diffusion rates as well as long-term market shares.We conclude our analysis by identifying two groups from the population with dis-tinctive diffusion speeds and saturation levels.

3. SURVEY DATA AND ANALYSIS

The data used in this study consist of two cross-sectional survey samples of house-holds recruited by Taylor Nelson Sofres. The first sample was gathered in 1994 andcontains information from 8700 households, and the second sample was gatheredin 1998 and contains information from more than 16 000 households. Each house-hold filled out a questionnaire and submitted copies of its telephone bills. The ques-tionnaire asked whether a household member owned a mobile telephone and ques-tions related to the composition of the household, education level of the head ofhousehold, income, and other socioeconomic factors.

In Table 1 we list the proportion of households that owned a mobile telephone in1994. Because two of the main determinants of mobile-phone adoption are thewealthoftherespondentandthepotentialsizeofthemobile-phonenetwork,weseg-



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mented the data by annual household income and the size of the metropolitan ser-vice area according to the U.S. Bureau of the Census (http://www.census.gov/).Cellsinthetablewereexcludediftherewerefewerthan30householdsinacategory.

3.1 A Simple Diffusion Model of Mobile Phone Adoption

Let xtbe the population of a particular market segment t timeperiods after the intro-duction of a new technology. Letztbe the number of people in the population whohave adopted the new technology at time t. Define [0, 1] as the long-run marketpenetration of the technology in the population, hence in the long run xt andzt will

be related as

zt = xt.

For a mature technology, any diffusion of information about the benefits of adop-tion has occurred, and market penetration centers around the equilibrium level that depends on price, availability of competitive technologies, current consumertastes, and general economic conditions. We expect to drift slowly with time.

For a new technology the population dynamics are very different. When a newtechnology is introduced there is often little known about the potential benefits ofadoption. When successful, a small group of initial users learn the benefits of the

new technology, and this information spreads to other potential customers, whoquickly adopt the new technology [14]. Let yt [0, 1] denote the proportion of the


Table 1

Penetration Rates for Mobile Telephones 1994

Size of MSAa

Incomeb Non-MSA 50 to 250 250 to 500 500 to 999 1000 to 2500 2500+

under 7500 .000 .000c .000c .000c .001c .002

7500 to 9999 .000c .000c .000 .017 .024

10 000 to 12 499 .012 .024c .000c .000c .048c .071c

12 500 to 15 000 .009 .053c .000c .051c .068c .073c

15 000 to 19 999 .005 .040c .049c .030c .037 .044c

20 000 to 24 999 .034 .016 .039 .000 .052 .048

25 000 to 29 999 .021 .038c .000c .059c .031 .021c

30 000 to 34 999 .046 .010 .104c .100c .040 .055

40 000 to 44 999 .085 .101c .089c .139c .070 .051

45 000 to 49 999 .035 .036c .064c .061c .060 .060

50 000 to 59 999 .083c .083c .123c .140c .086 .141

60 000 to 69 999 .104 .095c .151c .157 .137 .135

70 000 to 69 999 .149c .143c .101c .163c .158 .167

70 000 to 74 999 .179c .189

75 000 to 99 999 .148c .222c .266 .214

100 000 to 149 999 .353c .318c

150 000 to 199 999 .467c .357c

Note. MSA = metropolitan service area.aGiven in thousands. bGiven in dollars. cLess than 100 respondents.


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potential market that is informed about the benefits of the new technology and hasadopted it. Hence, in a the case of a new technology, the equation for market pene-tration becomes

zt = ytxt,,

where the trajectory ofyt is governed by the diffusion of information in the popula-tion.

We expected that different thresholds of information will be necessary for differ-ent consumers to adopt the technology. Some consumers will adopt immediately,whereas others will hold out until they receive multiple favorable reports. To cap-ture this, we imposed the condition that yt exhibit an inverted S shape whengraphed against time. A class of functions that exhibits this shape is the set of all

probability distribution functions of a positive random variable. The random vari-able can be interpreted as the amount of time before a typical consumer hears con-vincing evidence that the benefits of adoption outweigh the costs [24]. Lety f u dut = ( | )0 where is the density function of some positive random variablewith parameter , which describes the mean, variance, and all other aspects of thedistribution. Hence, while describes the long-run market share, describes thetrajectory or diffusion to the long run. If we assume a constant growth rate ofinthe population, this gives:

where Kis the size of the population at the time the technology is introduced (t =0).Hence, to forecast the size of the market for a new technology at some future time (t> 0), we need to know the long-run penetration and the parameters of the diffu-sionprocess . Asanexample, Figure 1 shows a samplediffusion where =andytevolves as a gamma distribution with shape parameter = 5. The populationgrowth rate is 5%.

Moreover, at any time t in the short run we may observe the current and past

market sharesz

x

yt

t

t= , but without an estimate of the diffusion parameters we

cannot infer from the market sharesz

xt

t

. For any trajectory of market sizes, we

cannot distinguish between a slow diffusion to a large long-run market (big , shal-low (t)) and a fast diffusion to a small market (steep (t), small ).

To identify the parameter of the diffusion process we need to impose somestructure onyt [24]. When the new technology is introduced simultaneously to dif-ferent market segments, we can use the adoption rates across markets to identifythe structure of the diffusion process.

Suppose the technology is offered simultaneously in n different markets, each

with a possibly different long-run equilibrium i, i = 1, , n. The market segmentscan be different geographic regions (countries, states, cities) or different socioeco-


0

( ) ,ttz K e f u du=


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nomicgroupswithinasinglearea(agesexcategories,highvs.lowincome,etc.).For

eachmarketattime twehavez

xyit

it

i it= wheretheindex idenotesdifferentmarkets.

Ifweimposethe condition thatthe diffusion process is thesameacrossall n markets,thenwehaveyit, and thelong-runmarket sharesare all identifiedupto scale; that is,

givenany t,wecansolvefortheothers.Forexample,ifweknow t, thenyz

xt

t

t

= 1

1 1

and we can identify any from the current share zx

it

it

. This allows us to bound the

long-run market sizes in the following way: Let t = max(t,, t) without loss of

generality. Write i iit t

it

z y

x= =( )1 wherey

z

xt

t

t

= 1

1 1. Hence, (t,, t) (1, t(1),

, t(1)) gives a crude boundary for the long-run market sizes in each of the n mar-kets. These boundaries can be narrowed if we hypothesize a maximum market size

[25].Ifthelargestmarketsharethatcanbeimaginedis


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Another description of the diffusion process would allow different processesytacross markets. For example, we might expect young, technology-literate individ-uals tohave a higher rate ofadoptionthen say, retireesover 65 years ofage. Wealsoexpect the rate of adoption to be positively related to the long-run market share 1.When the diffusion is positively related to the long-run market share (the mostlikely case), a higher penetration rate for one market over another at any point intime will persist, but over time we expect large variations in adoption rates early inthe diffusion and to lessen later [26]. Socioeconomic factors that explain statisticalvariation in the adoption rates for cellular phones will identify groups with differ-ent diffusion rates as well as long-run market shares. Furthermore, we expect theexplanatory power of these factors to decrease over time [27]. In the next section we

specify a class of diffusion processes that vary with the long-run market size.

3.2 A Diffusion Model of Market Heterogeneity

As a first step, we need to identify the socioeconomic factors that differentiate mar-ket segments for cellular phones. Table 3 displays maximum likelihood estimatesfor a probit model of cellular adoption from our sample. Estimates are shown fromthe 1994 sample, the 1998 sample, and the combined sample. The results give asharper view of the socioeconomic factors that differentiate markets.

In Table 3 one can see that the most important predictor ofcell phone adoption isincome, which is strongly significant across all three models. The population of the


Table 2

Upper Bounds for Long-Run Market Share

Size of MSAa

Incomeb Non-MSA 50 to 250 250 to 500 500 to 999 1000 to 2500 2500+

under 7500 .000 .000 .000 .000 0.002 .004

7500 to 9999 .000 .000 .000 0.036 .054

10 000 to 12 499 .026 .051 .000 .000 0.103 .152

12 500 to 15 000 .019 .113 .000 .109 0.145 .156

15 000 to 19 999 .011 .086 .105 .064 0.079 .094

20 000 to 24 999 .073 .034 .084 .000 0.111 .102

25 000 to 29 999 .045 .081 .000 .126 0.066 .045

30 000 to 34 999 .098 .021 .223 .214 0.086 .118

40 000 to 44 999 .182 .216 .191 .298 0.150 .109

45 000 to 49 999 .075 .077 .137 .130 0.128 .128

50 000 to 59 999 .178 .178 .263 .300 0.184 .302

60 000 to 69 999 .223 .203 .323 .336 0.293 .289

70 000 to 69 999 .319 .306 .216 .349 0.338 .357

70 000 to 74 999 0.383 .405

75 000 to 99 999 .317 .475 0.570 .458

100 000 to 149 999 0.756 .681

150 000 to 199 999 1.000 .764

Note. MSA = metropolitan service area.aGiven in thousands. bGiven in dollars.


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geographic area of the household (the size of the censuss metropolitan servicearea) is also a positive predictor of cell phone demand, along with being married.The occupation of the head of household is a very strong predictor of cell phone

adoption. As one might expect, people in sales positions consistently adopt cellphones at the highest rates, followed by executives, whereas professionals do notadopt at significantly higher rates than the general population. Given the travel in-herent in sales and executive positions, this confirms our intuition. It is interestingthat African American respondents adopted cell phones at higher rates than thegeneral population, whereas Hispanic and Asian respondents are not statisticallydifferent from the general population. One household characteristic was found tobe a consistent negative predictor of cell phone adoption: the presence of children.Because households with children are generally less mobile than those without,this is expected. It is interestingthat neither education nor age seems to be a signifi-

cant predictor of mobile phone adoption.

3.3 Market Heterogeneity and Long-Run Market Share

Althoughthe resultsofTable 3 help to identify groupsof the population with differ-ent diffusion processes, we still need to formalize a link between the long-run mar-


Table 3

Probit Maximum Likelihood Estimates

Sample

1994a 1998b Pooledc

Variable M SE M SE M SE

Constant 2.4894* 0.1248 1.6699* 0.1713 2.2155* 0.0555

Income 0.1103* 0.0072 0.0718* 0.0028 0.0777* 0.0025

Education level 0.013 0.0237 2.6004 0.0106 0.0085 0.0095

Age 0.0355* 0.0107 0.0021 0.0039 0.001 0.0037

Population 0.0404* 0.0125 0.0098 0.0053 0.0148* 0.0049

Household size 0.0565* 0.0243 0.0408* 0.0149 0.027* 0.0094

Own residence 0.1667* 0.0610 0.1332* 0.0329 0.0222 0.0267

Married 0.1199 0.0625 0.0615* 0.0307 0.0916* 0.0271

Children 0.0386 0.0641 0.1331* 0.0376 0.0655* 0.0226

Executive 0.4190* 0.0579 0.1138* 0.0348 0.2067* 0.0293

Professional 0.0081 0.0648 0.0301 0.0396 0.0061 0.0329

Sales position 0.5498* 0.0789 0.1808* 0.0505 0.2813* 0.0421

Student 0.4144* 0.1867

Hispanic 0.0109 0.0978 0.0138 0.0344 0.0193 0.0323

African American 0.2924* 0.1144 0.1324* 0.0660 0.1576* 0.0568

Asian 0.2195 0.1753 0.0409 0.1732 0.0936 0.1227

Dummy (1998) 0.3059 0.1305

Log likelihood 2195 7792 10 063

an = 8731. bn = 16 089. cn = 24 820.*p < .05.


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ketshare andthediffusionprocessyt. Tocapture the dependenceof the diffusion onthe long-run market share, we specify our diffusion as:

where is a parameter to be estimated. This specification allows us to rescale thetime dimension of a baseline diffusion according to and . Hence, is the diffu-sion process when the long-run market share is 1, and for any


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4. IMPLICATIONS FOR 3G MOBILE COMPUTING DEVICES

We can continue to expect to see a positive correlation between income levels andmobile telephone adoption. This trend can be intuitively extended to mobile com-puting applicationsaswell. Althoughthesizeof the metropolitan area of the house-hold is also a positive indicator, this factor is more ambiguous. First, we expectcolinearity with income and the size of the censuss metropolitan service area. Sec-ond,thereismostlikelyaweakimplicationthatthepositivecorrelationresultsfrom

positive network externalities; that is, the value of the mobile telephone is corre-latedwiththesizeofthenetwork.Thedegreetowhichthesefactorsareequallyrele-vant in the relationship between city size and 3G mobile computing devices is diffi-cult to postulate, but we believe that it would have less significance with mobilecomputing applications that are increasingly heterogeneous in function.

The occupation of the head of household is a very strong predictor of mobilephone adoption; in particular, people in sales positions consistently adopt cellphones at the highest rates, followed by executives, and professionals do not adoptcell phones at significantly higher rates than thegeneral population. We expect thiscorrelation to be equally strong, if not stronger, in 3G mobile computing applica-

tions because we assume that a great deal of the functionality offered in these de-vices will complement and support professional occupations. For example,doctors, who are less mobile, may be prone to use one type of device, whereas indi-viduals in sales and executive positions may adopt another, given the travel inher-ent to these positions. Hence, as 3G applications become increasinglyheterogeneous we would look for occupation as a positive predictor of both adop-tion rate and type of application.

There are a number of positive predictors of cell phone demand that are intu-itively more opaque. African American respondents adopted cell phones at higherrates than the general population, whereas Hispanic and Asian respondents werenot statistically different from the general population. Not only is this result diffi-

cult to understand in the context of mobile telecommunications, but also it isequally problematic to extrapolate to mobile computing applications. We also


Figure 2. Estimated diffusion processes for Groups 1 and 2 from 1990.


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found one household characteristic to be a consistent negative predictor of cellphone adoption: the presence of children. Because households with children aregenerally less mobile than those without, this is does not contradict intuition. Con-

versely, being married is positively correlated with mobile phone adoption.Finally, neither education nor age seems to be a significant predictor of mobilephone adoption.

As mobile computing applications emerge, it will be interesting to determine if,and why, significant differences emerge within ethnic groups. Moreover, it is oftenassumed that education levels equate with greater technical literacy and, hence,higher adoption rates. This certainly has not proven correct in our sample, but dif-ferences may emerge if 3G applications become more technically demanding.However, as most products within multimedia and telecommunications evolve,user friendliness has been consistently stressed in an effort to broaden the market

to the general population. If this is the case with 3G applications, then educationlevel may continue to be insignificant. A similar argument can be made concerningage. To the degree that applications require technical savvy, we may find somenegative correlation with ageandadoption levels. Should applicationsbe designedto serve the needs of a general market, which is probable, then age will not be a sig-nificant predictor.

This analysis offers some additional provocative insights for 3G mobile comput-ing devices. In identifying two major groups in the popular as either fast adopters(Group 1) and normal representatives of the population (Group 2), we can makecertain predictions about mobile computing applications. In instances where spe-cific segments of the population can be identified as fast adopters, their adoption

rates will likely be significantly faster than the rest of the population. Marketingex-penditures targeted at these groups, who are normally considered rich target mar-kets, may in fact be delinquent and suboptimally applied. For example, as early as1994, about 60% of the households inGroup 1 who would adopt in the long run hadalready done so, whereas only about 30% of the households in Group 2 hadadopted mobile communications! Although speculative, few would claim that itwas common belief that 60% the market for sales professionals was penetrated asearly as 1994. As early as 1996, Group 1 was approaching full saturation levels at85%, whereas Group 2 remained at roughly 55%, a difference of 30%. By 1998,Group 1 had attained 90% saturation, whereas Group 2 had attained 85%. So, not

only are diffusion rates significantly faster in the intuitively appealing target mar-kets, but also long-term saturation rates may arrive earlier than otherwise antici-pated (see Figure 2).

4.1 Limitations

As we mentioned earlier, extrapolating sample results to a subsequent genre oftechnology presents several challenges. Adoption of mobile telephones is fairlysimpleand can becapturedina binaryresponsemodel asanall-or-nothingdecisionwithout extreme oversimplification, but 3G computing devices will be both more

sophisticated and heterogeneous. Modeling adoption as a static decision may ne-glect important information about level and type of use. Second, the assumption



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that mobile computing is a logical descendant of mobile communications may nothold as the breadth of functionality expands. Finally, in this study we neglected toaddress the manner in which mobile communications technology has improved

through time. As technology improves in terms of efficiency and applicability, itshould be possible to identify different development stages, or generations, pre-sentingdistinct diffusioncourses [18]. The improvement of technology is an impor-tant parameter that determines its diffusion path, but we neglected this in thisstudy. For 3G computing devices still in conception the relative evolution in thefunctionality/yield relationships should significantly affect diffusion patterns.

5. CONCLUSIONS

We examined the diffusion of mobile telecommunications on the basis of across-pooled sampleofdata from1994and 1998. Wefound that, asexpected, mobiletelephone adoption is positively correlated with income and metropolitan area andis strongly correlated with occupation, specifically, sales and executive profession-als. However, other parameters of our estimated model were curious. For example,we found that one ethnic group, African Americans, adopted mobile phones at arate significantly faster than that of the general population, whereas families withchildren were clearly negatively correlated with rates of mobile phone adoption.Contrary to expectations, neither age nor education level was correlated with mo-bile phone adoption. Given these socioeconomic parameters, we have identifiedtwodistinctivegroupsof thepopulation: fast adoptersandthegeneral population.

We have extrapolated these findings to predict the adoption of mobile comput-ing devices. We expect that occupation will continue to be an important predictorof 3G mobile computing devices, as the functionality offered will support specificprofessions. Our analysis also suggests that neither age nor education will be deter-minative of 3G mobile computing devices, assuming that the interface design ofthese devices is targeted toward the general population and does not requireabove-average technical proficiency. Finally, our analysis suggests that there willbe specific groups in the population that adopt 3G computing devices at a rate sig-nificantly faster than that of the general populace. In the instance that these groupscan identified, penetration levels in these groups will be substantially higher than

is commonly expected of thepopulation at large. This has implications for develop-ment and marketing of 3G mobile computing products.

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