Intern. J. of Research in Marketing 26 (2009) 304–313

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Intern. J. of Research in Marketing

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The impact of adoption timing on new service usage and early disadoption

Remco Prins a,⁎, Peter C. Verhoef b, Philip Hans Franses c

a Erasmus University Rotterdam, Department of Marketing, Office H15-09, P.O. Box 1738, NL-3000 DR Rotterdam, The Netherlandsb University of Groningen, Department of Marketing, The Netherlandsc Erasmus University Rotterdam, Econometric Institute, The Netherlands

⁎ Corresponding author. Tel.: +31 20 598 2565.E-mail address: rprins@feweb.vu.nl (R. Prins).

0167-8116/$ – see front matter © 2009 Elsevier B.V. Adoi:10.1016/j.ijresmar.2009.07.002

a b s t r a c t

a r t i c l e i n f o

Article history:First received in 30, June 2008and was under review for 5 months

Area editor: Jacob Goldenberg

Keywords:AdoptionPostadoption usageNew productsNew servicesDiffusionTelecommunicationsEndogeneity

Post-adoption usage can be a crucial element in obtaining substantial revenues from new service introduction,especially when adopters display low usage levels or decide to disadopt the service altogether. Here, theauthors specifically examine the effects of adoption timing on post-adoption usage and disadoption. Using alongitudinal, individual-level usage data set of 6296 adopters of a new telecom service, they show thatthe earliest adopters have lower initial usage levels than do later adopters. However, early adoptersshow increasing usage after adoption, whereas late adopters tend to decrease their usage over time. Also,disadoption rates are higher among later adopters.

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© 2009 Elsevier B.V. All rights reserved.

1. Introduction

In many saturated markets, companies seek to increase revenuesby introducing new products and services, thereby generating organicgrowth. Successful introductions are beneficial in achieving higherrevenues from current customers and can also attract customers fromcompetitors. Failing to keep up with the innovations in the marketmay lead to lower revenues from current customers and to customerchurn, which in turn will slow down a firm's growth (Gatignon &Xuereb, 1997; Prins & Verhoef, 2007).

For many new products and services, not only adoption but alsosubsequent usage is very important. For example, if a customer adoptsa Nespresso coffee machine, Nescafe will gain more revenues throughthe usage of Nespresso coffee pads if the customer uses the coffeemachine. The same holds for many subscription-based services, suchas mobile telephone and Internet services. Increased usage leads tocustomer expansion, which is a key driver of customer lifetime valueand customer equity (Bolton, Lemon, & Verhoef, 2004; Gupta et al.,2006; Hogan, Lemon, & Libai, 2003), which in turn are linked toshareholder value (Gupta, Lehmann, & Stuart, 2004). The issue of theadoption and subsequent usage of services is essential in the telecomindustry, where firms have invested huge amounts in new service

technologies, such as Universal Mobile Telecommunication Systems(UMTS), in the hope that new 3G (third-generation) services willcause growth in the saturated telecommarket (The Economist, 2004).3G services are still growing and are an important source of organicgrowth for telecom firms (Lomas, 2008). Early disadoption of thesenew 3G services and low usage levels may have a detrimental effecton firm revenues (Hogan et al., 2003).

Achieving a high level of new service usage is not an easy task forcompanies, as theremight bea trade-off between the rapid adoptionof anew service and its subsequent usage. Firms may successfully markettheir new services with advertising campaigns (Prins & Verhoef, 2007)and achieve a large number of adopters fairly quickly. However, if thenew service is not fully developed, theutility of the new servicemay stillbe low, and this can cause subsequent disadoption and/or lower serviceusage (Bolton & Lemon, 1999). Moreover, the early adopters of a newservice are not necessarily the heavy users (Ram & Jung, 1994). As aresult, the service provider may end up with a large set of adopters butalso experience low service usage levels and multiple disadopters in anearly stage. In this paper, we focus on the effect of adoption timing onthe usage and early disadoption of newly introduced services.

Although the adoption process itself has gained considerableattention in marketing, knowledge on disadoption and service usageis relatively scarce. While knowledge about the degree of use afterinitial adoption is required for a full picture of the adoption process foran innovation (Mahajan, Muller, & Bass, 1990; Robertson & Gatignon,1986), most adoption studies (e.g., Meuter, Bitner, Ostrum, & Brown,

Table 1Overview of relevant studies on customer postadoption behavior.

Study Disadoption Postadoption usage Individual customer level data Longitudinal data Study on effect of adoption time

Morgan (1979) √ √Danko and Maclachlan (1983) √ √Mahajan, Muller, and Srivastava (1990) √ √Ram and Jung (1994) √ √ √Parthasarathy and Bhattacherjee (1998) √ √ √Bolton and Lemon (1999) √ √ √Lemon et al. (2002) √ √ √Hogan et al. (2003) √ √ √Shih and Venkatesh (2004) √ √Kim and Malhotra (2005) √ √ √Wood and Moreau (2006) √Gielens and Steenkamp (2007) √ √Libai et al. (2009) √ √This study √ √ √ √ √

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2005; Prins & Verhoef, 2007; Steenkamp & Gielens, 2003) only modeltrial. Several studies do integrate repeat purchases into their models(e.g., Kamakura & Balasubramanian, 1987; Norton & Bass, 1987), andsome studies consider post-adoption behavior at the individualcustomer level. In Table 1, we provide an overview of these studies.

Disadoption has been studied using both aggregated and individual-level data, applying both cross-sectional and longitudinal researchdesigns. Using longitudinal individual-level data, Lemon, White,and Winer (2002) show that regret is an important determinant ofdisadoption. Several studies consider post-adoption usage at theindividual customer level (Gielens & Steenkamp, 2007; Shih &Venkatesh, 2004, Wood & Moreau, 2006), mainly using cross-sectionaldata to gain insight into various determinants of postadoptionusage, such as ease of use. Other studies focus on the differencesbetween early and late adopters in terms of their usage levels (e.g.,Danko & MacLachlan, 1983; Mahajan, Muller, & Srivastava, 1990;Morgan, 1979; Ram & Jung, 1994). These studies offer no conclusiveevidence, which could be due to the fact that they use cross-sectionaldata. Hence, there seems to be a lack of understanding of usagedevelopment over time and how it might be affected by adoptiontiming. The time dimension seems critical because individual usagelevels of many services likely vary over time. Also, one may expectvariation in behavior across customers. Consumers can try a newproduct several times, gradually learn about its advantages anddisadvantages, and adjust their behavior accordingly (Boulding, Kalra,Staelin, & Zeithaml, 1993; Hoch & Deighton, 1989; Shih & Venkatesh,2004; Villas-Boas, 2004;Wood &Moreau, 2006). The dynamic nature ofindividual usage patterns therefore requires a longitudinal approachwhen one is studying post-adoption usage. Moreover, a cross-sectionalapproach that measures a single point in time ensures that earlyadopters have more usage experience than do late adopters, whichmakes it difficult to distinguish adoption time effects from learningeffects. The overview in Table 1 also clearly shows that there is alack of studies investigating both disadoption and service usage.We believe that in order to achieve a full picture of post-adoptionbehavior, onemust study both disadoption and usage (e.g., Robertson &Gatignon, 1986).

The primary objective of our study is therefore to further investigatethe disadoption and usage of new services by early adopters. Wespecifically focus on the effect of adoption timing and how this effectchanges over time in the first months of usage. Furthermore, weinvestigate the impact of several customer characteristics on usageand early disadoption, such as relationship age and current categoryusage. We use longitudinal data on 6296 individual adopters of anewly introduced telecommunications service and show that adop-tion time initially is positively related to service usage. However,relatively late adopters tend to show decreasing usage levels over the

followingmonths. Moreover, later adopters tend to disadopt faster thanthe earliest adopters.

In the next section, we discuss the prior literature in more detail.Subsequently, we present our hypotheses about the effects ofadoption timing and of selected customer characteristics on disadop-tion and service usage levels in the early usage stages. In the sectionsthat follow, we describe our data, the statistical model, and the resultsof our analysis. Additionally, using a simulation, we demonstratehow inducing faster adoption may impact customer revenues, whichaffects organic growth. We end with a discussion of the most impor-tant findings, managerial implications, and research limitations, aswell as various issues for further research.

2. Conceptual background on the effect of adoption time

Throughout innovation literature, the notion is that early adoptersdiffer substantially from late adopters, such as in terms of demo-graphics (Rogers, 2003) or innovativeness (Hirschman, 1980;Midgley& Dowling, 1978). However, the effect of adoption timing on post-adoption usage and disadoption is not clear. Rogers (2003) posits thatearly adopters, because of their innovative nature, typically havebetter technological skills that enable them to use the new product orservice more extensively, so that early adopters should display higherusage levels than later adopters. Some empirical evidence at theaggregate level supports this theory. In addition, Morgan (1979) findsthat early adopters of banking services, on average, use the servicemore than later adopters, and multiple studies provide similar resultsfor home-PC usage (Danko & MacLachlan, 1983; Dickerson & Gentry,1983; Mahajan, Muller, & Srivastava, 1990). However, Ram and Jung(1994) find no significant difference in usage frequency between earlyadopters and the early majority for household technologies. In themobile telecommunications industry, Jain, Muller, and Vilcassim(1999) find that the first adopters of mobile phones have higherusage levels than the later adopters. This difference is mainly causedby the fact that the earliest adopters use their mobile phones forbusiness purposes, whereas the regular consumers adopt the mobilephone during a later stage.

Despite these findings, several other issues need resolution beforemore conclusive evidence regarding the effect of adoption timing onservice usage can emerge. Specifically, the moment of adoption thatthe consumer chooses is not independent of other factors that affectpost-adoption behavior. The adoption literature generally asserts thatmore innovative customers, who are usually younger (Rogers, 2003)and tend to display more domain-specific innovativeness (Im, Bayus,& Mason, 2003), adopt new services more quickly. Therefore, post-adoption usage levels should depend partially on demographics andinnovativeness instead of being solely dependent on adoption timing.

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Because these various factors play distinct roles, the nature of theoverall effect is not straightforward. For example, many early adoptersmight already be heavy users in the product category before theyadopt an innovation (Gatignon & Robertson, 1985; Jain et al., 1999;Mahajan, Muller, & Srivastava, 1990), which would imply that theeffect of adoption timing is partly an indirect effect of category usage.Moreover, if the provider of a new service wants to persuade existingcustomers to adopt the innovation, it will first target the so-calledhigh potentials—that is, customers who are most likely to adopt.Assuming that the provider uses some type of selection criterionbased on demographics or past purchase behavior, a customer'sadoption timing cannot be independent of these factors (Prins &Verhoef, 2007). However, simply including such variables in a post-adoption analysis could still cause amisspecification of the true effectsbecause adoption timing may correlate with the other explanatoryvariables. We therefore treat adoption timing as an endogenousvariable that is affected by consumer characteristics and priorpurchase behavior and that, in turn, could affect postadoptionusage. A conceptually similar approach is used by Chandrashekaranand Sinha (1995), who simultaneously model adoption timing andthe volume of adoption.

3. Hypotheses

In our attempt to map the dynamics of individual usage levels, weconsider initial usage levels and the development of usage levels overtime. Consumers' initial usage levels reflect their usage in the firstperiod after adoption, at which point we assume they have no priorexperience with the new service. Subsequently, consumers continueto use the new service and gradually adjust their usage levels asthey learn, over time, about the (dis)advantages of the new service.Eventually, consumers either arrive at a stage of sustained use at acertain level or decide to reject or disadopt the innovation altogether(Libai, Muller, & Peres, 2009; Shih & Venkatesh, 2004). We developand test hypotheses about both initial usage levels and changes inusage levels over time, considering disadoption with a focus on theeffects of adoption timing. Given our data limitations, we constructhypotheses only for relatively early adopters—that is, customers whoadopted the new service in the first year after its market introduction.

3.1. Adoption timing and initial usage

Our first hypothesis posits that the initial service usage level willbe higher for customers who adopt later. Several mechanisms maydetermine the initial usage level of early versus later adopters of high-tech services. Although the goal of our research is not to test which ofthe underlying mechanisms causes the changes in service usage, webriefly discuss them before we construct our hypotheses on usagepatterns. We consider the expected utility of the service and theuser skills of the adopter.

First of all, we consider the anticipated utility of the new service. Theassociated technology often is not developed to its fullest extent at themoment of introduction to themarket. Because service providers investheavily in innovations, theywant to launch their new services as soon aspossible to obtain returns on their investments and possibly achieve afirst-mover advantage. If the technology behind the new service stillrequires substantial improvements after its introduction, this may limitthe usage possibilities for early adopters in the first months becauseof, for example, compatibility issues between hardware and software(e.g., Gupta, Jain, & Sawhney, 1999; Nair, Chintagunta, & Dubé, 2004;Stremersch, Tellis, Franses & Binken, 2007). In addition, direct networkeffects are important for many technologies in that more people in thenetworkwill have adopteda technologyby the time later adopters enterthe market, resulting in higher overall utility, which might stimulateusage (e.g., Fisher & Price, 1992).

Besides considering the objective utility of a new service, adoptersmay also develop subjective expectations about its utility, dependingon the sources of influence they experienced in their adoptiondecision. Prior to adoption, later adopters are influenced mainly bytheir interpersonal communication with earlier adopters (Bass, 1969;Rogers, 2003). Because of their lower level of innovativeness, theycannot assess this subjective information effectively, which may leadto overly high initial expectations about the new service at themoment of adoption (Keaveney & Parthasarathy, 2001; Parthasarathy& Bhattacherjee, 1998). In contrast, early adopters may make theirinitial adoption decision on more rational grounds, predominantlyinfluenced by external information sources such as advertising (Bass,1969; Rogers, 2003). Hence, we expect later adopters have higheranticipated utility than early adopters, which would translate intohigher initial usage levels for later adopters.

Second, the skills of adopters may play a role in determining initialusage levels. Because early adopters typically have more experienceand skills pertaining to high-tech services (Rogers, 2003), they willmore easily learn to use the new service. Thismay increase their initialusage level as compared to that of later adopters. However, differencesin skill levels between adopters within the first year after serviceintroduction could be limited.

On the whole, we expect that despite the somewhat lower levelof technological skill at play, the higher expected utility of the newservice will cause higher initial usage levels for later adopters than forearly adopters. Thus, we hypothesize:

H1. Adoption timing has a positive effect on initial usage levels.

3.2. Effect of adoption timing on usage over time

The adopters' anticipated utility will affect service usage after thefirst trial period. In this period, consumers experience the actual benefitsof the new service and may adjust their expectations accordingly(Boulding et al., 1993). Among later adopters, the highly anticipatedutility may cause dissatisfaction when the reality does not meet theirexpectations (Anderson, 1973; Oliver, 1980). As Bolton and Drew(1991) show in a telecommunications setting, the differences betweenprior expectations and service performance affect perceived servicequality even more than service performance itself. In an empiricalstudy on PDA adoption, Wood and Moreau (2006) also find that thedisconfirmation of prior complexity expectations affects future usageexpectations and product evaluations in the first period after adoption.Moreover, empirical findings from various studies show that (dis)satisfaction is an important driver of service usage and discontinuanceof services (Bitner, 1990; Bolton & Lemon, 1999; Keaveney &Parthasarathy, 2001; Shih&Venkatesh, 2004). Effectively, later adoptersgradually learn that their expectations were too high, and their usagelevels decline. For some, this decline ultimately leads to discontinuanceof the service, described by Rogers (2003) as discontinuance resultingfrom “disenchantment”, which occursmore often among later adopters,as Parthasarathy and Bhattacherjee (1998) show. In contrast, earlyadopters experience more realistic anticipated utility and are not aseasily dissatisfiedwhen using the service; this should result in relativelyconstant usage levels over time, compared to those of later adopters.For both early and later adopters, usagemay be positively influenced bythe continuous development of the service itself.

The distinction in usage skills between early and later adopterswillbecome smaller over time as many less-skilled later adopters learn touse the new service. However, some less-skilled later adopters mayhave difficulty using the new service, and this may cause frustrationwith the technology (Parasuraman, 2002).

On the basis of the preceding arguments, we expect that the initialdifference in usage levels between early and later adopters willbecome smaller over time. Thus, time since adoption decreases theeffect of adoption timing on postadoption usage over time. In practice,

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this could mean that the usage levels of early adopters will start toexceed those of later adopters. Thus, we hypothesize:

H2. The positive effect of adoption timing on postadoption usagedecreases with greater time since adoption.

3.3. Effect of adoption timing on disadoption behavior

A possible consequence of declining usage may be that customersdecide to discontinue the new service altogether; this is also calleddisadoption (Hogan et al., 2003; Shih & Venkatesh, 2004). Serviceusage may therefore be a good predictor of disadoption behavior,as low usage levels may signal a high probability that the customerwill disadopt. According to Rogers (2003), customers discontinue theuse of a new product or service either because of disenchantment orbecause of replacement. For new high-tech products, both types ofdiscontinuance are likely to occur. Whereas replacement will mostlyhappen among innovators who move on to a newer technology,disenchantment will most likely occur among later adopters becauseof disconfirmation or frustration with the technology. In an onlineservice context, Parthasarathy and Bhattacherjee (1998) showedthat disenchantment discontinuance is the most common type ofdisadoption, and that disadoption rates are higher among lateradopters. Therefore, we hypothesize:

H3. Service usage has a negative effect on the probability ofdisadoption.

H4. Adoption timing has a positive effect on the probability ofdisadoption.

To test for the first two hypotheses, we estimate themain effects ofadoption timing and time since adoption on service usage, aswell as aninteraction effect between adoption timing and time since adoption.The third and fourth hypothesis will be tested by estimating themain effect of service usage and adoption timing on the probability ofdisadoption in a duration model.

3.4. Effects of relationship characteristics

We also investigate the impact of relationship characteristics onpostadoption behavior. Relational variables such as relationship ageand category usage may affect the use of additional services (Boltonet al., 2004; Prins & Verhoef, 2007; Reinartz & Kumar, 2003). Thecross-selling literature suggests that relationship age positively affectscross-buying (Kamakura, Wedel, de Rosa, & Mazzon, 2003). Both theadopter's usage level with regard to a preexisting service and theadopter's relationship age may serve as indicators of his or herexperience in the category and with the service provider. A higherlevel of experience may lead to a higher probability of the customer'sadopting the service (Gatignon & Robertson, 1991; Steenkamp &Gielens, 2003) and to a higher usage level, as a result of the greaterutility derived from additional services (Gatignon & Robertson,1985; Mahajan, Muller, & Srivastava, 1990). This higher utility fromadditional services could also decrease the disadoption probability inthe case of experienced customers. Along similar lines, previousstudies found that loyalty and experience increase service retentionrates (Lemon et al., 2002). We therefore hypothesize:

H5a. Relationship age has a positive effect on post-adoption usage.

H5b. Category usage has a positive effect on post-adoption usage.

H6a. Relationship age has a negative effect on the probability ofdisadoption.

H6b. Category usage has a negative effect on the probability ofdisadoption.

It may also be true that because of the higher level of experienceof loyal customers and heavy category users, these customers willhave more realistic expectations about the new service and thusnot be dissatisfied after initial usage. Whether this leads to stable orincreasing usage levels is an empirical question that we test byincluding interaction terms for both relationship variables and timesince adoption.

3.5. Covariates

We include several covariates in our analyses that appear inprevious studies as possible determinants of adoption and post-adoption usage. Including these factors enables us to separate theireffects on post-adoption usage from the effects of adoption timingitself. First, we expect consumer innovativeness to affect post-adoptionusage positively (Ram & Jung, 1994; Rogers, 2003) because adopterswho display a high degree of domain-specific innovativeness—which reflects their interests and knowledge within a certain productcategory (Goldsmith & Hofacker, 1991)—should have higher post-adoption usage levels. Domain-specific highly innovative userswould therefore also have a lower probability of disadopting due todisenchantment. However, these users may still disadopt throughreplacement with a newer technology (Rogers, 2003), so the overalleffect of innovativeness on disadoption behavior is not clear. Second,we control for the effects of demographics because we expect younger,male adopters to demonstrate higher usage levels (Rogers, 2003).Third, we control for changes in service price over time, includingfree trial periods. Finally, we try to control for technology effects anddirect network effects by estimating a time period specific fixed effectin our model.

4. Data

We investigate postadoption usage in the context of a new mobiletelecommunication service in the Netherlands that allows subscribersto browse the Web via their mobile phones. This service, based onGPRS (General Packet Radio Service) technology, was introduced tothe Dutch consumer market in August of 2002 by a single telecomprovider. At the time, regular Internet penetration in The Netherlandswas as high as 58% (and rose to 64% in the next two years), giving thetelecom provider a solid foundation of web familiarity among itscustomers to use in introducing a web-based service. After subscrib-ing, customers pay a small fixed monthly subscription fee and anadditional fee depending on the usage level. All adopters receive acertain number of free megabytes for downloads in the first fewmonths but have to pay for any additional usage. The usage fee andthe number of free megabytes adopters receive varies slightly overtime but can be controlled for. For our adoption sample, we havemonthly observations from the product introduction date of August2002 until August 2004. By August 2004, very few new adopters wereobserved. From the provider's customer database, we collect monthlydata about adoption time, service usage, past purchase behavior, andconsumer characteristics for 6296 randomly selected individual non-business adopters. For each adopting customer, we record serviceusage levels for the 12 months after adoption, which means we mustremove the latest adopters from our sample because we need at least12 months of post-adoption data. Therefore, in our study, the use ofthe term late adopter is relative because, given the time-frame, we donot have truly late adopters in our sample. However, in considerationof the relatively short diffusion time for the new service, we stillobserve all adopters up until the early majority.

The dependent variable new service usage is measured in down-loaded kilobytes per month. The service usage in the first monthafter adoption represents the adopter's initial usage level. Choosingan observation period of 12 months may seem rather arbitrarybecause adopters may not have reached a stable end usage level,

Fig. 1. Adoption time and time since adoption.

Table 3Correlation matrix (N=6296).

NSU AT Dis RA CU Age Gend DSI

New service usage(NSU)

1.00

Adoption time (AT) .31 1.00Disadoption (Dis) − .08 .00 1.00Relationship age (RA) − .07 − .06 − .25 1.00Category usage (CU) .02 − .12 − .33 .24 1.00Age − .13 .06 − .02 .14 − .15 1.00Gender (m=0) (Gend) − .03 .05 .12 − .17 − .08 − .07 1.00Domain-specific (DSI)innovativeness

.03 − .03 − .02 .12 .07 − .03 − .04 1.00

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but preliminary analyses at the aggregate level show that mostchanges in usage levels occur during the first few months afteradoption and that usage has more or less leveled off after 12 months.

A total of 1270 users disadopt the new service within the 12-monthperiod. We define disadoption as actively discontinuing the servicesubscription—which is possible at any moment in time—withoutleaving the service provider. Churning customers, whose motivationsto discontinue the new service may be very different from those ofregular disadopters, were deleted from our sample. For the remainingsubjects, we use an indicator variable to reflect disadoption statusat the end of each month (0=active, 1=disadopted). Importantly,we distinguish between users who do not use the service but stillpay the monthly fee and those who have actively discontinued theservice.

Our key explanatory variable, adoption time, reflects the numberof months between the introduction date of the service and theadoption date of the specific customer. The customer-specific timesince adoption is measured in months, starting at zero at the timeof adoption (see Fig. 1). If a customer disadopts within the first12 months, we observe service usage only until the moment ofdisadoption.

As noted, we also include several explanatory variables that reflectthe consumer's past purchase behavior. First, we measure use ofpreexisting telecom services (category usage) according to the averagemonthly amount spent by a customer over his or her total customerlifetime before adopting the new service. Second, we consider rela-tionship age, measured as the number of months an adopter has beena customer of the provider at the time of adoption. Third, we use adummy variable to indicate whether the customer had adopted aprevious-generation mobile service as a proxy for domain-specificinnovativeness. Fourth, we control for age and gender. The genderdummy equals 0 for male subjects. In Table 2, we summarize theincluded variables and their descriptive statistics, and we depict thecorrelation matrix in Table 3.

Table 2Measurements and descriptive statistics (N=6296).

Variable Measurement

New service usage Downloaded kilobytes per month (log transformaAdoption time Number of days between service introduction andDisadoption Dummy variable (1=disadopted)Relationship age Number of months the adopter has been with the

(log transformation)Category usage Average monthly amount spent with the service pAge Age in years at the moment of adoption (log transGender Dummy variable (male=0)Domain-specific innovativeness Dummy variable (1=adopted previous generation

5. Econometric model

Our model consists of three parts: adoption timing, new serviceusage, and disadoption. We first discuss adoption timing and newservice usage. Because we have made observations both across andspecific to individuals over time, we use a random-effects paneldata model to estimate the effects of customer characteristics, pastpurchase behavior, and adoption time on new service usage. Whenmodeling new service usage in our chosen empirical setting, weencounter two problems. First, a significant proportion of observa-tions indicate zero use (12% of our data set)—that is, adopters did notdownload anything during certain months. We account for the largeproportion of zero observations using a tobit model in which zero usecan be interpreted as a censored observation. Second, endogeneityexists for the key explanatory variable, adoption time, as we explainedpreviously. We account for this problem by estimating two equationssimultaneously, one for adoption time (ATi) and one for new serviceusage (NSUit), and including adoption time as an explanatory variablein the second equation. Thus, we separate the direct effects ofconsumer characteristics and past purchase behavior on new serviceusage from the indirect effects through adoption time (Greene, 2002).

Using a linear regression, we let (log) adoption time dependon relationship age (RAi), category usage (CUi), and the vector ofcovariates Zi, consisting of age, gender, and domain-specific innova-tiveness (see Eq. (1)). All variables in Eq. (1) are fixed over time.In Eq. (2), we allow new service usage to depend on the same setof covariates Zi, as well as on relationship age (RAi), category usage(CUi), adoption time (ATi), time since adoption (TSAit), and theinteraction of the latter two variables. A time period specific fixedeffect (τt) is added to control for general market trends andunobserved events during the observation period. We use a simul-taneous equations model consisting of a linear regression specifica-tion for adoption time (1) and a random-effects tobit specificationfor new service usage (2). We allow the error terms εi and νit tocorrelate so that we can test for the exogeneity of adoption timethrough a t-test of ρ[εi,νit]=0. If the correlation between the errorterms of both equations differs significantly from zero, adoption timeis not exogenous (Greene, 2002), and our model with endogenous

Mean Standard deviation

tion) 4.69 2.28adoption (log transformation) 5.47 .74

.20 .40service provider at the moment of adoption 2.59 1.26

rovider before adoption (log transformation) 7.65 1.31formation) 3.58 .31

.28 .42mobile service) .02 .15

Fig. 2. Mean service usage over time by adoption category.

309R. Prins et al. / Intern. J. of Research in Marketing 26 (2009) 304–313

adoption time should reflect the true relationships better than amodel that does not account for it.

ln ðATiÞ = α + β1 lnðRAiÞ + β2 lnðCUiÞ + β3Zi + εi; ð1Þ

and

NSUit⁎ = γ1 lnðATiÞ + γ2 lnðTSAitÞ + γ3 lnðRAiÞ + γ4 lnðCUiÞ+ γ5ððlnðATiÞx lnðTSAitÞÞ + γ6Zi + τt + ui + νit ;

ð2Þ

where

ATi =adoption time,RAi =relationship age,CUi =category usage,Zi =vector of consumer characteristics,TSAit =time since adoption,NSUit⁎ =latent utility of new service usage,NSUit =observed new service usage; andif NSUit⁎≤0, then NSUit =0; whereasif NSUit

⁎>0, then NSUit =NSUit⁎.

As a result of the log-transformation of time since adoption, boththis variable and the interaction term will cancel out of Eq. (2) whentime since adoption equals 1—that is, in the first month after adoption.This enables us to interpret γ1 as the influence of adoption time on theconsumer's initial usage level. In line with our hypotheses, we expecta positive value for γ1 and a negative value for γ5. We do not have aspecific expectation for γ2, which represents the direct effect of timesince adoption. As we hypothesize a positive effect of the relationshipcharacteristics, we expect positive values for γ3 and γ4. In addition,we also test this model including interaction effects betweenrelationship characteristics and time since adoption.

Next, wemodel disadoption behavior through a durationmodel, inwhich duration is measured as the time since adoption of the newservice. Duration models are widely used in new product adoptioncontexts (e.g., Prins & Verhoef, 2007; Sinha & Chandrashekaran, 1992;Steenkamp & Gielens, 2003) and service relationships (e.g., Bolton,1998). The hazard rate hit is given by the conditional likelihood that acustomer will disadopt at time t, given that (s)he is still an active user.Adopters who have not yet disadopted at the end of the 12-monthobservation period yield censored observations. Because we do notwant to assume a specific parametric form for the baseline hazard rate,and because we are mainly interested in the effects of the explanatoryvariables rather than the hazard rate itself, we use a Cox proportionalhazard model (Box-Steffensmeier and Jones, 2004; Cox, 1972). Thismodel leaves the baseline hazard rate unspecified, which allows us tocapture irregular disadoption rates over time. As specified in Eq. (3),the hazard rate depends on lagged new service usage (NSUt−1),adoption time (ATi), category usage (CUi), relationship age (RAi), andthe set of covariates Zi.

hit = h0t ½expðδ1 lnðNSUit�1Þ + δ2 lnðATiÞ + δ3 lnðRAiÞ+ δ4 lnðCUiÞ + δ5ZiÞ�;

ð3Þ

where

hit =Hazard rateh0t =Baseline hazard.

We use a lagged value for new service usage in the hazard functionbecause many disadopting customers use very little during the monthin which they disadopt, whichmay cause some causality problems. Byemploying lagged usage levels, we avoid this problem (Petersen,1995). Given the recursive nature of the usage and disadoptionprocess, we do not need a simultaneous equation estimation method

to estimate the parameters of the two-equation recursive system.Moreover, constructing a joint error process between the endogenoustobit model of Eq. (2) and the hazard model of Eq. (3) would increasethe model complexity disproportionately.

According to our hypotheses, we should expect negative valuesfor δ1, δ3, and δ4, indicating that higher service usage, a longerrelationship, and higher category usage all decrease the probabilityof disadoption. We expect a positive value for δ2, indicating that theprobability to disadopt will be higher for later adopters.

6. Empirical results

We first present some descriptive and aggregate statistics to offerpreliminary insights. Subsequently, we present the results of oureconometric model and test our hypotheses.

6.1. Descriptive analyses

To develop a preliminary view of postadoption usage dynamics,we divide our sample into three adoption categories. The first groupconsists of 844 consumers who adopted within 4 months of theintroduction of the new service, the second group consists of 1803consumers who adopted between 4 and 8 months after introduction,and the third group consists of 3649 consumers who adopted after8 months. The cut-off dates are arbitrary, but they result in cleardifferences in usage patterns among the adoption categories (seeFig. 2), and changes to these dates do not offer any significant changesin the interpretation. We index monthly usage such that the meanusage rate in the first month after adoption equals 100. Only activeusers are included—that is, customers who disadopt are no longertaken into consideration. Still, some subjects may have a usage level ofzero as long as they did not discontinue their service subscription.

On average, early adopters initially display usage levels ofapproximately 50 per month and gradually increase this to a usagelevel of around 80. The second adopter category shows the oppositepattern: they start off at a usage level of around 80 but graduallydecrease their usage to a more or less stable level of approximately50. The third category, which contains relatively late adopters, even-tually arrives at a stable level of 40 but starts at a much higher usagelevel of 140. Thus, initial usage levels are lower for early adopters, buttheir usage tends to increase somewhat over time, whereas lateradopters reveal a sharp decrease in usage in the first six months.Another important observation that arises from this figure is thatthe usage levels of later adopters fall below those of earlier adopters.Furthermore, the disadoption rate for the earliest group of adoptersis 13%; for the middle group it is 21%, and for the latest group it isalso 21%.

To see to what extent the usage patterns of later adopters aredriven by opportunistic users who discontinue using the service after

310 R. Prins et al. / Intern. J. of Research in Marketing 26 (2009) 304–313

using it extensively for some months, we perform a median split onour sample, based on the usage level in the first month after adoption.It emerges that disadoption is almost non-existent among the initial‘heavy’ users, even for the group of later adopters. Among the ‘light’users, disadoption rates are 19% for the earliest adopters, 34% for themiddle group, and as high as 54% for the latest adopters in our sample.The usage patterns of both heavy and light users look similar to thepatterns in Fig. 2. This suggests that the usage patterns of both earlyand later adopters are not so much dependent on the initial usagelevel and are not driven by discontinuance only.

Fig. 2 provides some initial evidence to support our theory, thoughthese are aggregate-level results that only take adoption time intoaccount without a distinction between general time trends anddevelopments in individual usage levels. Moreover, the differentpostadoption usage patterns among adopter categories could becaused by other factors such as consumer characteristics or pastpurchase behavior. We therefore present the results of the randomeffects tobit model and the proportional hazard model at theindividual level, which provide more conclusive outcomes regardingour hypotheses.

6.2. Model estimation results

6.2.1. Service usageIn Table 4, we show the results of the econometric analysis.

Model 1a/b is the random effects tobit model with endogenousadoption time that uses 6296 observations over 12 periods. Themodelwith endogenous adoption timing (Model 1b) has a significantlybetter fit than the one without, according to the likelihood-ratio test(p=.0000). To test for the exogeneity of adoption timing, we considerthe correlation between the error terms of Eqs. (1) and (2) (Greene,2002); according to a t-test, this correlation is significantly smallerthan zero (ρ=− .64; p=.0000), so we reject the null hypothesis ofexogeneity.

Regarding the results for adoption timing in Model 1a, youngerand male consumers have shorter adoption times. Relationship agehas a negative effect; consumers who have been with the companyfor a longer time adopt the new service earlier. In line with ourexpectations, consumers with high category usage levels and thosewho adopted an earlier-generation mobile service tend to adoptearlier. However, the effects on adoption timing in model 1a areconditional upon the adoption of the new service, because we onlyconsider adopters. Therefore, the results for adoption timing cannotbe directly compared with those of duration model studies in whichadoption timing is the main variable of interest and both adopters and

Table 4Model estimation results for service usage (N=6296).

Model 1a

Dependent Adoption time

Independent Effecta t-value

Age .14 13.36⁎⁎Gender (m=0) .08 10.58⁎⁎Domain-specific innovativeness − .06 −3.74⁎⁎Relationship age − .02 −7.30⁎⁎Category usage − .07 −19.09⁎⁎Adoption timeTime since adoptionAdoption time×Time since adoptionRel. age×Time since adoptionCategory usage×Time since adoptionLog-likelihoodBIC

⁎p<.05.⁎⁎p<.01.

a Positive effect means a longer adoption time.

non-adopters are included (e.g., Prins & Verhoef, 2007). The estimatedeffects on new service usage (Model 1b) are similar when it comes todemographics: younger and male adopters have higher usage levels.Adopters who previously adopted an earlier-generation mobile webservice also tend to use the new service more.

With regard to our hypotheses, we find that initial usage levels arehigher for later adopters, which supports H1. Although the directeffect of time since adoption is positive, our results show a negativeinteraction effect between time since adoption and adoption time.Thus, the positive effect of adoption time decreases over time, insupport of H2. Customers with a higher relationship age use the newservice less, contrary to H5a. As expected, adopters with higher usagelevels for the category tend to have significantly higher usage levelsfor the new service, in support of H5b.

We investigate the usage patterns further by including interactionterms between relationship characteristics and time since adoptionin model 2b (Table 4). Including the interaction effects improvesthe model fit significantly (p<.01). For this model, we also treatedadoption timing as an endogenous variable, but these parameters areexactly the same as in model 1a, so we do not report them again. If wecompare the estimation results of model 2b to those of model 1b, wesee that the interaction effects do not change the direct effects of therelationship characteristics. The interaction effect occurring betweencategory usage and time-since-adoption is not significant. Relation-ship age has a negative interaction effect with time-since-adoption,which means that loyal customers start at lower usage levels anddecrease their usage over time; this is somewhat unexpected.

6.2.2. DisadoptionThe results of the proportional hazard model pertaining to

disadoption are shown in Table 5. As expected in H3, service usagehas a negative effect on the hazard rate, implying that higher usagedecreases the probability of disadoption in the next period. Thesignificant positive effect of adoption timing shows that later adoptershave a higher probability of disadopting, which supports H4. We see asignificant negative effect of relationship age and category usage,meaning that loyal customers and heavy category users have a lessertendency to disadopt, supportingH6a andH6b. Furthermore, the resultsshow that women and innovative customers are more likely todisadopt. Age does not have a significant influence on disadoption.

6.2.3. Robustness checksWe use several checks to test the stability of our model results.

First, we randomly split our sample into two equal groups and find nodifferences in the model results. Second, we add nonlinear terms of

Model 1b Model 2b

New service usage New service usage

Effect t-value Effect t-value

−1.36 −65.21⁎⁎ −1.36 −64.73⁎⁎− .46 −27.20⁎⁎ − .46 −26.82⁎⁎

.84 21.31⁎⁎ .84 21.61⁎⁎− .19 −32.45⁎⁎ − .12 −4.70⁎⁎

.17 28.74⁎⁎ .14 7.84⁎⁎1.40 63.52⁎⁎ 1.43 49.99⁎⁎2.04 25.50⁎⁎ 1.96 23.59⁎⁎− .68 −49.03⁎⁎ − .67 −39.79⁎⁎

− .05 −3.58⁎⁎− .01 − .91

−124955.6 −120495.13.7311 3.5985

Table 5Hazard estimation results disadoption behavior (N=6296).

Model 3

Dependent Disadoption hazard rate

Independent Effecta t-value

Age − .16 −1.71Gender (m=0) .47 7.83⁎⁎Domain-specific innovativeness .58 2.89⁎⁎Relationship age − .40 −15.20⁎⁎Category usage − .42 −26.65⁎⁎Adoption time .46 8.58⁎⁎New service usage (t−1) − .12 −9.87⁎⁎Log-likelihood −12439.89BIC .3721

⁎p<.05.⁎⁎p<.01.

a Positive effect means a higher probability to disadopt.

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time since adoption to our model, but the empirical finding of anegative interaction effect between adoption timing and time sinceadoption does not change. Third, we estimate models that considerusage periods of 6 and 18 months, respectively. Although this affectsthe number of observations, there are no major changes in thedirection of the effects. Fourth, we test for effects of service pricechanges, limited free usage periods, and direct network effects.However, the price effects turn out not to be significant. Adding avariable that reflects the cumulative number of adopters in a certaintime period to measure network effects results in a significantlypositive effect but does not change the effects of the other variables ofinterest. Moreover, adding this variable makes it impossible toestimate time period specific fixed effects, so we do not include it inour final model. Finally, if we take out the disadopters from oursample or the ‘zero’-users, the results remain similar. Together, theserobustness checks provide evidence of the stability of our findings.

6.3. Simulations on revenue implications

To assess the economic significance of our main findings pertain-ing to adoption timing, we conduct a simulation in which we shortenthe adoption time of our sample by 3 months where possible. Weassume that in the end, the same number of consumers will adopt thenew service. This results in a simulated usage level and disadoptionbehavior that can be compared to those of the actual sample, in whichthe adoption time remains unchanged. Subsequently, we compute thetotal revenues for the service provider from these simulated usagelevels, taking into account both the fixed revenues and the usage-dependent revenues. The results show that speeding up the adoptionof all adopters by 3 months will decrease service revenues by as muchas 17% in the first 12 usage months. This is mainly due to the fact thatthe usage levels in the first few months will be considerably lowerwhen adoption time is shortened. Although speeding up adoption bydefinition results in 3 more usage months, and in slightly lowerdisadoption rates, this is not enough to outbalance the lower initialusage levels. We must point out that we do not change the adoptiontime of the earliest adopters (i.e., those who adopted within 3 monthsof the service's introduction). In the long run, speeding up adoptionmay still be beneficial. The results of this simulation assume that theinstruments used to speed up adoption time do not affect usagepatterns.

7. Discussion and implications

Below, we summarize and discuss our most important findings.First, the earliest adopters initially use the new service less than

later adopters when they start using the new service. This may seemcounterintuitive, as early adopters should be more innovative and

therefore use more. However, the effects of innovativeness and otherpersonal characteristics are captured by separate explanatory vari-ables, and adopting early is not in itself sufficient to result in highinitial usage levels. Although this study cannot provide conclusiveevidence about the underlying processes that lead to these initialusage levels, our findings suggest that later adopters may have higherexpectations regarding the service and therefore start off with higherusage levels. Alternative explanations for the usage levels we find,such as different price schemes for early and later adopters or networkeffects, can be ruled out based on additional analyses.

Second, we clearly identify some post-adoption usage patterns.Early adopters show stable usage levels over time, whereas lateadopters tend to use less as time goes by. At the aggregate level,monthly usage seems to stabilize at a certain level after some months,in which supports the idea of a learning consumer (Hoch & Deighton,1989; Villas-Boas, 2004). The eventual usage level among earlyadopters remains about the same as their initial usage, which suggeststhat early adopters may have had realistic expectations about theservice, so that their usage is minimally affected by their experienceover time. Later adopters, in contrast, begin with high usage levels butmay find the service less useful than they expected because theirexpectations were too high, which therefore causes them to decreasetheir usage levels. They are also more likely to disadopt. This confirmsthe findings of previous studies (Parthasarathy & Bhattacherjee, 1998)that discontinuance rates are higher among later adopters, eventhough we only consider relatively early adopters. From a customermanagement point of view, our results provide some new insightsinto the customer value of different early adopter groups. Althoughinitial usage levels among the earliest adopters may not be very high,these customers can be very valuable to the service provider, not onlybecause their service usage will increase over time, but also becausetheir probability of disadopting is lower than that of later adopters.Our finding thus supports the proposition by Hogan et al. (2003) thatthe earliest adopters have the highest customer value, whereas lateradopters seem particularly valuable in the short run, when their usagelevels are high. Hogan et al. emphasize nurturing early adopters, aslosing early adopters may have dramatic effects on customer equity.However, our results highlight the importance of later adopters andmanaging them carefully. Later adopters with low initial usage levelshave a very high probability of disadopting early, whereas lateradopters with high initial usage levels will continue to use the service,but their usage levels will go down over time. In both cases, firms insubscription-based services may lose substantial revenues, damagingorganic growth. Service providers therefore should not focus toomuch on acquiring as many adopters as possible for a new service butinstead should attempt to stimulate usage among the group of lateradopters and prevent them from discontinuing the service.

Third, because we separate the direct effects of personal char-acteristics and past purchase behavior on post-adoption usage fromtheir indirect effects through adoption timing, we demonstrate thatthese factors still have an impact on usage levels beyond theirinfluence through adoption timing. In other words, adoption timingalone cannot solely explain the differences in usage levels. Asexpected, heavy users of the category use the new service more,and their disadoption probability is lower. This supports the notionthat experienced users in the category derive more utility from anadditional compatible service (Gatignon & Robertson, 1985; Mahajan,Muller, & Srivastava, 1990). Loyal customers display lower usagelevels but are not likely to disadopt, which suggests some sort ofinertia effect: loyal customers may decrease their usage levels but donot actively discontinue the service, so that on average, lower usagelevels are observed. Overall, we conclude that relationship character-istics can have an important effect on post-adoption usage; this addssignificantly to earlier findings regarding effects on adoption timingand probability alone (Prins & Verhoef, 2007, Steenkamp & Gielens,2003). Finally, we find that younger and male consumers display

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higher usage levels than do older or female adopters, and that femaleadopters have a higher tendency to disadopt. Although the first part ofour model already shows that consumers who have shown theircategory innovativeness by adopting a previous-generation mobileservice tend to have a shorter adoption time, we also observe someadditional effects of innovativeness. We see that, given adoption time,consumers with high domain-specific innovativeness display higherusage levels with regard to the new service. However, they are alsomore likely to disadopt, which points to replacement discontinuanceby this innovative group (Rogers, 2003).

8. Managerial implications

Our results indicate some interesting managerial implications forfirms to use to achieve greater growth from new services with a usagecomponent. The earliest adopters tend to use less in the beginning butincrease their usage over time. Later adopters use more initially butdecrease their usage over time and are also more likely to disadopt.We ran a simulation to study the impact of speeding up adoption time,thereby assuming that the same number of adopters will finally adoptthe new service. Our results show that speeding up adoption byseveral months will significantly decrease the overall service revenuesfrom the first usage year, mainly because of decreased initial usage.Moreover, it seems that the ‘accelerated later adopters’ will notdisplay the increase in usage levels over time that the ‘true’ earlyadopters do. This suggests that shortening adoption time cannot turnlater adopters into more valuable users because their characteristics(e.g., less innovative, less experienced with the category) will stilldetermine their usage levels in the longer run.

Although the simulation results involve several assumptions, theyimply that a service provider should not focus on speeding up theadoption time of later adopters but should rather emphasize retainingthe high usage levels of those later adopters and preventing themfrom disadopting the service. We also show that in order to get morerevenues from the new service, firms should mainly aim to cultivateheavy users of the existing service and loyal customers as adopters ofthe new service, given the higher usage levels and lower disadoptionrates that these groups represent.

9. Research limitations and further research

This study has several limitations. First, our results are based ondata from a single service. Thus, some of our findings may be specificto this particular mobile service or the telecom industry. Futureresearch should be executed in other industries with other newservices and products to confirm the usage patterns we find in thisstudy.

Second, we observe post-adoption usage and disadoption in thefirst 12 months after adoption. Long-term developments in usagelevels could make for interesting further research, particularly if theycan be linked to the long-term profitability of early and late adopters.In addition, we only observe adoptions within the first two years ofintroduction to the market, which implies that all adopters in oursample are relatively quick to adopt the new service. A study thatcontains a full spectrum of adopters (i.e., including the late majorityand laggards) may obtain different results.

Third, we only use transaction data. Attitudinal data and, inparticular, measures of consumer innovativeness and prior expecta-tions could offer more insights into the processes that underlie post-adoption usage patterns (e.g., Shih & Venkatesh, 2004). Furthermore,to consider the potential stimulating effect of marketing communica-tions on new service usage and on disadoption in both the short andthe long run could be an interesting research direction.

Fourth, our model does not take non-adopters into account, whichimplies that the effects of customer characteristics and past purchasebehavior on adoption timing are only valid for adopters of the new

service. A more comprehensive econometric model would includeadoption probability in addition to adoption timing and postadoptionusage over time.

One final future research direction concerns developing a methodof forecasting usage over time. One could extend our model toforecasting actual revenue growth arising from new service introduc-tions by combining our usage and disadoption equations withacquisition patterns for new service adoption (e.g., Bass). We believethat this is the next frontier in marketingmodel-building on adoption,disadoption and usage.

In sum, post-adoption behavior is a very relevant research areathat requires further investigation. This study reported the results of amodel explaining usage and disadoption for only one service coveringa limited time horizon, and it yielded interesting findings concerningthe role of adoption time. However, although we theorized aboutthe effects of adoption time, we do not have clear insights into theunderlying behavioral mechanism driving these results. We hopethat our paper will stimulate many other researchers to study post-adoption behavior, enriching the knowledge base for this important—though often neglected—aspect of adoption.

Acknowledgments

The authors acknowledge the data support from a large Dutchtelecommunications company. They thank editors Don Lehmann andRuss Winer, the Area Editor, and two anonymous reviewers for theirvaluable comments. They also thank Bas Donkers, Rene Segers, andvarious seminar participants for their helpful comments on a previousversion of this article.

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