Web Use and Net Nerds - A Neofunctionalist Analysis of the Impact of Information Technology at Home,...

8/2/2019 Web Use and Net Nerds - A Neofunctionalist Analysis of the Impact of Information Technology at Home, Jonathan G

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Social Forces, University of North Carolina Press

Web Use and Net Nerds: A Neofunctionalist Analysis of the Impact of InformationTechnology in the HomeAuthor(s): Jonathan GershunyReviewed work(s):

Source: Social Forces, Vol. 82, No. 1 (Sep., 2003), pp. 141-168Published by: Oxford University PressStable URL: http://www.jstor.org/stable/3598141 .

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WebUse andNetNerds:ANeofunctionalistAnalysisof the Impactof InformationTechnologyn the Home*JONATHAN ERSHUNY,University of Essex

AbstractThis articleinvestigateshe impact of use of the WorldwideWebon patternsofsociability.tssetsout a neofunctionalistodelof socio-technologicalnnovation hatis designedto exploreprospectivelyhe impact of innovations n areassuch asinformationand communicationstechnology, n thefull range of sociableandnonsociableactivities. It uses evidence rom a unique data set (a nationallyrepresentativeimediarypanelstudy, ollectedn the U.K.ortheperiod1999-2001)toexplorehismodel. tconcludeshatInternet se,contraryo"time-displacement"expectations,s notnegatively ssociatedwithsociability.

A Neofunctionalist View of ConsumptionNORMANNIE'S NET NERDSWhat follows was stimulated by a striking finding reported by a group of socialscientists in the U.S. (Nie & Erbring 2000). On the basis of U.S. cross-sectionalsurvey evidence, they concluded that those who use the Internet have less socialcontact than others and implied that the use of the net turns us into Web nerds(nerd s an Englishterm of artpreviouslyused to describe 1960sopticalastronomersduring their night-time observationphases,collectors of locomotive numbers, andother solitaryand technologically oriented obsessive personalities).The present article contends that this finding is methodologically flawed,both because of the questionnaire-based design of the cross-sectional time-use*Thesupportof the U.K.Economicand Social ResearchCouncil(ESRC),BritishTelecom,andtheUniversity fEssex sgratefullyacknowledged.amgratefulfordiscussionswith BenAnderson,MalcolmBrynin,EileenClucas,and RobertKrauton the substanceof whatfollows,andfor thecontributionsof two referees.DirectcorrespondenceoJonathanGershuny,Universityof Essex.E-mail:[email protected].? TheUniversity f NorthCarolinaPress SocialForces, eptember 003,82(1):141-168


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142/ Social Forces82:1,September2003measurementand becauselongitudinal panel surveymaterialsare necessaryto draw any such implication; it argues for a neofunctionalist theoreticalperspectivewithin which the impact of such new technologies may morehelpfully be considered; and it illustrates these propositions through theanalysisof a new U.K.panel studyof time diarists.TECHNOLOGIESS CHAINS OF PROVISION OR SERVICESThere are a numberof recent reviewsof researchon the social impactof theInternet interested eadersaredirectedparticularlyo DiMaggioet al. 2001andWellman t al.2001); he theoretical iscussion hat followsrelatesparticularlyothecurrentlyontested ssue of thetime-displacementesultsof the Internet.Theissueconcerns he role of technologicalhange n deliveringinalservices thoseultimateconsumptionexperienceshat are the realpurposeandendpointof alleconomicactivity.Therearemanyalternative hainsor sequencesof activity hatmightleadinprinciple o thesame or a similar inalservice.Awomanmight plantand harvesther ownwheat,millit herself,bake,slicethebread,butterher ownsandwich, ndeat t. Orshemightenteracafe,eata sandwich ndpay or t. Thereare undamentaldifferencesn the sensations hemight experienceas a resultof these two chainsof activity.Butit would alsosurelybe perverse o denythatthere are alsosomeimportant imilarities, degreeof functionalequivalence.Rather imilar ortsofwantsaresatisfied yquitedifferentequences fvarious ortsofpaidandunpaidworkandconsumption by, n the broadsociologicalenseof theword,differenttechnologies.Thefullydevelopedheoreticalperspectiveromwhich thisexample s drawnis too elaborate o spellout in detailhere(seeinsteadGershuny 000,chapters2and 8). But a simplelistingof some of its keydefinitionsprovidesa sufficientintroduction or the limitedpurposesof this article. tdistinguishes arious inalservice unctions,whichmightbespecified ither n averygeneralway(e.g.,basicwants/luxury ants/other)r else namoredetailedmanner e.g.,nutritionshelter/educationmedicinespectator port) n suchawaythatallthepaidwork,unpaidworkandconsumptionactivityn thesocietycan berelated o one orotherof thecategories. hedifferent etsofactivityhatrelate o one oranother erviceunctionconstitute the chains of provision for that categoryof want. Technologicalinnovations allowthe developmentof new sets of activitiesthat go to satisfyparticularwants innovationsn modes of provision.Thus, to choose a pertinent example, in the 1950s,people progressivelyreduced time devoted to trips to the cinema to watch films but boughttelevisionsand producedthe final entertainment ervicethemselvesat home;thus a changein the balancebetweendifferentmodes of provisionfor passiveentertainment - a change (to adopt a term from transport studies) in the


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144/ Social Forces82:1,September2003economies (France,Germany,Sweden)havecontinuedsuccessfully o reducework hours throughoutthe twentiethcentury.Unpaidwork time may be substitutedfor paid service work (or vice versa).Mid-twentieth-century economic growth was fueled by technologiesencouraginga major transferof time of this sort, which might be thoughtof as the self-servicerevolution(e.g., using domesticwashingmachines forlaundryservices,drivingprivatecarsinsteadof purchasing ransport ervices,use of supermarkets ersus fully servicedshops;see Gershuny1978, 1984).

Of course, closely related to these self-servicing examples, new technologies leadto displacementof consumption time. This is the substitution of new consumptionactivities or old - precisely he DiMaggioand colleagues' elevision-versus-cinemaexample.It is not unlikely that the impact of home-based computing technologies, incombination with increasingly broad bandwidth switched telecommunicationsinfrastructure,will have a scale of impact - though with quite distinct impactsontime allocation- similarto the mid-twentieth-century self-servicinginnovations.New consumer information systems, systems for purchase and delivery of goodsand services, new systems for provision of advice and training, have alreadyemerged as net-based commodities, and there areopportunities for much more ofthis sort of change. These various sorts of innovation imply a number ofpossibilities. The time-displacement case - with Internet time, considered as aleisure or final consumption activity, displacing, for example, social contact ortelevision - is in fact just one among a considerably wider group of suchphenomena.Let us considerjust two furthercases,of more complex chains of consequences,which are perhaps more generally representative of the effects of a multipurposeconsumer technology (or it may be more appropriate to consider it as atechnological infrastructure) such as the Internet.The final service function may be subject to inelastic demand, in the sensethat its end could be achieved more efficiently n time asa result of the technologicalchange, and the resulting time savings could then be devoted to satisfyingotherwants. This corresponds to the historical case of the declining social time devotedto food production. For example, home Internet time may take on a new role inan existing chain of intermediate production activities such as shopping. Homeshopping using the Internet could lead to a substantial reduction in time spentshopping away from home (and related travel) by consumers, while in turn(1) generating new paid employment both directly in software andtelecommunications industries, and indirectly in construction and homedelivery services; and (2) freeing time that consumers could use to satisfywantsfor other forms of consumption, which might not directly involve the Internetat all (and might well generate yet more new paid employment).


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WebUse andNet Nerds 145The final service unctionmaybe subject o elasticdemand:Thetechnologymight mproveheefficiency ndeffectivenessi.e.,either he volumeor thequalityof result)of timedevoted o the satisfaction f aparticularlassofwant,to sucha

degree hat consumerswantmoreof it. Tousetheoldlanguageof production orthe chain of provisionas a whole,in sucha case,growth n outputof theservicefunction sgreaterhanproductivityrowthand eads o moreconsumptionime;thisis thehistorical aseof the effectof domesticradioandtelevision echnologieson time spentin passivehome leisureconsumption.We shallreturn o considerthiscasein relation o the Internetafter heempiricaldiscussion.It shouldbe admitted n advanceof theempiricalanalysishattheimpactsofWeb-relatedctivity hatwe canidentifyare,asyet, verysmall.Weareatpresent,to continue heanalogywiththemid-twentieth-centuryelf-servicing hanges,nsomething ikethe late 1930s of the entertainment evolution n relation o theputativenformationechnologyrevolution.But we now havethatanalogy, s wedid not in 1937.We can, even at this earlystage,both considerappropriatemethodologiesor theinvestigation f thisprospective ew waveof technologicalchangeandstudy he evidenceof itsveryfirstmanifestations. ndwhatever hesemanifestations re,we can be certain hattheywillbe complex, nvolve variousdifferent ortsof changes hroughouthechainsof provision or variousdifferentwants,andin general nvolvea mixtureof changes n paidwork,unpaidwork,andconsumptionactivities,not a simpletransferbetweenpairsof consumptionactivities.Onthebasisof theargumentset outhere,we wouldcertainly xpect ofind somethingmore thanjust the straightforwardransfer rom out-of-homesociable activity to Web-basedhome computing implied by Nie's net nerdmodel.

Questionnaires,Diaries, and Time-Use EstimatesThe argumentsdeployedhereconcern the effects in a sense to be discussedin a moment- of the diffusionof aparticularechnologyon time-usepatterns.So we require,at thispoint,a briefpreliminarydiscussionon the measurementof time use. Thereare two distinct methodologies:stylizedestimates(directquestions about amounts of time devoted to particularactivities over givenperiods)and time-use diaries.The first of these methods, perhapssurprisingin the light of the comments that follow, is verywidely used;virtuallyeveryindustrializedeconomy has an annualor continuouslabor force surveythatincludesa questionor seriesof questionsof thegeneral orm:"howmanyhoursdid you worklast week/month?"It has been demonstratedrepeatedly (e.g., Hoffman 1981;Niemi 1983;Robinson & Godbey 1997) that the estimates resulting from this sort ofinstrumentare systematicallybiased, since, while most employedpeople areawareof their contractualhours of work, their actual hours of work very


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146/ SocialForces82:1,September2003frequentlydifferfrom them. (Therecentsuggestion, n Jacobs1998,that thesefindings reflect the phenomenon of "regression owardthe mean" seems tomistake wo balancingsourcesof systematicerror men working ong hoursoverestimating because of unnoticed work-hours interruptions, womenworkingshorthoursunderestimating ecause of employment-regime-inducedguilt- for a randomerrorprocess.)In fact,unlesstheyhave some particularreasonfor knowingtheir work hours (e.g., employeesclockingin and out, orthe self-employedbillingclients for workactuallydone), peoplesimplydo notknow the answerto this question.And if not paidworktime, how much lesslikelyis it that one might know the weeklytime devotedto other activities?The reasonsfor an a priorirejectionof the stylizedestimateapproach otime-use measurement nclude the following:* we do not in generalmaintainin our conscious mind a continuouscumulativeountof timerecentlydevoted o particularctivities;

* f wedid,we wouldhaveno reasonn generalither o chooseanyparticularperiodof cumulationday/week/month)r to useanyparticularet of time-usecategories;* which means in turn that it is unlikely that respondents share thequestionnaireesigners' articularoncepts e.g.,doesshoppingime ncludevisitsto theoculist,but not to thedoctor?)

All in all, since people cannot be expectedto have knowledgeof the elapsedtime they have recentlydevoted to variousactivities,it seems inappropriateto base our measurementof the use of time on a techniquethat presupposessuch knowledge.Questionnairerespondentswill in generalanswerestimatequestions, since giving answers to sensible-sounding questions is whatrespondentsdo. But in factif theyare to construct heseanswers,respondentshave to go through, repeatedly,casual and inexplicit versions of the diarymethodology tself,summarizingheir recenttime use in their own minds,andthen totaling, probably naccurately, lapsed periods in the targetactivity, nthe real time of the interview(and probably n the presenceof an impatientinterviewerpaid on a completed-interview-based iece rate).Thediary-basedmethodology,which involves heestablishment f a randomsampleof recordsof sequencesof recentactivities(ideally n the respondents'own words) together with estimates of the clock time of the start of eachactivity,seems altogethermore reasonable. f the preliminaryexplanationofthe studyis reasonablyneutralwith respectto the researchers' hosen topic,respondentshave no reason to wish to mislead them, and there is not thetendency to overcount activities;calculations of daily time allocationswillautomaticallysum to 24 hours per day and so on. A narrativeaccount of asequence of recent events, by contrast to the stylized estimate, is a naturalcategoryof self-knowledge,and in fact the skill of constructing sequential


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WebUse andNetNerds 147narrativesis a frequent outcome of early-school-yearssocialization in therespondents'householdof origin ("Whatdid you do at school today?").This observations, in fact,prettymuch all there is to the firstobjectiontothe Nie and Erbringargument.Respondentsto an instrument focusing onInternetuse arealways ikelyto exaggeratehe extentof Internetuse in stylizedestimate questions, and also, perhaps as a result, to reduce their parallelestimatesof time devotedto other activities.Moregenerally, heydo not reallyknow the answers to the questions. Time-use patterns,in short, should beestablishedby diary techniques.However,here s a seriousproblemwiththediaryapproach.Diariesareveryonerousto complete.As a result,they haverelativelyow responserates. Thisproblem s, aswe shallsee,doubledand then redoubledbythe successivewavesof the panel design in the present study. The arguments for a panel orrepeated-measures pproachto this problemare set out in the next section;they lead us to adopt a three-times-repeateddiarycollection from the sameindividualsovera three-yearperiod.The strategyn factproducestwo distinctsortsof responseburden.Onerelates o thedesignof thediary nstrument tself.Thepointof thepanelapproachs to measurechange n time use.But,forany person,time allocationvarieswidely romday oday.Arepeated-measuresomparisonf single-day iarieswould be likely o reflectmoreintrapersonalariation hangenuinechange.As aconsequencef the observationhatmostintrapersonalariationscapturedwithinaweeklycycle, t was decided o collectsevenconsecutivedaysof diaryaccounts,considerablymore onerous hatthe standardime-diarynstrumentthoughusinga specially implifieddiaryformat,with precodedactivities,recordingmultiplesimultaneous ctivitiesbutwithno "whowith"record).The otherrelateso therepeatedmeasurement.naddition o thehigher-than-normalnonresponse o the diary nstrument tself,there is alsothe problemofattrition n successivewavesof datacollection.Ofcourse,sincewe knowa greatdeal aboutthe identityand characteristicsincluding specifically he time-usecharacteristics)f thediaryattritors,weareable o makeuse of the standard anelnonresponseweighting echniques o compensate or thisproblem.Butthe twoproblems ogethermean thatwe must devoterathermorepreliminary pace nthis accountto the discussionof the problemof systematicnonresponse hanwould normallybe the case.

The DataWhat follows is based on the Home-on-Line (HoL) time-diary panel study(Lacohee&Anderson2001;a discussionof resultsfrom the firsttwo wavesofthestudywillbe foundin Gershuny 002)basedatInstitute or SocialResearch,


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148 SocialForces82:1,September003Universityof Essex.The HoL studyhad an initialrandomprobability ampleof 1000 U.K. households, with an oversampleso as to provide 50% of theachievedhouseholdsamplewith home-computeraccess(the U.K.averagewas32%at the time of the first wavein 1999:Tayloret al. 2000).All adult (16+)members of household were interviewed. Two further annual waves wereundertakenn theearlyspringof 2000 and 2001.Wave1consistedof computer-aided personal interviews (CAPI), while waves 2 and 3 involved an initialtelephoneinterview CATI).Eachwave consistedof a multistage nvestigation,consistingof aninitialhousehold nterviewwith a randomly electedhouseholdinformant, ollowedbyindividual nterviewswith all adulthouseholdmembers.A seven-dayself-completion ime-usediarywas left behind for all adultsandone for childrenover 10 usinga slightlysimplifieddesign(resultsof whicharenot discussed urtherhere),with a requestto completethe diaryat least onceper day and then mail it back at the end of the designatedweek. Rules forinclusionin the subsequentwaves follow those of the BritishHousehold PanelSurvey (similar to the U.S. Panel Study of Income Dynamics);broadly allmembers of wave 1 respondenthouseholds,plus all currentcoresidents,areinterviewedn subsequentwaves.Therehave been previousdiarypanelstudies(Harvey& Elliott1983;Juster1985;Krautet al. 1998 discuss the specificissueof the impactof the Interneton sociability);he presentstudyis, however, hefirstnationallyrepresentative iary panel using the sort of long diarysuitable(since it reduces currentintrapersonalvariability n time use) for exploringchangesin time use at the individual evel.Theheavyrespondentburden mpliedbythedesign s, however,notwithoutits costs. The wave 1 questionnaire esponseratewasa barelyrespectable 7%.Only66%of thewave1respondentsompletedhe wave2 questionnaire;o doubttheextremely nerousrequiremento completea full andcontinuousseven-daydiary ontributedo thepoorresult.Because f thehighattrition atea fresh amplewas drawn n wave2, and similarresponserates and attritionfrom wave2 towave 3 resulted: or this reasononly pooled two-waveanalyses i.e., wave 1 towave2 transitionspooledwith wave 2 to wave3, togetherwith somewave 1 towave 3 transitions or the wave2 attritorswho rejoined he samplein wave3)areex,anined n thisarticle.Longitudinalweightsto compensate or differentialnonresponseandattli+;^chavebeen calculated ut arenot usedin whatfollows(on the groundsthat they are inappropriateo the regressionanalyses).Justover62%of wave 1 questionnaire espondents uccessfully ompleteddiaries,whencea major ssueof concern s thepossibility f systematic ias n thediary sample.The next section thereforesets out an approach o establishingwhether the diary sample is in fact biased.


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Web Use and Net Nerds/ 149TABLE 1: Nondiarist versus Diarist Leisure Participation FrequencyEstimates

1999 2000Probabilityof participation

perweek Nondiarist Diarist Nondiarist DiaristPlay sport/takewalk .70 .71 .70 .71Watchsport .17 .15 .17 .15Go to cinema .17 .14* .17 .14**Eat out .50 .47 .50 .47**

p


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Web Use and Net Nerds / 151TABLE 3: Nondiarist versus Diarist Domestic Task Division (domdiv) Index

1999 2000Nondiarist Diarist Nondiarist DiaristBoth .73 .77* .67 .70Men'saccounts .72 .75 .60 .65*Women'saccounts .74 .79* .73 .74*p


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152/ Social Forces82:1,September2003TABLE4: Stylized Estimates of Three Sorts of Work in Hours per Week

1999 2000Nondiarist Diarist Nondiarist Diarist

Housework 11.2 11.5 11.9 11.7Do ityourself 4.2 4.4 4.7 5.4Paidwork(participantsonly) 37.9 36.7 37.5 36.0All resultsarenonsignificant.

able to put together quite a long-term time-use data series for the U.K. TheBBC Audience Research Department (which conducted time budget surveysas far back as the 1930s) has surviving time-diary microdata going back to theearly 1960s. From the early 1980s, the Economic and Social Research Councilhas been supporting national time-use surveys. The 1999/2000 comes from theBT-funded study described above.So we are able to give quite a long-term picture of change in national activitypatterns stretching back from the present to the early 1960s. Table5 covers U.K.adults aged 25 to 65; a more detailed tabulation of these U.K. materials may befound in Gershuny 2002). We see something of a decline in personal care timeover the period; though sleep has remained constant at around 64 hours per weekfor men, 65 for women, nonsocial eating at home has declined markedly andregularly rom around 10hours perweek for men, 12 for women, at the beginningof the period to 6 hours for men, 7 for women in 2000. A largepart of the growthin out-of-home leisure, however, consists of eating and drinking in the moresociable contexts of pubs and restaurants,which increased from less than 1 hourper week in 1961 to around 4 hours in 2000. Men's unpaid work has increasedslightly over the period, while women's has been halved, partly as a consequenceof technological changes within the household, partly because of aredistribution between men and women associated with the increase inwomen's paid employment. (A discussion of the processes of change in unpaidwork time is found in Gershuny 2000:180-202). Men's paid work time hasreduced substantially over these four decades (though this has also beenaccompanied by a change in the distribution of paid work; while higher-socioeconomic-status men worked markedly shorter hours than lower-statusmen in 1961, by 2000, those of higher status worked somewhat longer hours.The increase in women's work time reflects the increase in women'sparticipation in the labor force, combined with the proportional growth ofpart-time paid work for women.For the purposes of the current article we should particularly note therelatively low level of time devoted to home computing overall - hardly 2hours per week for working-age men, around 1 hour per week for working-age women. There is also a very strong social class gradient in the amount of


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WebUse andNet Nerds 153TABLE : HistoricalChange n TimeUse

U.K.Men25-65 U.K.Women 5-651961 1975 1987 1999-00 1961 1975 1987 1999-00HoursperweekSleep,oilet,eating 74 74 73 69 76 76 75 72Allunpaidwork 11 10 17 18 42 34 28 29Paidwork 47 38 34 34 15 15 21 22Travel 3 7 9 7 2 5 8 6Out-of-homeeisure 5 9 7 8 3 6 5 7

Radio,TV, tc. 16 17 17 19 14 15 14 16Other ome eisure 11 12 10 10 15 17 13 13Other 1 1 2 1 2 1 4 2Homecomputing 0 0 0 2 0 0 0 1Total 168 168 169 168 169 169 168 168

time used for this purpose: Men in higher professional and managerialoccupations spend 4 hours per week in computer-related activities andsimilarlyplacedwomen, 3 hoursperweek.Thosein manualoccupationsspendhardly1hourperweek in theseactivities.Thedigitaldivide in home-computeraccess is mirroredby the digital divide in computeruse. The time-use dataput the questionnaire-basedstudies of the effects of the Internet into anappropriateperspective. Plainly, if there are any observable effects of theInternet, hese aregoing to be small ones.CROSS-SECTIONALIFFERENCESETWEEN SERSAND NONUSERSWe cannowturnto thestraightforwardomparisonsof time dataforWebusersand nonusers.Thebriefsummary f the results s that there s littlesignof theNieand Erbring indingin the U.K.time-diarydata.(Similarnonresultshave beenreportedboth fromU.S.questionnaire vidence(Wellman t al. 2001 and fromtime-diarymaterials:Kestenbaum t al. 2002).Table6 (usingunweighted ata,as in all thefollowing ables)shows he meansfora comprehensiveet of 14categories f time use (togetherwitha 15thcatchallother/missing ategory:This table ncludes ustthose"gooddiarists"with atleast23 hours/dayallocated o one of the substantiveactivitycategories).The tableshows,first,despitethe samplingvicissitudesdescribedearlier,and despitetherelatively mall numberof cases,quite a considerable tability n estimatesofthe sample'sbehaviorover the threesuccessiveyears.We see - as indeedwewould expect over such a short time - very little evidence of change inanything.The tableprovidesus with a firsthint of comparisonbetweenWeb usersandnonusers.It looks as if Webusershaverathermorepaidworkand lesssleep


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154/ SocialForces82:1,September2003TABLE 6: Time in Various Activities: "Good" Diarists Only

AllRespondents Non-WebUsers WebUsers1999 2000 2001 1999 2000 2001 1999 2000 2001Minutesper dayPaidwork,full-timeeducation,and associated ravel 229 225 227Unpaidwork,shoppingetc.,andassociated ravel 208 212 208Sleep, personalcare,

meals athome 619 617 624Social ife,going out,andassociated ravel 52 52 49Visitsto friends'houses,

beingvisited 55 52 51Playingsports,walks,outings 24 29 24Telephone alls 9 10 10Hobbies,games,etc. 12 11 12Radio,TV,video, etc. 159 153 159Readingnewspapers,books,magazinesDoing nothing,otherComputergames,etc.E-mail,browsingthe WebStudy,paidwork, other,on thecomputerMissingTotalminutes

27 29 2920 20 235 4 33 5 7

11 12 99 8 6

1,442 1,439 1,441

226 220 212210 221 218

622 620 63350 50 4656 53 5325 29 248 9 9

12 11 12163 158 16727 31 2920 21 254 3 20 0 08 6 49 8 6

1,440 1,440 1,440

251 243 267194 183 180

603 607 60062 56 5642 50 4519 30 2511 11 129 12 14

132 133 13827 26 2721 17 1612 10 722 23 2628 32 218 7 6

1,441 1,4401,440938 673 683 822 520 499 116 153 184

than nonusers; more going out but less visiting friends in their own houses, lesstelevision watching. But for this sort of comparison, and given the short-termintertemporal stabilityof the estimates, we would do better to turn to the pooleddata for all three years as in Table 7.Table 7 illustrates, in quite the most straightforwardof ways, the most basic- and as it will turn out, the most important - of the objections to the simplecomparison of Web users and nonusers: that users and nonusers differ also withrespectto othervariables.We can see thatmanyof the generalcategoriesof apparentdifference from Table 6 emerge as significant differences (by t-test). Web users doindeed have substantially, significantly, more paid work and less unpaid, forexample. But immediately we consider the means separately by sex, these

N


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WebUse andNet Nerds / 155TABLE7: Web Users and Nonusers: Time in Various Activities (PooledData, 1999-2001)

All All Men WomenMinutesper day Nonuser User Nonuser User Nonuser UserPaidwork,full-time

education,andassociated ravel 227 220 255** 258 294* 192 200Unpaidwork,shopping,etc.,and associated ravel 209 215 185** 167 151 251 232Sleep, personalcare,

meals at home 620 624 603** 611 587** 634 626Social ife,going out,andassociated ravel 51 49 58** 56 56 44 60**Visitsto friends'houses,

beingvisited 53 54 46* 46 43 61 50*Playingsports,walks,outings 26 26 25 30 26 22 24Telephone alls 9 9 11** 6 9** 11 15*"Hobbies,games,etc. 12 12 12 10 11 13 13Radio,TV,video, etc. 157 163 135** 178 144** 151 121**Readingnewspapers,books,magazines 28 29 27 32 24** 26 30Doing nothing,other 21 22 18 23 18 20 17Computergames,etc. 4 3 9** 5 13** 1 4**E-mail,browsingtheWeb 5 0 24** 0 28** 0 18**Study,paidwork, other,on thecomputer 10 6 26** 9 31** 4 20**Missing 8 8 7* 7 6 9 8Totalminutes 1,440 1,440 1,441 1,438 1,441 1,439 1,438N 2,294 1,841 453 791 258 1038 190*p


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156/ Social Forces82:1,September2003TABLE8: A Cross-SectionalTime-UseModel (Pooled Data, 1999-2001)Minutes erday Personal Goingout Visits Sports,Etc. PhoneAge -3.15** -.81 -.03 .94** -.21*Age2 .04** .00 -.01 -.01** .00*Woman 21.76** -3.80 11.60** -8.02** 5.12**Wave 1.01 2.24 -.61 4.83* 1.23Wave 1.76 -.41 -1.10 1.36 .38Contractedime -.25** -.08** -.14** -.07** -.01**Committedime -.16** -.15** -.13** -.06** -.01**Computer ames -.30* -.21 * -.09 -.07 -.01Otherhomecomputer -.10* -.10* -.14** -.06* -.01Internet -.31* .16* -.22* .02 .06*Constant 760.14* 136.56** 122.10* 41.36* 14.61*AdjustedR2 .35 .13 .12 .07 .05use. The two time-use categories most closely connected with the net nerdhypothesis - going out and visits - show something of this sort: relativelysmall differences for the men, but really quite substantial differences betweenwomen users and nonusers, with women users apparently going out a lot morebut visiting friends' houses a lot less - which might in turn suggest that thefemale users and nonusers are in some sense different sorts of women.

This is clearly not the right way to go about the analysis. Three distinctproblems are emerging:

* Theremay some other "thirdvariables,"measuredin the surveybut not yetincluded in the analysis,causallypriorto both net use and socialactivity, hatconfound our view of the connection between them.* Some of these "third variables"(e.g., employment status) have very strong

connectionswithparticular ategoriesof time use (paidwork),andso the effectof the variablemight be either direct (i.e., employed people are the sort whodo less visiting) or a result of time-use crowding (employed people have lessfree time for visiting).* Some of the remainingdifferencesbetween users and nonusers may still notbe consequencesof the net use but relate o otherinterpersonaldifferences hathave not been measuredand, indeed, might in principlenot be measurableatall (unobservedheterogeneity).

We can deal with the first two of these problems by adopting a rather moreformal regression modeling strategy. And the third problem has astraightforward solution in the panel analysis to which we turn in the nextsection.

There are three categories of right-side variable in the regression modelspresented in Table 8. First, there are the straightforward sorts of categorical


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Web Use andNet Nerds/ 157TABLE8: A Cross-Sectional Time-Use Model (Pooled Data, 1999- 2001) (Cont'd)Minutesper day Hobbies TV,Etc. Reading Nothing Missing TotalAge .06 1.59** .11 1.34** .15* .00Age2 .00 -.01* .01** -.02** .00 .00Woman 1.96 -25.07** -3.73* -2.14 2.33** .00Wave2 -1.26 -6.93 .06 1.27 -1.85** .00Wave3 -.40 -1.21 -2.27 5.68 -3.80** .00Contracted ime -.02** -.25** -.05** -.13** -.01** -1.00Committedtime -.02** -.27** -.05** -.14** .00 -1.00Computergames -.04 -.11 -.01 -.16 -.01 -1.00Otherhome computer -.03 -.45** -.02 -.10* .01 -1.00Internet .02 -.53** -.07 -.12 -.01 -1.00Constant 16.03** 247.00** 32.67** 64.56* 4.96** 1440.00AdjustedR2 .04 .29 .26 .08 .06*p < .05 **p < .005

and other classificatorynformation.Secondarethose time-useelements thatmight be expectedto be closelyassociatedwith causallypriorthirdvariables.In the models, I have used paid work and unpaid work ("contractedandcommittedactivities"n the conventional ime-diaryanalysis erms:Aas 1978)asright-sidepredictorvariableswheretheycan bothact asproxies orcategoriesof employment status and household responsibilityand provide appropriatecross-sectional lasticityestimates so as to deal with the crowdingproblem.Third, there are the predictorvariablesthat are of direct interest to ouranalysis,connectedwith Web use. Web use is itself a time-usecategory, o weenterit as a scalarquantityrather han as a classificatoryategory.And highlycorrelatedwith Internet use are the two other sorts of home-computeruse(gameplayingand use of the personalcomputerfor work or studypurposes),which are also enteredas scalarquantities.It would of course be possibleatthis point to do somethinga little more econometrically ophisticated n thewayof causalmodelingto disentangle he effects of the Webuse from that ofthe other scalarvariableshere;I will make the reason for not doing so fullyexplicit in the next section. But for the moment we will simply estimateequation1 for each of the time-usecategoriesnot mentioned on the rightsideof the regression.

Time use= f(age, agesquared, ex,dateof survey) (1)+ f (time in contracted,ommitted, ames,othercomputer,nternet)

This producesa table of results with three pleasingcharacteristics:*The effects of the categorical and other classificatorycharacteristicssum,across the full set of time-use variablesnot included on the right side of


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158/ Social Forces82:1,September2003equation 1, to exactlyzero, since,whatever he characteristics age, femalegender,yearof measurement there arealwaysexactly1,440minutes in theday;more time spent by a personwith any givencharacteristicn one activitymust therefore be exactlycompensatedfor by less time spent in the otheractivities.

*The effectsof the right-sidevariablesmust sum, forjust the samereasons,to-1 (sincethe regressioncoefficients ellus, for each left-sidetime-usecategoryin turn, the effect of spending one extra unit of time on the rhs variable).*And the interceptssum to exactlyone day'sworth of the time units (since

theyrepresent he conditionwhereall the right-sidevariablesareset to zero).Thus Table8 gives us the evidence we require:the effect of spending time on theInternet,controllingappropriately or the effectsof all the other measured relevantvariables. It appears, on this basis, that each extra minute on the Internet isassociated with about one-third of a minute reduction in personal caretime, one-fifth of a minute less visiting,half a minute less watching television - but, to picka result that does not apparently accord well with the net nerd hypothesis, nearlyone-fifth of a minute of extra time devoted to going out - eating or drinking ina public place, going to the theater or cinema. Not necessarily what we wouldinitially associatewith our nerds.It should immediatelybe said that even this formallyspecifiedregressionmodelis still not the correct way to consider the general problem. In fact, the elasticitieswe areestimatinghere are not reallyelasticities n the sense of changesconsequentialon the variationof the right-sidescalarvariables.Allwe have so far arein factcross-sectional differences;we are in effect attemptingto simulate changeby comparingpeople who haveat some point changed.Wehave,forthe moment, differentpeople,perhaps people who differ in ways that are not yet included in the model, perhapseven differing in ways that are not measured in the survey instrument. There isreallyonly one wayto see effects of change: o takerepeatedmeasuresof thebehaviorpatterns of the same individuals. We can ultimately identify change only bymeasuring changes. We need, to get at Nie's net nerds, the sort of naturalexperiment provided by the diary panel, which looks at the consequences ofpeople changing their net use.But before we turn to the evidence from our natural experiment, there area couple of other things we might note from Table 8. The first concerns thecoefficients for the dummy variables indicating the year of the survey. Theseare insignificant for all the time-use categories with the exception of the finalmissing data category. In this last category the coefficients are increasinglynegative with each of the two successive years of measurement. The numbersare small, -2 minutes in the second year of the panel, -4 minutes in the thirdyear, but in both cases clearly significant. We have here a combination of


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Web Use andNet Nerds/ 159TABLE9: Time-Use Means by Internet Change Status

On-lineUseNeitherTime User at Timep, New User Old UserNot q

P q P q P q P qPaidworkTravelUnpaidwork

(including voluntary)SleepandpersonalcareEatingat homeStudy,coursesSocial ife,"goingout"Visits to friends'

houses,beingvisitedPlaying ports,walks,outingsTelephone allsHobbies, games,etc.Radio,TV,video, etc.Readingnewspapers,books,magazinesDoing nothing,otherComputergames,etc.E-mail,browsing he WebStudy,paidwork,other,on thecomputernot knownN

16446

20656067

1434

170 17347 55

20756471**

1235

19356858

1640

18348

216549602436

19751

178546603930

192 187 19360* 71 80

170 167 171549 531 53457 59 6227 16 1739* 38 39

54 51 49 52 44 47 46 3825

913

1673117205

27622

249

131693016205

15

221013

14629126

15102642

281113

1532710

10**

823

251018

1572719100

1417

116

22 26 2611 9 1113 15 12

142* 133 12929 29 2416 24 2110 14 7*18"* 21 29*20 39 3319 15 13

79Notes:Paired -testsignificance*p


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160 / Social Forces82:1,September2003coefficient indicates that something is really going on, that there is some sortof complementarity between the independent and the dependent variable.More of this in a moment.LONGITUDINALONSEQUENCESF NET USENow we can start to use the diary panel study as a panel. In this section wepool year-on-year changes, putting together pairs of years, so that we considertogether the pair of years 1999 and 2000, and 2000 and 2001 (and also justthose cases where individuals kept diaries in 1999 and 2001 but not 2000),referring to the earlier year as p and the later as q. We can first makestraightforwardcomparisons of change in time-use patterns for changers andnonchangers, in the manner of the natural experiment mentioned previously.There are in fact four possible cases: an Internet user in neither year, a user inyear p who stopped using the Internet in year q, a new user in year q, and anold user who makes use of the Internet in both years.Of these four groups, clearlythat relevant to the question of the impact of theInternet on styles of life is the third group, the new users, whose time use beforeand afterthe startof their net-using career is given by the third pairof columns inTable9. This for the first time allows us to look genuinely at what happens whenpeople start to use the net. What emerges from this natural experiment issubstantially(and in some cases significantly)contraryto the Nie net nerd model.In the 116cases of new users,we notice firstthat (ascomparedwith for examplethe user/nonuser columns of the cross-sectional Table 7) not much changes -since, unlike the earliertable, these areactuallythe same people at successive timepoints. Thereis significantlymore traveltime, for a reason not yet apparent.Thereis significantlymore going out and less television watching; study time at home isreduced by 12 minutes per day (though this change is not significant) andstudy time on the computer increases by 6 minutes (again not significant).These are not big changes, but at least we are now genuinely looking at change.And they are not unambiguously in the direction of the reclusive, screen-fixated loser of all social contact. On the contrary: starting to use the Internetseems if anything to be associated with a small increase in social life. This isnot unexpected, given the implications of the neofunctionalist analysis alludedto earlier: just like the car and the telephone, the Internet is not in itselfnecessarily just an object of final consumption but may used as part of newchain of provision - it may complement other sorts of time use. Which bringsus back, finally, to the line of modeling started in the previous section.A more formal and general analytic approach models change from year pto year q, using essentially the same regression models as for the pooled cross-section data. So equation 1 becomes the pair of equations:


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Web Use andNet Nerds / 161Time use t = f(age, age2,sex, surveydate at tp) (la)+ f(time at contracted, ommitted,games,othercomputer, nternetat tp)Time use tq= f(age, age2,sex, surveydate at tq) (Ib)+ f (time at contracted, ommitted,games,othercomputer,nternetat tq)

Now, our research question concerns change in the left-side time-use variables,tq minus tp. And we have to estimate a new regression equation derived bysubtracting equation la from equation lb.Some of the right-side variables estimated in the original equation 1 aretime-invariant. Sex, for example, does not change between year p and year q;when we subtract la from lb constants disappear altogether. Others (e.g., age)advance by exactly the same amount for all cases between the waves of datacollection, so subtraction produces a new constant that must be dropped fromthe regression equation. Alone among the nontime-use variables in theestimation of equation 1, the age-squared term does not drop out as a resultof the subtraction. This is, potentially at least, substantively meaningful insofaras (for example) older and younger people might have different dynamics.However, we find that in fact the effect of the (age2 t - age2 t ) variable is notsignificant and has little effect on the other coefficients (and it does slightlycomplicate the interpretation of the model), and so the following analyses dropthe variable, and we find ourselves estimating the straightforward equation 2:

Time-usechangetq - tp= f(contracted ime at tq- contracted ime at t?,committed ime at tq- committed ime at tp,computergamestime at tq- computer ames time at tp, (2)othercomputer ime at tq- othercomputer ime at tp,Internettime at tq- Internettime at t ),which has only time-use variables on both sides. This is genuinely an elasticityequation; it is estimated from panel data, with repeated measurements of thesame respondents, and shows us directly the effect of changes in time use, ontime use!

Now, if, over some period, we spend more time in one activity, we mustnecessarily spend less time in some other activity - a simple matter of timedisplacement. So, by default, we would expect that all the regression coefficientsshould be negative. And if we do not find negative coefficients relating one ofthe right-side variablesto a particular left-side variable, then we may be entitledto conclude that there is some kind of complementarity between thosevariables.

Consider Table 10. My expectation, on the basis of time displacement, is thatin each case the coefficients will be negative. And indeed we do find thatvirtually all the coefficients are negative. But not quite all of them. The effectof spending more time on the Internet on going out is substantial and negative(and we might note, quite a bit bigger than the equivalent coefficient in the


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162/ Social Forces82:1,September2003TABLE 10: Modeling Change in Time Use

Changen:Personal Go Out Visits Sports PhoneChangenPaidwork -.23** -.10** -.23** -.06** -.01**

Unpaidwork -.14** -.16** -.17** -.06** -.01*Computergames -.26 -.36** -.04 .03 .02Computeroffline .03 -.17* -.23* -.10* .00Computer:Web -.16 .23 -.20 -.11 -.02Constant 4.67* .72 -1.15 -.52 .72

AdjustedR2 .14 .09 .12 .04 .01(and we might note, quite a bit bigger than the equivalent coefficient in thecross-section-based estimate). According to our model, for each extra minutespent logged onto the Internet, there is something like 14 seconds of extra timespent going out. The coefficient is not significant- but if we look at the womenin the sample alone (Table 11), the coefficient is even larger; an extra minutedevoted to the Internet is associated with more than 30 seconds of extra timespent eating, drinking, going to the cinema. And this coefficient is statisticallysignificant. In both cases, other sorts of socializing time do reduce: time spentvisiting other people's homes declines at about the same rate that going outincreases. But if we sum these two types of out of home socializing, we still seethat the increase in the Internet time does not lead to a reduction of socializingtime. (Note, incidentally, that the change coefficients in Table 11 are generallyquite similar to the cross-sectional elasticity effects in Table 8: this tells us thatcross-sectional observations in this case are not in fact misleading - but panelobservations are nevertheless needed as confirmation.)

There is a potential statistical objection to modeling change in the way Ihave done in Tables 10 and 11, in that there may be an inherent correlationbetween the level of the dependent variable and its rate of change. (Amongpossible substantive, as opposed to merely econometric, reasons for this, mightbe barriers to entry to the activity such that those already engaged in it find iteasier to increase the time devoted to it, than do those who have not yetstarted.) One simple way of dealing with this problem is to enter the initiallevel of the dependent variable as an additional predictor on the right side ofthe regression equation. But there is a significant disadvantage, as comparedwith the approach taken in general in this article: in all the previously reportedregressions, we estimate the same set of right-side variables for all thedependents, so we can sum the coefficient along the rows, so as to see thebalance of effects of the independent variableson time use as a whole. Enteringa different initial-level variable for each equation loses this straightforwardlyinterpretable feature of the earlier tables. Table 12 compares the coefficients


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Web Use and Net Nerds / 163TABLE 10: Modeling Change in Time Use (Cont'd)

Changein:Hobbies TV,Etc. Reading Nothing Missing Total

Change nPaidwork -.02* -.19** -.05** -.11** -.01 -1.00Unpaidwork -.03* -.24** -.06** -.11** -.01* -1.00Computergames -.05 -.39* .11 -.07 .02 -1.00Computeroffline -.13** -.20* -.04 -.17* .01 -1.00Computer:Web -.18* -.43* -.03 -.13 .03 -1.00Constant -.85 1.11 -1.52 -.24 -2.95 .00AdjustedR2 .02 .14 .04 .06 .01*p


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164/ SocialForces82:1,September2003TABLE 11: Modeling Change in Time Use: Women Only

Changen:Personal Go Out Visits Sports Phone

ChangenPaidwork -.20** -.11** -.23** -.06** -.01Unpaidwork -.12** -.20** -.17** -.07** -.02**Computerames -.68 -.21 .76 .05 -.05Computerffline -.23 -.29* .09 -.19* .02Computer:Web .02 .51* -.46 -.07 .07Constant 6.26 .48 -3.67 -1.78 1.03

Adjusted 2 .10 .16 .12 .05 .01WHAT WE KNOW, AND DO NOT KNOW, ABOUT THE IT REVOLUTION, AND HOW TO FINDIT OUTIt is not of course at all surprising that, once we model this process correctly,using time diary measurement techniques, controlling appropriately for othersources of variation, and checking the cross-sectional difference evidenceagainst longitudinal change, the apparent association between Internet use andunsociable behavior should disappear. We all knew people, in the 1980s and1990s, who corresponded to the Nie and Erbring stereotype of the net nerd,somewhat reclusive, somewhat obsessive, more often than not located behinda computer screen. We would expect the stock of computer users in 1998 toinclude a fair number of such people. But this is not to say that the diffusionof net use leads to such behavior. It is, ultimately, only when we distinguishthe sort of person who had a computer in 1998 from the consequences ofacquiring a computer, or an Internet connection, at that particular historicaljuncture that we can establish the effect of the Internet. The panel design allowsus to do this. And a time-diary panel study,which provides adequate and stablemeasurement of time allocation at successive points in history, allows us toconstruct proper time-use elasticity models that show, at least, the time-usecorrelates of growth in Internet usage.But this does not provide an interpretation for our findings. Why is Internetuse positively (or at least, contrary to our time-displacement expectations, notnegatively) associated with sociability?I started by discussing the conceptualization of technological change aschange in the chains of activity associated with the provision of particular finalservices. New technologies are used in chains of provision that satisfyparticularwants. So, how (apart from my own work-related obsessions) do I use thistechnology? I use it (or I could do so)

To find out what is showing at my local cinema.To book tickets on trains and airplanes.


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WebUse andNet Nerds/ 165TABLE 11: Modeling Change in Time Use: Women Only (Cont'd)

Changen:Hobbies TV,Etc. Reading Nothing Missing TotalChangenPaidwork -.03* -.18** -.06** -.11** -.01 -1.00

Unpaidwork -.04* -.20** -.06** -.11* -.01 -1.00Computergames .06 -.44 .07 -.64 .08 -1.00Computerffline -.11 -.03 -.08 -.22 .03 -1.00Computer:Web -.60** -.77* .25 .08 -.02 -1.00Constant -1.01 4.59 -1.31 -1.61 -2.98** .00

Adjusted 2 .03 .11 .05 .05 .00*p


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166/ SocialForces82:1,September2003TABLE 2: Two AlternativeModels of Effectsof the Internet on Sociability

Model1 2 1 2Goingout(wavep) -.54**Visitingwavep) -.68**Changenpaidwork -.10** -.08** -.22** -.15**unpaidwork -.16** -.12** -.16** -.10**computerames -.36** -.28* -.04 -.10computerffline -.17* -.14 -.23* -.18*Internet .23 .29* -.20 -.24Constant .71 26.85** 1.15 34.43**Adjusted 2 .092 .311 .12 .435*p


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Web Use and Net Nerds / 167provision are only by asking, and seeing, what people are actually using the netfor, how it relates to other aspects of peoples' lives - by observing directly howthe technology is embodied in the chains of provision for the various finalservices we consume.

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