Ren, F.; Kwan, M.-P.; Schwanen, T. -- Investigating the temporal dynamics of Internet activities.pdf

http://tas.sagepub.com/Time & Society

http://tas.sagepub.com/content/22/2/186The online version of this article can be found at:

DOI: 10.1177/0961463X11421359 2013 22: 186Time Society

Fang Ren, Mei-Po Kwan and Tim SchwanenInvestigating the temporal dynamics of Internet activities

Published by:

http://www.sagepublications.com

can be found at:Time & SocietyAdditional services and information for

http://tas.sagepub.com/cgi/alertsEmail Alerts:

http://tas.sagepub.com/subscriptionsSubscriptions:

http://www.sagepub.com/journalsReprints.navReprints:

http://www.sagepub.com/journalsPermissions.navPermissions:

http://tas.sagepub.com/content/22/2/186.refs.htmlCitations:

What is This?

- Jul 11, 2013Version of Record >>

at UNIV OF VIRGINIA on June 14, 2014tas.sagepub.comDownloaded from at UNIV OF VIRGINIA on June 14, 2014tas.sagepub.comDownloaded from

Time & Society

22(2) 186215

! The Author(s) 2013Reprints and permissions:

sagepub.co.uk/journalsPermissions.nav

DOI: 10.1177/0961463X11421359

tas.sagepub.com

Article

Investigating thetemporal dynamicsof Internet activities

Fang RenUniversity of Redlands, Redlands, USA

Mei-Po KwanUniversity of California, USA

Tim SchwanenUniversity of Oxford, Oxford, UK

Abstract

The temporal dimensions of Internet use have been primarily examined as daily

or weekly totals in the literature, which ignores other important structural

dimensions of time and the contextual nature of Internet use. Using event

history analysis, this study provides a nuanced analysis of the timing and dura-

tion of Internet use with an Internet-activity diary dataset collected in

Columbus (Ohio, USA) in 20032004. Compared to the analysis of daily

totals, the fine-grained analysis reveals that besides social, demographic, and

geographic factors, the attributes of Internet episodes, such as activity purpose,

also significantly affect both the timing and duration, and these temporal dimen-

sions of Internet use are also connected.

Keywords

activity duration, activity timing, Columbus (Ohio, USA), event history, Internet

use analysis

Corresponding author:

Fang Ren, MS GIS Program, University of Redlands, Redlands, CA 92373, USA.

Email: [email protected]

at UNIV OF VIRGINIA on June 14, 2014tas.sagepub.comDownloaded from

Introduction

Over the past 15 years, the social implications of Internet use have attractedsignicant attention in media studies, sociology, geography, and relateddisciplines. The temporal dimensions of Internet use have featured in atleast two ways in the related literatures. First, some researchers haveargued that time spent online is a meaningful indicator of digital inequal-ities (DiMaggio et al., 2004; Barzilai-Nahon, 2006; Van Dijk, 2006), andothers have actually used it in this manner (Davidson and Cotton, 2003;Dholakia, 2006; Livingstone and Helsper, 2007). This reects that simplisticand binary indicators of access/non-access and use/non-use have increas-ingly been replaced by measures of the degree of inequality along multipledimensions, including time online (Rice and Katz, 2003; Van Dijk andHacker, 2003; Warschauer, 2003; Crang et al., 2006).

Second, a debate about whether the Internet takes away time from otherforms of media use and social contact was triggered by inuential studies byKraut et al. (1998) and Nie and Erbring (2000). The former proposed thatnew Internet users experienced lower levels of face-to-face communicationwith friends and family; the latter found that Internet users had less oinecontact with family and friends and spent less time on TV and print media.While subsequent studies also found that Internet use displaces the use oftraditional media (especially TV watching) and sleeping, the evidence aboutthe impact of the Internet on social participation is more mixed (Boase andWellman, 2006). Other empirical research nonetheless indicates thatInternet positively relates to interactions with friends (Shklovski et al.,2006; Wang and Wellman, 2010).

Notwithstanding the attention to the temporal dimensions of Internetuse, these studies have not truly engaged with the very notion of time itself.The ontology of time has been taken for granted, and time has been equatedto the amount of clock-time people spend online. Yet time has been inter-rogated and re-conceptualized in philosophy, sociology, and geography(Adam, 1990, 1998, 2008; May and Thrift, 2001; Schwanen, 2006), andwork along these lines can be used to reconsider the temporal complexitiesof Internet use. One useful framework is Adams notion of timescape.Here, time is a multifaceted social construct that is closely interrelatedwith both space and matter. It is embodied in a specic and unique contextand thus inherently contextual (Adam, 1998, 2008). Key structural dimen-sions of Adams conception of time include: time frame (scale, e.g. minute,hour, year, lifetime, epoch), temporality (process, irreversibility), timing(when), tempo (pace, intensity of activity), duration (length, extension),sequence (order, succession), and temporal modalities (past, present,future).

Ren et al. 187


In this study, we do not attempt to examine all the above temporaldimensions of Internet use, but rather focus on its timing and duration.The former has received little attention in the Internet usage literature weare only aware of Spennemanns (2006) work about daily usage curves forAustralia and the US West Coast. While insightful, Spennemannsapproach does not allow the context of Internet use to be analyzed ingreat detail. More insight in the timing dimension is nonetheless required,as it may reveal additional layers of inequality in Internet use, especiallywhen individuals face constraints that restrict such use to particular times ofthe day.

As the above discussion suggests, duration has only been examined asdaily or weekly totals. Hence, important attributes of the contextuality ofduration are lost. One way to foreground this contextuality is to analyze theduration of individual Internet activity episodes. In this study, an Internetactivity episode is dened as a continuous stretch of (clock) time onlinedevoted to a particular purpose, such as online shopping for householdneeds or web browsing for personal information needs. An activity episodeapproach is appropriate because it emphasizes the context (the why, when,where, with whom, duration and sequence) of activity participation (Bhatand Koppelman, 1999: 125) and is thus commensurable with Adams time-scape framework.

In short, this study focuses on the two important themes that havenot been fully examined in the literature on Internet use. First, thestudy addresses the timing and duration of Internet use, examininghow these temporal dimensions dier according to a broad range offactors. However, given the multi-modal distribution of timing andduration of individual Internet episodes and interdependence amongInternet episodes, conventional regression analysis is not appropriate.Instead, event history analysis was used in the study. Second, byusing event history analysis, the study provides a ne-grained analysisof the temporal dimensions, which furnishes a more nuanced under-standing of Internet use than the total time or the binary indicatorof use/non-use. For example, the duration of an Internet episode notonly relates to the characteristics of the Internet user, but also dependson the Internet usage of its previous episode and the timing of thisInternet episode. Data were drawn from a two-day activity-Internetdiary survey conducted in Columbus (Ohio, USA) in 20032004. Theremainder of this paper begins by outlining an interdisciplinary frame-work for understanding dierences in the timing and duration ofInternet activities. This will be followed by a detailed description ofthe data and methods used. Section 4 describes the results and thenthe paper concludes with discussion of ndings.

188 Time & Society 22(2)


Timing, duration and Internet use

Various lines of literature can be used to formulate expectations aboutfactors that may be related to the timing and duration of online activityepisodes. These include the aforementioned literature about the socialimplications of the Internet and studies of activity participation in the o-line world in transport studies, sociology, and human geography.

One lesson learned from research about oine activities is that variousattributes of activity episodes duration, timing, location, purpose, and soon tend to be interdependent (Burnett and Hanson, 1982; Kitamura et al.,1997; Schwanen et al., 2008). For instance, the starting time and duration ofoine activity episodes are shown to be correlated with each other. Whilethe associations are complex, it appears that oine activities for householdneeds and recreational purposes tend to last longer if conducted in theevening (Pendyala and Bhat, 2004; Schwanen, 2004). Further, durationsof dierent oine activities conducted at the same day are often relatedto each other. Some studies have suggested saturation eects in activityengagement: the time that people have already spent for a certain activitypurpose on a given day may reduce the duration of later episode of the samepurpose, presumably because their need for that type of activity has alreadybeen satised (Kitamura et al., 1997; Schwanen, 2004). It may well be thatthe observed interactions between attributes of oine activity episodesextend to online episodes.

Within the literature about the social implications of Internet use anddigital divides, a number of studies have suggested that such usage com-prises multiple, interdependent contextual dimensions (DiMaggio et al.,2004; Barzilai-Nahon, 2006; Van Dijk, 2006). DiMaggio et al. (2004), forinstance, proposed ve broad dimensions of inequalities in Internet use.These dimensions include technical apparatus, autonomy of use or the con-trol people have over their Internet use, skills, availability of social support,and purpose of use. We expected at least four of these dimensions to beassociated with the timing and duration of Internet activity episodes. Allelse being equal, a better technical apparatus, more autonomy of use andbetter skills may be associated with longer episodes and online activitiesstarting earlier in the day. With regard to purpose of use, our expectationswere less clear-cut. However, based on transport studies of activity partic-ipation in the oine world (e.g. Niemeier and Morita, 1996; Bhat andSteed, 2002), we expected Internet episodes for recreational purposes, cete-ris paribus, to last longer and to be undertaken later during the day thanepisodes for other purposes.

It is evident from the literature that the above dimensions of inequality inInternet use are related to peoples socioeconomic status, gender, age, and

Ren et al. 189


so on (Hargittai and Shafer, 2006; Zillien and Hargittai, 2009). However,socioeconomic position, age, and gender have been found to aect Internetuse directly after equipment, skills, availability of social support, and moti-vations are controlled (Zillien and Hargittai, 2009). Studies of time onlinehave also shown that these variables have direct impacts on the time online,though the eects vary across studies and not all studies controlled forequipment, skills, social support or autonomy of use (Davidson andCotton, 2003; Dholakia, 2006; Goldfarb and Prince, 2008; Loke and Foo,2010). Further, socioeconomic status, age and gender signicantly inuencethe timing and duration of activity episodes in the oine world (e.g.Niemeier and Morita, 1996; Bhat and Steed, 2002; Pendyala and Bhat,2004; Schwanen, 2004; Schonfelder, 2006; Pinjari and Bhat, 2010). Wetherefore included them in the analysis.

In addition to these factors, oine activities may also aect thetiming and duration of online activities. Geographers and transportresearchers have extensively studied the interactions between oineand online activities (Kwan, 2002; Kwan et al., 2007; Dijst et al.,2009; Ren and Kwan, 2009b). Their work suggests that the relationsbetween online and oine activity episodes are strong, complex, con-textual, and can take dierent forms. Salomon (1986) proposed aninuential typology of interactions between online and oine activities:neutrality (no interaction), substitution (online activity replaces oineactivities or vice versa), generation (online activity generates new oineactivities or vice versa), and modication (online activity modies oneor several dimensions of oine activities and vice versa). The last typeis most relevant to this study. The need to conduct particular activitiesin the oine world (e.g. childcare and housework) may result in onlineactivities being undertaken at other times of the day or for a shorterduration. In this way, gender roles in the physical world and the socialprocesses through which housework and childcare responsibilities areprimarily assigned to women may impose greater restrictions on theInternet use of women and/or those individuals who undertake manyhousework and childcare activities.

Further, a number of studies have indicated that geographic contextaects peoples use of the Internet to complete a specic task. For example,studies by Farag et al. (2007) and Ren and Kwan (2009a) have shown thatpeoples inclination to engage in online shopping decreases when morediverse brick-and-mortar stores can be reached within a limited traveltime from their residences. We may therefore expect that longer onlineepisodes are more likely to be performed outside the opening hours ofshops and recreational facilities or in the areas where accessibility to suchplaces is low.



In summary, we anticipated the timing and duration of Internet episodesto vary according to:

. Attributes of Internet activity episodes: purpose, duration (for timing) andstarting time (for duration);

. The technical apparatus and skills available for and autonomy overInternet use;

. Gender role within the household and time available for online activityparticipation;

. Accessibility to places for oine activity participation; and

. Socioeconomic position, age and gender. These may aect the timing andduration of Internet use independently from the above factors, becausemen and women, younger and older adults and people who are better orworse o may dier in terms of acquired resources, interests, dispositionsand motivations for using the web (Zillien and Hargittai, 2009).

Data and methods

Rationale

The dataset used in this study was collected by Kwan and Ren through apaper-based activity-Internet diary survey in the Columbus metropolitanarea (Ohio, USA) in 20032004. The survey was originally developed andaimed at collecting data to answer important research questions related toInternet use, including the impact of Internet use on peoples space-timeconstraints, activity-travel patterns, and share of household responsibilities.It also sought to examine the gender dierences with regard to the interac-tions between these dimensions and Internet use.

Because the data were collected in 20032004, they do not allow usto explore the recent growth in Internet use via mobile devices and insocial networking and social media applications (e.g. Facebook,LinkedIn) in great depth. We nonetheless believe the data have consid-erable value and relevance even today. First, to the best of our knowl-edge there is no other dataset that includes the kind of detailedinformation about households (e.g. sharing of dierent householdtasks) and participants physical and online activities (e.g. geographiclocation and spatial and temporal xity). The data make it possible toexamine hitherto under-examined substantive issues about the relationsbetween space-time constraints, oine activities and the many dimen-sions of Internet use, using an equally under-utilized but highly appro-priate methodological approach (event history analysis see below) toexamine the temporal dimensions of Internet use. Second, ndings of

Ren et al. 191


this study can provide a useful point of departure for future work onthe dierent factors that shape peoples Internet use in the mobile com-munications era. They can be used, for instance, as a reference pointfor comparisons. It should also be noted that Internet activities usingmobile devices are not entirely free from the inuence of the contextualfactors revealed in the study (e.g. household responsibilities) and thisstudy can aid in designing future research about constraints on wirelessInternet activities. Finally, the category of online leisure activities mayto some extent be comparable to social networking via the web.

Sample collection

The survey began with the creation of a stratied random sample of 32,000households in the study area using a commercial-grade and highly accurateresidential address list purchased from Haines & Company. This samplingmethod ensured that all the sub-areas of the study area were covered.Screening packages were then mailed to the 32,000 randomly selectedhouseholds, inviting households of couples with/without child(ren) orsingle parents with child(ren) to participate in the project. Eight hundredsand seventy ve households agreed to participate and were sent a surveypackage that included activity-Internet diaries of all adult household mem-bers. When returned surveys were incomplete or had inconsistencies,follow-up calls were made to correct the inconsistencies and ll in the miss-ing information.

Besides collecting household, individual, and Internet use information,the survey instrument also contained two-day activity-Internet diaries forall adult members of a household. The diary collected detailed data aboutparticipants physical and Internet activities for two designated survey days(weekdays only). All respondents were also provided with two activityrecord sheets, which they used to record when and where all of their activ-ities were undertaken for the two survey days. This greatly helped them torecall all the activities at the end of the day when they lled in the activity-Internet diaries. In the diary, the respondents answered more detailed ques-tions regarding the activities undertaken on a specic survey day, includingthe reasons why they conducted a particular activity, the start time andduration of the activity, travel time associated with the activity, the travelmode used, and so on. With regard to the primary purpose for undertakingan activity, six purposes were included in the survey: work or work-relatedactivities, essential household needs, personal needs, leisure or recreationalactivities, social activities, and other types of activities. It is possible that agiven activity was classied in several ways. For example, shopping can beas a household chore or a leisure activity. We let the respondents decide



which purpose each activity mainly fullled, given the specic situation inwhich it was undertaken. The respondents also needed to dierentiate theprimary and secondary activities when multitasking occurred. The follow-ing analysis only considers the primary activities.

Completed diaries from 420 individuals were eventually obtained. Sincethe number of non-Internet users in the nal sample is very small (n 28),this study focuses only on the Internet users in the sample (n 392). As eachInternet user kept two diaries for two weekdays, there are 784 diaries for thefollowing empirical analysis. The subsample of 392 Internet users includes256 women (65%) and 136 men. Most are white (93%), highly educated(80% are college or graduate degree holders), and rely on their own auto-mobile (97%). Further, 42 percent of the households in the subsample hadan annual income above $80,000; only 14 percent of the households earnedless than $40,000 per year. Obviously, women, whites, highly educated andhigh-income individuals are over-represented in the sample, which reects aself-selection process with highly educated and high-income individualsbeing more inclined to participate. This is understandable given the com-plexity of the 36-page diaries. Due to these characteristics of the sample, wedo not intend to generalize the empirical results reported in this paper to theentire population of the study area. Further, the analytical methods used inthe study may still be applicable for examining other population groupswhen such data are available.

Operationalization of timing and duration of Internet use

Among the 9244 primary activity episodes obtained via the activity-Internetdiaries, there were 1307 Internet activity episodes. Following previous workin transport studies (Reichman, 1976; Bhat and Koppelman, 1999), weaggregated their purposes into three general categories and focused onthe rst two categories of online activities because earlier research hadshown these activities to be gendered in dierent ways (Singh, 2001;Dholakia, 2006; Helsper, 2010):

. Online maintenance activities: associated with household responsibilitiesand personal needs, and including personal emails, shopping, banking,searching for information for childrens education and so on;

. Online leisure activities: comprising playing games, watching onlinevideos, browsing for fun, and online social activities such as chatting;and

. Online subsistence activities: including Internet use for employment andwork-related purposes.

Ren et al. 193


Using the above denitions, we derived the following sets of dependentvariables from the activity-Internet diaries:

. Daily total of time spent online, by purpose (maintenance and leisuretogether, maintenance only, leisure only), and whether or not respon-dents undertook online activities on a given day, by purpose (mainte-nance only, leisure only). These indicators of general Internet use areused as a base against which results of our ne-grained analysis of thetiming and duration of online episodes can be contrasted;

. Starting time of online activity episodes, by purpose (maintenance andleisure together, maintenance only, leisure only), and separately for therst and subsequent episodes on a given day, as an indicator of Adams(1998, 2008) timing dimension of Internet use. The starting time of therst episodes is the clock time the activity episode starts, while the start-ing time of a subsequent episode is measured by the time in minutes sincethe previous episode has ended; and

. Duration of online activity episodes, by purpose (maintenance and lei-sure together, maintenance only, leisure only), and separately for the rstand subsequent episodes on a given day, as an operationalization ofAdams duration dimension of Internet use.

In the empirical analysis, we draw a distinction between the rst Internetepisode and subsequent episodes on a given day. This is because subsequentepisodes are likely to have dierent behavioral underpinnings and may beaected dierently by certain factors. In transport studies, Bhat and Misra(2002) and others have argued and veried that decisions about whetherand when to undertake the rst (out-of-home) activity episode are driven bydierent factors than those for subsequent episodes. We thus assume in ouranalysis that choices with regard to subsequent episodes are dependent onprevious online activities.

Factors that may differentiate Internet use

Multiple indicators were used to represent/operationalize the attributes ofInternet episodes, technical apparatus, skills, gender roles, time availability,accessibility to places for oine activity participation, socioeconomic posi-tion, age, and gender. Table 1 provides denitions of all variables that weretested in the empirical analysis.

The indicators of attributes of online episodes and autonomy over theInternet were derived from the activity-Internet diaries. In line with theliterature, time availability was operationalized through two general indi-cators the number of hours in paid employment per week and the ratio of



Table

1.Variablesusedin

theempiricalanalysis

Nam

ebycategory

Description

Type

Mean

Share

Attribu

tesof

onlineep

isod

es

Purpose

Purpose

ofthecurrentonlineepisode

Maintenance

56%

Leisure

44%

Startingtime

Startingtime(24hclock)ofcurrentepi-

sode(inminutes)

Continuous[0,1440]

15:40pm

Duration

Durationofcurrentepisode(inminutes)

Continuous[2,290]

43min

Comparisonofpurpose

Comparison

ofpurpose

ofcurrentand

precedingepisode

Categorical

Samepurpose

64%

Differentpurpose

36%

Internet

apparatus

Technicalequipment

Equipment

for

accessing

Internet

from

home

Categorical

Mode

56%

Cable/D

SL44%

Internet

autono

my

Location

Location

where

Internet

episode

is

conducted

Categorical

Home

81%

Employm

entlocationandelsew

here

19%

Internet

skills

Internetexperience

Monthssince

first

usingtheInternet

Continuous[0,223]

86

(continued)

Ren et al. 195


Table

1.Continued

Nam

ebycategory

Description

Type

Mean

Share

EaseofusingtheInternet

Self-perceivedeaseofusingtheInternet

Ordinal[1,5]

Internetability

Self-perceivedability

inusingtheInternet

Ordinal[1,5]

Tim

eavailability&

gend

errole

Weeklyemploym

enthours

#ofhours

allocated

topaid

work

per

week

Continuous[0,88]

30h

Numberofchildrenperadult

Ratio

of#

ofchildrenunder18to

#of

adultsin

thehousehold

0.51

Grocery

shopping

Theshareofgrocery

shoppingwithin

the

household

perform

edeachweek

Continuous[0,100]

59.3%

Cooking

The

share

of

cooking/baking/preparing

meals

within

the

household

perform

ed

eachweek

Continuous[0,100]

59.3%

Indoorcleaning

Theshareofindoorcleaningwithin

the

household

perform

edeachweek

Continuous[0,100]

57.9%

Outdoorcleaning

Theshareofoutdoorcleaningwithin

the

household

perform

edeachweek

Continuous[0,100]

48.9%

Gardening

Theshareofgardeningwithin

thehouse-

hold

perform

edeachweek

Continuous[0,100]

47.2%

Repairs

Theshareofrepairs

within

thehousehold

perform

edeachweek

Continuous[0,100]

45.8%

Financialmanagement

Theshareoffinancial

managementwithin

thehousehold

perform

edeachweek

Continuous[0,100]

57.5%

Care-givingto

infants

Theshareofcare-givingto

infants

within

thehousehold

perform

edeachweek

Continuous[0,100]

13.9%

(continued)



Table

1.Continued

Nam

ebycategory

Description

Type

Mean

Share

Chauffeuring

Theshareofchauffeuringchildrenwithin

thehousehold

perform

edeachweek

Continuous[0,100]

31.2%

Help

withhomew

ork

The

share

ofhelping

with

homew

ork

within

the

household

perform

ed

each

week

Continuous[0,100]

26.8%

Playingwithchildren

Theshareofplayingwithchildrenwithin

thehousehold

perform

edeachweek

Continuous[0,100]

29.1%

Accessibility

Urban

opportunities,5minutes

#ofurban

opportunities

that

can

be

reachedfrom

theresidence

within

a

5-m

inute

drive

Continuous[0,923]

193

Urban


#ofurban

opportunities

that

can

be

reachedfrom

theresidence

within

a

10-m

inute

drive

Continuous[57,2727]

873

Urban


#ofurban

opportunities

that

can

be

reachedfrom

theresidence

within

a

15-m

inute

drive

Continuous[105,4213]

2059

Populationdensity

Population

density

ofthe

Censusblock

grouparea(persons/sq

miles)

Continuous[4,743]

156

Socioe

cono

mic

position

Household

income

Gross

household

annualincome

Ordinal[1,7]

Education

Highest

educationattainment

Ordinal[1,4]

(continued)

Ren et al. 197


Table

1.Continued

Nam

ebycategory

Description

Type

Mean

Share

Occupation

Respondentstypeofoccupation

Categorical

Services

17.3%

management/professional

45.7%

Precisionproduction

14.0%

Operator/fabricator

1.0%

Clericalandadministrative

2.0%

Technical

8.2%

Sales

7.9%

Other

3.89%

Neighborhoodincome

Medianhousehold

incomeoftheCensus

block

grouparea(U

S$)

Continuous[13167,200001]

61518

Sharewhite

Percentage

thepopulationresidingin

the

Censusblock

groupareathat

iswhite

Continuous[0,1]

86.8%

Gen

der

Respondentsgender

Categorical

Female

65%

Male

35%

Age

Respondentsagein

years

Continuous[20,75]

42.4



children per adult in the household. Gender role was evaluated via a seriesof indicators that measure respondents self-rated contribution to specichousework and childcare tasks. These measures thus captured subtle dier-ences in the gender division of household labor between spouses orpartners.

Accessibility to places for oine activity participation was evaluated intwo ways. First, three accessibility measures of the cumulative opportunity(CO) type were used, measuring the number of urban opportunities that canbe reached within specic travel times from respondents home location(Kwan, 1999; Weber, 2003). The number of urban opportunities wasderived from the Franklin County land parcel dataset maintained by theFranklin County Auditor. Twenty-eight categories of a total of 4996 parcelswere considered as urban opportunities where people might conduct dailymaintenance and leisure activities in the oine world. Generally, theyinclude various types of stores, entertainment opportunities (such as nightclubs, cinemas and parks), banks, restaurants and sports facilities. Usingthese parcels as representative of urban opportunities, we calculated the COindices for 5-, 10-, and 15-minute driving times respectively for each home,using a specialized geographic information system (GIS) software (ArcGISNetwork Analyst). Second, we used population density at the neighborhoodlevel as an indicator of oine interaction opportunities in the study area. Inthis way, we can account for the greater availability of opportunities foroine activities in the neighborhoods that have a higher population density.People in such neighborhoods may more actively interact with their friendsand neighbors.

Finally, socioeconomic position was operationalized through ve indi-cators. These include income and education, as well as occupation (sincecertain occupations do not lend themselves well for using the Internet forpersonal purposes during employment hours). Two indicators of the neigh-borhoods where respondents live were also included. This is because socialpolarization between neighborhoods is rather pronounced in the Columbusmetropolitan area.

Event history analysis

Event history analysis (EHA) or survival analysis was used to examinedierences in the timing and duration of Internet episodes. This methodexamines the occurrence of a signicant event in a certain period under theassumption that the probability of it occurring depends on the time-spanthat has elapsed (Allison, 1984). Hence, unlike linear regression analysis,EHA takes duration dependence in the occurrence of events into account.Death is the classic example of an event in medical research, and the

Ren et al. 199


probability of its occurrence increases when the lapse of living (surviving)lasts. However, social scientists have employed survival analysis to under-stand the (non)occurrence of many other and less harmful events. The termsurvival in these applications refers to the cases that have not experiencedthe given event. For example, survival analysis can be used to analyze theprobability that a person becomes employed (equivalent to the death ear-lier) as a function of the time-span he/she has been unemployed (equivalentto the survival earlier) and such covariates as age, gender, and education.EHA is particularly helpful in explaining why certain individuals have ahigher risk of experiencing an event under consideration than others.

Event history analysis usually starts with a descriptive analysis of thedistribution of events across the time period considered. To this end, thetime period is divided into discrete intervals. Based on tests with our data,15-minute intervals were used for the timing analysis and 5-minute intervalsfor the duration analysis. For each interval tj, the number of cases at risk(i.e. cases that have not experienced a given event at the beginning of tj) andthe number of survived cases at the end of tj need to be calculated. Usingthese two values, the proportion of cases that survived (not experiencing theevent) through tj is then computed. By plotting the proportion of survivedcases for each interval, a survival curve is obtained, which shows the survivalprobability at dierent times. The inverse of survival probability is calledthe hazard rate, which indicates the probability that a case may experiencethe event at the end of tj. Thus, with regard to timing, the hazard ratemeasures the probability that an episode starts by the end of tj; for duration,it indicates the likelihood that an episode ends by the end of tj.

Various linear, regression-type models have been developed to examinewhich covariates aect how the hazard rate changes over time. The Coxproportional hazard model was used for this analysis as it requires no priorknowledge about the probability distribution of events (Cox, 1972).Because it is quite general and nonrestrictive, it has been widely appliedin the study of oine activity participation in transport studies and beyond(Niemeier and Morita, 1996; Schwanen, 2004; Ettema et al., 2007).

Empirical results

General Internet use

The subsample contains 392 respondents and 748 diaries for the two surveydays. After averaging the total time spent on online maintenance and leisureactivities, we obtained 308 respondents who engaged in at least one Internetepisode for either maintenance or leisure purposes. On average, the 308respondents spent 34 and 30 minutes per day on online maintenance and



leisure activities. While these averages do not dier signicantly from eachother (p 0.35), the share of respondents who participated in online main-tenance activity is considerably higher than that for online leisure: 60 per-cent against 48 percent. This may reect that there are more maintenancetasks to complete in everyday life and they are more mandatory than leisureactivities (Schwanen et al., 2008).

In keeping with the literature on the social implications of Internet use,linear regression analysis was used to analyze which factors are associatedmost strongly and directly with the total time spent online for maintenanceand leisure purposes combined (Table 2). Weekly employment hours, anindicator of time availability, is correlated most strongly with time onlinefor these purposes. As expected, people who work longer hours on a weeklybasis tend to spend less time on maintenance and leisure activities on theInternet. Other factors that previous studies have shown to be related tototal time online, such as technical equipment (connection speed), Internetskills, accessibility to places in the oine world, socioeconomic status, age,and gender, yielded no statistically signicant results for our respondents.

In addition, logistic regression was used to analyze the proles of respon-dents participating in online maintenance and leisure activities respectively.We further specied two linear regression models for the total time onlinefor maintenance purposes with the respondents engaging in this activity,and for the total time online for leisure with those who undertook thisactivity. As shown in Table 2, the indicators of time availability featureprominently in all four models; as respondents work longer hours, theyare less likely to engage in online maintenance or leisure activities andspend less time on maintenance activities. The number of children peradult in the household has a strong negative association with the totaltime dedicated to online leisure, which likely reects that many childcare-related activities, such as transporting children to school and helping withhomework, cannot be completed over the web and thus reduce time avail-able for recreational Internet use.

Although the total time spent on maintenance activities does not vary bygender, the logistic regression analysis indicates that women are more likelyto engage in these activities than men. These ndings concur with expecta-tions. Because of womens greater responsibility for housework andchildcare, they are more likely to conduct online activities related to theseroles. At the same time, domestic responsibilities make it dicult for thewomen in the sample to spend much time on online activities. As with totaltime online, technical equipment, Internet skills, accessibility to placesoine, socioeconomic position and age are not signicantly related toparticipation in and total time spent on either maintenance or leisure activ-ities on the web.

Ren et al. 201


Table

2.Logisticandlinear

regressionmodelsforindicators

ofgeneralInternetuse

Dailytotaltime

onlineperperson,

maintenance

and

leisure

combined

Maintenance

activity

online(Yes1)

Dailytotaltime

onlineperperson,

maintenance

only

Leisure

activity

online(Yes1)

Dailytotaltime

onlineperperson,

leisure

only

Coef.

S.E.

Coef.

S.E.

Coef.

S.E.

Coef.

S.E.

Coef.

S.E.

Weeklyemploym

ent

hours

0.405**

0.157

0.012*

0.006

0.374***

0.131

0.014**

0.226

Numberofchildren

peradult

0.391**

0.165

10.543*

5.729

Gender(female)

0.768***

0.222

Modeltype

Linear

regression

Logisticregression

Linear

regression

Logisticregression

Linear

regression

nobs.

308respondents

392respondents

232respondents

392respondents

188respondents

R2/

2a

0.126

0.150

0.134

0.127

0.118

Notes:aR2isusedforthelinear

regressionmodels;2forthelogisticregressionmodels.*p

Ren, F.; Kwan, M.-P.; Schwanen, T. -- Investigating the temporal dynamics of Internet activities.pdf

Documents

Transcript of Ren, F.; Kwan, M.-P.; Schwanen, T. -- Investigating the temporal dynamics of Internet activities.pdf