Computers in Human Behavior 26 (2010) 1158–1165

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Computers in Human Behavior

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The use of interactive media among today’s youth: Results of a survey

Antoine Van den Beemt a,*, Sanne Akkerman b, Robert-Jan Simons b

a Fontys Hogeschool ICT, Fontys University of Applied Sciences, The Netherlandsb IVLOS Institute of Education, P.O. Box 80127, 3508 TC Utrecht, The Netherlands

a r t i c l e i n f o a b s t r a c t

Article history:Available online 10 April 2010

Keywords:Youth cultureInteractive mediaGamesUser’s activitiesUser’s opinions

0747-5632/$ - see front matter � 2010 Elsevier Ltd. Adoi:10.1016/j.chb.2010.03.022

* Corresponding author. Address: Fontys Hogesch4.106, Fontys University of Applied Sciences, P.O. BoxNetherlands. Tel.: +31 877 872 271; fax: +31 877 871

E-mail addresses: a.vandenbeemt@fontys.nl, a.vanden Beemt).

1 ’Youth’ refers to a cohort of young people, especithan to the social construct of youthfulness. See also Budiscussion of ’youth’ as a social construct.

The intensive use of interactive media has led to assertions about the effect of these media on youth. Thispaper presents a quantitative study on the position of interactive media in young people’s lives. Ratherthan following the assumption of a homogeneous generation, we investigate the existence of a diversityof user patterns. The research question for this paper: Can patterns be found in the use of interactive mediaamong youth? We answer this question by a survey among Dutch youngsters aged 10–23. Four clusters ofinteractive media users, namely Traditionalists, Gamers, Networkers and Producers were identified usingcluster analysis. Behind these straightforward clusters, a complex whole of user activities can be found.Each cluster shows specific use of and opinions about interactive media. This provides a contextualizedunderstanding of the position of interactive media in the lives of contemporary youth, and a nuancedconceptualization of the ‘Net generation’. This allows for studying the intricate relationship betweenyouth culture, interactive media and learning.

� 2010 Elsevier Ltd. All rights reserved.

1. Introduction

During the last decade, games and Internet applications, to-gether comprising interactive media, have become tools for infor-mation and communication that are used daily by the so-called‘Net generation’ (Tapscott, 1998). This has led to assertions aboutthe enormous effect of interactive media on youth1 (cf. Prensky,2006; Tapscott, 1998). These assertions describe today’s youth asusing interactive media with great intensity and skill (Bennett, Ma-ton, & Kervin, 2008).

The Net generation discourse often starts from these assertionsto describe the difference between contemporary youth and oldergenerations. For instance, Tapscott (1998) and Prensky (2006)based their work on two sets of binary oppositions, between tele-vision and Internet technology, and between the babyboomer gen-eration and the Net generation (Buckingham, 2008a, 2008b). Thisstark opposition has contributed to an image of today’s younginteractive media users as one homogeneous group, rather thanas diversified in subgroups.

Often, the Net generation discourse is motivated by a concernabout the relationship between young people, interactive media

ll rights reserved.

ool ICT, Building R1, Room347, 5600 AH Eindhoven, The

400.denbeemt@manabi.nl (A. Van

ally those aged 10–25, ratherckingham (2008a, 2008b) for a

and education (cf. Oblinger & Oblinger, 2005; Shaffer & Gee,2005). Buckingham (2008a, 2008b) speaks in this respect of a dig-ital divide between in-school and out-of-school use, which he seesas a symptom of the ‘‘widening gap between young people’s every-day ‘life world’ outside school and the emphases of many educa-tional systems” (Buckingham, 2007 as cited in Ito et al., 2008, p.4). The Net generation discourse is valuable for education, becauseit provides a framework to pose questions and make decisionsregarding the use of interactive media in learning. However, thisdiscourse often starts from the premise of today’s youth beingone generation. Moreover, the discourse tends to focus on com-puter and internet use and skills instead of the meaning of interac-tive media in young people’s lives (Bennett et al., 2008; Margaryan& Littlejohn, 2008). By studying the use of interactive media andthe resulting social relations, we are able to describe an importantpart of young people’s social space. It is by and through this socialspace, that people develop their values and beliefs, as such inform-ing their cultural space (Author, 2007). Investigating both youngpeople’s social and cultural space, in terms of use and meaning,contributes to a better understanding of youth’s perspective oninteractive media.

In a number of studies, the premise of a skillful and homoge-neous generation is seriously questioned by empirical research.According to these studies, young people have intermediate ratherthan high ICT-skills (Cameron, 2005; Margaryan & Littlejohn, 2008)and their internet use is characterized by ‘‘relatively mundaneforms of communication and information retrieval” rather than‘‘spectacular forms of innovation and creativity” (Buckingham,2008a, 2008b, p. 14). A small number of studies pay attention to

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gender differences. These studies show that generally, gender dif-ferences are not very pronounced for interactive media use itself(Hargittai, 2010; Jones, Ramanau, Cross, & Healing, 2009; McQui-llan & O’Neill, 2009), apart from games, which boys play two timesas often compared to girls (Duimel & De Haan, 2007; Kutteroff &Behrens, 2009; Schulmeister, 2008). Furthermore, boys are some-what more skilled in internet use compared to girls (Hargittai,2010).Young people use interactive media intensively (Duimel &De Haan, 2007; Schulmeister, 2008). However, there appears tobe a diversity in kinds of media being used, both within as wellas between age categories (Duimel & De Haan, 2007; Ito et al.,2008; Kutteroff & Behrens, 2008, 2009; Schulmeister, 2008). Be-cause most studies focus either on one educational level, or onage groups regardless of education, it is difficult to draw conclu-sions about the relation between education and interactive mediause. Taken together, these results show diversity among youth inthe skills, the kinds of media being used, as well as in the userintensity. These results do not point in the direction of a Net gen-eration as one skillful and homogeneous group.

The investigation into the Net generation often begins with pre-defined classes of activities such as ‘information retrieval’, ‘socialnetworking’, ‘online gaming’ or ‘downloading’ (Duimel & De Haan,2007; Kutteroff & Behrens, 2008; Livingstone & Bober, 2005). De-spite the usefulness of these classifications, the relationship be-tween activities remains unclear. Investigating this relationship isvaluable, since youth culture studies show that young people usecombinations of available content and media to show others whatthey think is important (Weber & Mitchell, 2008). Profile pages forinstance, often show images of their owners merged with photo-graphs of popstars or sportsmen. This bricolage (Lévi-Strauss,1962) of different kinds of both content and interactive mediacan be studied by looking for patterns in the activities. Does onlinegaming go together with information retrieval? Does social net-working leave time for downloading music or films? Can a conver-gence of media activities be found, or is there a divergence intoseparated user groups?

Instead of using pre-defined classes of interactive media, Itoet al. (2008) and Jenkins (2006) start their research from the user’sgoals. This results in the concept of participation: friendship-drivenand interest-driven genres of participation (Ito et al., 2008). Thisperspective allows for describing combinations of activities basedon the purpose of either contact and communication or interestin specific information-domains. This makes friendship-drivenand interest-driven genres of participation useful in the study ofthe meaning of interactive media to young people’s lives. Jenkins(2006), in his discussion of participation, uses the term ‘production’for both the creation of digital products, as well as for the interac-tive consumption that is part of production. Consumption in thissense describes the ways in which popular images are being col-lected, combined, critiqued and incorporated in new content thatpresents the user’s identity. Production leads to a convergence ofmedia when all kinds of content types and applications are beingcombined. What these studies show is the importance of lookingbeyond activities per se, and of describing possible motivationsfor interactive media use.

Following Ito et al. (2008), we argue that investigating solely apossible diversity of media activities would lead to a limited viewof the intricate relationship between young people and interactivemedia. A classification of activities, however useful, describes onlyone dimension, without examining the premise of the Net genera-tion as a homogeneous group. We argue that more nuance in theview on today’s youth can be obtained, for instance by investigatingyoung people’s social space and cultural space. Investigating the so-cial space entails a consideration of the various kind of social activ-ities of youth. Looking at user patterns, that is, analyzing how youthuses different sets of media can reveal this. Investigating the cultural

space entails a consideration of the values and meanings developedthrough these social activities. A first way of revealing this is by ask-ing youth about their opinions about the use interactive media forcontacting other people. By looking at user patterns and opinionsabout the use of interactive media, we expect that a second dimen-sion, formed by user groups, will appear. Considering both thedimension of grouped activities and the dimension of grouped usersallows us to study possible consequences for education of intensiveinteractive media use by young people. For instance, if our resultswould show that most students appear to be networkers with a neg-ative attitude towards gaming, education could consider using socialsoftware instead of games as a learning tool.

We are currently investigating both dimensions by means ofquantitative and qualitative research. This paper presents the re-sults of the quantitative research which aims at describing theinteractive media behavior among young people and their statedopinions about these media. In order to investigate user activitiesand opinions we formulated the following question: Can patternsbe found in the interactive media activities of young people? Inanswering this question, special attention will be paid to genderand educational level in relation to user patterns, because we ex-pect differences for these two factors. For instance, social software,such as Facebook, asks for a communicative attitude towardspeers. Games are often complex and they ask for spatial and stra-tegic skills. Creating interactive media content asks for a producingattitude. If the Net generation exists, we will not find differences inopinions about and use of interactive media for gender or level ofeducation. Furthermore, psychological accounts of adolescence,such as the notion of ‘moratorium’ (Erikson, 1968), suggest thatwe need to distinguish between age groups. In our study this isdone by considering different educational levels.

The answer to our main question forms the foundation fordescribing a diversity of subcultures based on the place of specificinteractive media in young people’s everyday life. Our analysis ofuser patterns will lead to new, more specific questions for furtherresearch on young people and their media behavior. With these re-sults we wish to contribute to the Net generation discourse, a bet-ter understanding of the possible meaning of interactive media foreducation and learning.

2. Method

2.1. Participants

The participants of the study were 178 Dutch students, in educa-tion levels ranging from primary education (N = 55; 25 female; age:M = 11.24, SD = .77), secondary education (N = 94; 30 female; age:M = 14.45, SD = 1.45) to higher professional education (N = 29; 4 fe-male; age: M = 21.86, SD = 1.25) (see Table 1). Participating schoolswere found through the Fontys University network. The study sam-ple was not drawn randomly because schools participated voluntar-ily, often with at least one class of respondents. Because of theexplorative nature of the study, and the intention to compare educa-tional levels, all responding students were included in our sample,instead of drawing a random selection. The participating secondaryand higher education schools have a curriculum focused on technol-ogy. As a consequence these schools have more male students,which affected the boy–girl ratio in our sample. The participatingschools all had a largely white middle class population.

2.2. Materials

The online survey consisted of 23 questions addressing actualuse of interactive media and opinions about specific media.Each item referred to one of all interactive media used in the

Table 1Participants.

Educational level Age group Number ofparticipants

Male Female

Primary education 10–13 55 30 25Secondary education 12–16 94 60 30Higher professional

education19–23 29 25 4

Total 178 119 59

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Netherlands at the time of inquiry. In Table 2 an overview of theseinteractive media can be found. Examples of questions were:

How often do you:

– Surf the Internet.– Play console games.– Maintain your profile page.

Do you agree with the following sentences:

– I like to play games together with others.– I feel unhappy when I cannot play games.– Sometimes I stay longer on MSN than I want to.

Answer categories followed a five-point Likert scale ranging from‘never’ to ‘every day’ for activities and ‘totally disagree’ to ‘totallyagree’ for opinions. Means of three and larger indicate, respec-

Table 2Clusters of activities, mean standardised score (and SD) within cluster.

Cluster traditionalistActivities N = 41

Browsing 2.22 (1.20)SearchE-mailMSNSurfing the webWatching videosReading news sites

Performing 2.42 (1.37)Large PC gamesSmall PC gamesOnline gamesCasual online gamesPortable gamesConsole gamesMobile games

Interchanging 1.14 (.42)Maintaining Hyves profileLooking at profile pagesLeaving a scrapUploading photo to HyvesMaint. weblog at HyvesReading weblogsMaintaining weblog other than profile

Authoring 1.34 (.58)SkypeUploading videosUploading photos other than to profileLooking at photo-albumGoogle EarthGoogle DocsLooking at MyspaceUploading to MyspaceDownloading podcastsMaking music on PCReading WikipediaDownloading musicDownloading filmsHabbo

N = 158; cluster analysis: Ward’s method, squared Euclidian distance, z-scores; 1 = neve

tively, a regular use and positive opinion. Because we expectedthe strongest response for in- and out-group behavior, gamesand social software, we asked mainly for opinions on these topics.

2.3. Procedure

The questionnaire was developed by incorporating key charac-teristics of subcultures (Brake, 1985) and existing media research(Duimel & De Haan, 2007). The survey was preceded by a ‘think-aloud’ session (Van Someren, Barnard, & Sandberg, 1994) withthree primary education students, to control for comprehensionof the questions and for the time required to fill in the survey.The results of this session led to minor adjustments in the phrasingof questions. No further reading level analysis was pursued. Allrespondents of the online survey received textual instructionsabout the survey’s purpose and ways to fill it in. The instructionsexplained the purpose of the survey, that it would not be graded,and therefore that any answer would be right. Furthermore, thestudents were asked to fill in the survey at their own comfortablespeed. Most schools arranged for each class to fill in the survey in acomputer laboratory with internet access. The survey was held be-tween February and April 2008.

2.4. Analytic strategy

The statistical analyses were performed in several steps. First,cluster analysis was applied to explore the existence of categories

Gamer Networker ProducerN = 64 N = 32 N = 21

3.92 (1.14) 3.45 (1.12) 4.34 (1.02)

2.35 (1.33) 1.71 (.87) 3.21 (1.53)

2.02 (1.07) 3.12 (.90) 3.42 (1.60)

1.69 (.74) 1.49 (.63) 2.55 (1.36)

r, 2 = less than once a week, 3 = once per week, 4 = more times per week, 5 = daily.

A. Van den Beemt et al. / Computers in Human Behavior 26 (2010) 1158–1165 1161

of media activities. In order to do so, Ward’s method with squaredEuclidian distance and z-scores (Aldenderfer & Blashfield, 1984;Milligan & Cooper, 1988) was applied on the interactive mediaitems from the questionnaire. This resulted in a pattern of relatedactivities. Cluster analysis with the same method was applied onthe cases to find a pattern of related interactive media users. Witha one-way ANOVA the significant difference in means between theclusters of activities and users was checked. Levene’s test of homo-geneity of variance showed a p < .05 for two clusters. Because ofthis violation of homogeneity of variance, the Welch F-ratio is re-ported in the results section. Furthermore, the Games–Howellmethod was used for post hoc comparisons. Following existing re-search (Duimel & De Haan, 2007), application use of at least onceper week is considered as ‘regular’ use.

Categories of opinions were derived from cluster analysis onvariables, applying Ward’s method with squared Euclidian distanceand z-scores. Again a one-way ANOVA was applied. Because theassumption of homogeneity of variance was violated, the WelchF-ratio is reported and the Games–Howell method was used forpost hoc comparisons. In order to enhance the description of eachuser cluster, we analyzed specific correlations between opinions.These correlations, when of relevance for the interpretation, willbe discussed for each cluster separately.

3. Results

Our data show a diversity in the use of interactive mediaapplications. All respondents reported making use of at least oneapplication once per week or more. This means that there are nonon-users in our sample. Myspace, wikis, podcasts, Second Lifeand Skype were reported by respondents to be rarely used. Onthe other hand, the more basic internet applications, such as e-mail, chat applications such as MSN, or surfing the web, were usedby more than two-thirds of our respondents.

Table 3Summary of post hoc results for groups of activities.

Activities Cluster Traditionalist Gamer Networker

Browsing Producer > > >Networker > >Gamer >

Performing Producer > > >Networker < <Gamer =

Interchanging Producer > > =Networker > >Gamer >

Authoring Producer > > >Networker = <Gamer >

‘=’ indicates no significant difference between cluster means, ‘>’ indicates horizontalmean significantly larger than vertical mean, ‘<’ indicates horizontal mean sig-nificantly smaller than vertical mean.

3.1. Diversity in interactive media use and users

Cluster analysis applied on the use interactive media applica-tions revealed four significant clusters in the behavior of ourparticipants. These clusters are an indication of diversity in interac-tive media use. One cluster (see Table 2) consists of e-mail, surfingthe web, searching for information and MSN. Because these are tra-ditional, more basic internet activities, focused on the consumptionof information we labeled them ‘browsing’. A second cluster, con-sisting of gaming activities, is a form of interest-driven participa-tion (cf. Ito et al., 2008) where users play a certain role on avirtual stage. We labeled this cluster ‘performing’. A third clustercan be called friendship-driven, and consists of all kinds of socialnetworking activities. We labeled this cluster ‘interchanging’. Thelast cluster consists of a larger number of activities, all of themcomprising some form of content production in line with Jenkins(2006). We labeled this cluster ‘authoring’. Together, the four clus-ters of activities form a dimension of interactive media use rangingfrom consumption (browsing) to production (authoring).

A second round of cluster analysis was applied to investigatehow the clusters of activities related to individual participants inour sample. This resulted in four clusters describing types of mediausers. We labeled them according to the main activity group ineach user cluster: Traditionalist, Gamer, Networker and Producer.These four clusters of respondents form a second dimension, thistime of interactive media users ranging from consumers (Tradi-tionalist) to producers (Producer). A one-way between subjectsANOVA was conducted to compare the activity clusters for eachuser cluster. The results show that all four clusters of users havesignificantly different mean scores on all four activity clusters at

the p < .05 level, indicating that groups of users engage with differ-ent intensity in these activities: browsing, F(3, 154) = 74.93,p < .001; performing, F(3, 154) = 17.59, p < .001; interchanging,F(3, 154) = 68.14, p < .001; authoring, F(3, 154) = 64.65, p < .001.Welch F-ratio showed a significant difference between activityclusters for all user clusters, which confirms the ANOVA results:browsing, F(3, 66.21) = 99.19, p < .001; performing, F(3, 64.11) =19.68, p < .001; interchanging, F(3, 58.81) = 162.00, p < .001;authoring, F(3, 62.27) = 37.14, p < .001.

As Table 3 shows, post hoc comparisons for browsing activities(p < .05) indicated that all four user clusters engage with signifi-cantly different intensity in this type of activity: Traditionalists(M = 2.22, SD = .52), Gamers (M = 3.92, SD = .70), Networkers(M = 3.45, SD = .72) and Producers (M = 4.34, SD = .55). For per-forming activities, post hoc tests indicated that Traditionalistsdid not differ significantly from Gamers in using games, as is rep-resented in Table 3 by the ‘=’-sign. However, the other user clustercombinations were indicated to engage with significantly differentintensity in performing: Traditionalists (M = 2.42, SD = .82), Ga-mers (M = 2.35, SD = .73), Networkers (M = 1.71, SD = .55) and Pro-ducers (M = 3.21, SD = .87). Analyses of interchanging showed thatNetworkers (M = 3.12, SD = .48) and Producers (M = 3.42, SD = 1.29)did not engage with significantly different intensity in this activity.Traditionalists (M = 1.14, SD = .27) and Gamers (M = 2.02, SD = .76)(p < .01) were indicated to engage with significantly differentintensity in interchanging. Finally, for producing activities, posthoc tests indicated a significant difference between Gamers(M = 1.72, SD = .33) and Producers (M = 2.54, SD = .57), and a non-significant difference between Traditionalists (M = 1.34, SD = .26)and Networkers (M = 1.46, SD = .24). Taken together, these resultssuggest that a relation exists between membership of a certainuser group and kinds of activities people participate in. The inten-sity of participation applies especially to browsing activities.Specifically, our results suggest that Traditionalists and Gamersboth engage in a similar way in performing activities, while Net-workers appear to engage significantly less in performing activi-ties. Networkers and Producers are most active in interchanging,while Traditionalists and Gamers are significantly less active withinterchanging. Finally, our results suggest that only Producers aresignificantly active with authoring. This makes Producers the mostdedicated users of their own applications, next to being the mostintensive users of all kinds of interactive media.

Following the gender issue in the Net generation debate, weanalyzed the division of user clusters among boys and girls. Table4 confirms the popular belief that boys are most often Gamersand girls are most often Networkers. Our data show the strongestgender-difference for Gamers and Networkers, while boys and

Table 4Cluster membership: percentage – educational level and gender.

Cluster traditionalist Gamer Networker Producer Total

Educational levelPE 64.6 14.6 10.4 10.4 100SE 11.9 41.7 29.8 16.7 100HPE 0 84.6 7.7 7.7 100

GenderBoys 26.2 52.4 6.8 14.6 100Girls 25.5 18.2 45.5 10.9 100

PE, primary education; SE, secondary education; HE, higher professional education.

Table 5Educational level: percentage – cluster membership.

Educational level Cluster traditionalist Gamer Networker Producer

PE 75.6 10.9 15.6 23.8SE 24.4 54.7 78.1 66.7HPE 0 34.4 6.3 9.5Total 100 100 100 100

PE, primary education; SE, secondary education; HE, higher professional education.

Table 6aOpinions on media activities.

Opinion code Opinion – full description

1. Gaming together I like playing games with others2. Longer MSN Sometimes I stay longer on MSN than I want to3. Unhappy no

HyvesI feel unhappy when I can’t go on Hyves

4. Unhappy nogames

I feel unhappy when I can’t play games

5. Unhappy no MSN I feel unhappy when I can’t go on MSN6. Belong to group I want to belong to a group7. Longer games Sometimes I play games longer than I want to8. Gamers group Because I play games, I belong to a special group9. No-gaming

friendsI have many non-gaming friends

10. Importantgaming

It is important to be good at games

11. Like gamersbetter

Children who play games are more fun than others

12. Like Wii better I prefer the Wii to games where you sit still13. Pretending I like games because you can pretend things14. Production I like internet and games because you can produce

things

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girls are roughly as often a Traditionalist or a Producer. In addi-tion to the gender issue, we analyzed the relationship betweeneducational level and user clusters (Tables 4 and 5). As Table 4shows, Traditionalists are most often primary education pupils(75.5%). Gamers (54.7%), Networkers (78.1%) and Producers(66.7%) can most often be found among secondary educationstudents.

Table 5 shows that primary education pupils are most often Tra-ditionalist (64.4%). In secondary (41.7%) and higher education(84.6%) the largest cluster is Gamer.

15. Join gamingfriends

When friends play a game, I want to play that game aswell

16. Playing outside I prefer playing outside to gaming17. TV, not

productionI prefer television because you don’t have to doanything

Answers on five-point Likert scale; 1 = completely disagree, 5 = completely agree.

3.2. Diversity in opinions about interactive media

We analyzed the respondents’ opinions about specific media, byapplying cluster analysis on the items as shown in Table 6a. The re-sult was a grouping together of opinions in four clusters (Table 6b).These clusters contain opinions about, respectively, the importanceof gaming, the importance of networking, gaming in-group behav-ior and active play. The ‘importance of gaming’ and ‘importance ofnetworking’ clusters contain opinions about feeling unhappy whenrespondents cannot use games or social software. The cluster‘game in-group behavior’ combines all in-group opinions charac-teristic for subcultures (cf. Brake, 1985). ‘Active play’ is formedby a preference for the Wii instead of non-physical games, togetherwith a preference for playing outside.

ANOVAs on the opinion clusters indicated that all user clustershave significantly different mean scores for all opinions, apart fromActive play: Importance of gaming, F(3, 153) = 18.34, p < .001;Importance of networking, F(3, 153) = 18.32, p < .001; Game in-group, F(3, 154) = 7.97, p < .001; Active play, F(3, 153) = .39,p = .76. These results are confirmed by Welch F-ratio, that alsoshowed a significant difference between all clusters, apart from Ac-tive play: Importance of gaming, F(3, 63.67) = 19.70, p < .001;Importance of networking, F(3, 64.67) = 18.85, p < .001; Game in-group, F(3, 63.14) = 14.70, p < .001; Active play, F(3, 62.34) = .42,p = .74.

Post hoc comparisons for ‘importance of gaming’ opinions usingthe Games–Howell test (p < .05) indicated that Traditionalists(M = 2.89, SD = .91) and Gamers (M = 3.17, SD = .79) do not signifi-cantly differ from each other on this opinion. The mean scores forNetworkers (M = 2.08, SD = .75) and Producers (M = 3.65, SD = .88)differed significantly from each other. For ‘importance of network-

ing’ opinions, post hoc tests showed a significant difference be-tween Traditionalists (M = 2.05, SD = .62) and Producers(M = 3.35, SD = .68) and a non-significant difference between Ga-mers (M = 2.46, SD = .70) and Networkers (M = 2.74, SD = .70). Posthoc test for opinions labeled ‘game in-group’ showed a significantmean score for Networkers (M = 1.20, SD = .39) and no significantdifferences for the other user clusters: Traditionalists (M = 1.80,SD = .92), Gamers (M = 1.86, SD = .86) and Producers (M = 2.32,SD = 1.13). Finally, post hoc tests for ‘active play’ showed no signif-icant differences on the mean scores for all user clusters: Tradition-alists (M = 3.51, SD = 1.23), Gamers (M = 3.33, SD = .85),Networkers (M = 3.47, SD = 1.02), Producers (M = 3.52, SD = .95).In line with the results for activities, membership of a certain usergroup appears to be related to types of opinions about specificinteractive media. Specifically, our results suggest that being a Net-worker influences opinions on games in a negative way. This holdsboth for opinions about importance of gaming and for wanting tobelong to the group of gamers. Furthermore, our results indicatethat Gamers and Networkers do not differ significantly from eachother on the stated importance of networking. These results indi-cate that the Gamers appear to have a more positive attitude to-wards networking than Networkers towards gaming. Being aProducer or Traditionalist appears to influence the opinion on theimportance of networking in a positive and a negative way, respec-tively. Finally, membership of a user cluster does not significantly

Table 6bMeans for opinions on media activities.

Opinion Clustertraditionalist

Gamer Networker Producer

Gaming 2.89 3.18 2.08 3.651. Gaming together7. Longer games13. Pretending14. Production15. Join gaming frnds

Networking 2.05 2.46 2.75 3.352. Longer MSN3. Unhappy no Hyves5. Unhappy no MSN6. Belong to group9. No-gaming friends17. TV, not production

Game in-group 1.80 1.86 1.21 2.328. Gamers group10. Important gaming11. Like gamers better4. Unhappy no games

Active play 3.51 3.33 3.47 3.5316. Playing outside12. Like Wii better

Answers on five-point Likert scale; 1 = completely disagree, 5 = completely agree.

2 Hyves, a website resembling Facebook, is the most popular social networking sitein the Netherlands at the time of inquiry.

A. Van den Beemt et al. / Computers in Human Behavior 26 (2010) 1158–1165 1163

influence one’s opinion on preferring the Wii and playing outside(‘Active play’).

Although analysis showed a grouping of activities in four clus-ters and a grouping of respondents in the clusters Traditionalist,Gamer, Networker and Producer, there appears to be a complexwhole of activity–user combinations, rather than a straightforwardone-to-one mapping of clusters. Instead of each cluster of usersengaging solely in their ‘own’ kind of activities, the means foractivities show that all groups engage in a number of browsingactivities as well. Furthermore, as Table 4 shows, Producers engagein performing and interchanging activities.

Each user cluster, together with the opinions expressed by itsmembers, forms a pattern of interactive media use. In order toget a clear view on these patterns, we will discuss each cluster interms of intensity of media use, differentiation on age and educa-tion level and acknowledged importance of media.

3.3. Traditionalists

The Traditionalists form one group of respondents. Theserespondents use the basic functionalities of interactive mediarather than web 2.0 applications. This might explain the large per-centage of primary education students. Members of the other clus-ters use applications such as MSN, e-mail or search engines as well,which is shown by the relatively high means for these activities.However, they do this together with activities related to theirown cluster of Networker, Gamer or Producer. The basic level ofinteractive media use corresponds to the finding that Traditional-ists on average report to have no strong opinions about mediaactivities (see Table 6b). The high means for the ‘Active Play’ opin-ions are the result of the primary education-students who statethat they prefer the Wii and playing outside to using a computer.As we can see from opinions 2 and 5 in Table 6b, of all the clusters,Traditionalists appear to be the least depending on MSN.

3.4. Gamers

Gamers engage in all gaming activities in the questionnaire.They can most often be found among secondary education stu-dents. Although Gamers, of all clusters, have the lowest means

on opinion 6 ‘I want to belong to a group’, they prefer playing to-gether with others. Gamers on average state that they do not feelunhappy if they cannot play games. However, there is a significantcorrelation (r = .41) between gaming together and feeling unhappywhen they cannot play games, indicating that the joy of gaminglies in its social aspect. The strongest correlation for opinions ongaming activities can be found between pretending and production(r = .63). This means that some gamers like games because you canpretend to be a superhero or a manager of a zoo. The correlationshows that they also like the production of (virtual) content ingames. One can think in this respect of obtaining weapons in roleplaying games or designing your car in racing games.

Gamers prefer to play large PC games or online games, which isshown by relatively high user means. Other clusters show highmeans for portable or casual games, which corresponds to the les-ser level of commitment needed for these games, compared to con-sole of PC games.

3.5. Networkers

Networkers make use of all kinds of social software. In our sam-ple this means mainly a simultaneous use of several Hyves2 func-tionalities and MSN. Profile sites, such as Hyves, are beingmaintained by two-thirds of all young people in secondary and high-er education. Most active are the secondary education students,while half of the primary education students report that they donot ‘own’ a profile page. This latter group consists mainly of 10-year-olds. Networkers are mainly found among secondary educationstudents in our sample.

Responding to pages of others is most often done by secondaryeducation students. Owners of profile pages spend more timemaintaining their own page than looking at or responding to pagesof others. However, for all educational levels there is a strong cor-relation (Pearson’s correlation > 0.7, p < .01) between having a pro-file page and looking at and responding to the pages of others.Networkers on average state that they are least unhappy whenthey cannot play games, which shows a difference in attitude be-tween Networkers and Gamers. This might be interpreted as a tra-ditional difference between boys and girls: while most of theGamers in our sample are boys, most Networkers are girls. Inter-estingly enough, Gamers have less negative opinions about net-working than Networkers have about gaming.

3.6. Producers

Producers form a cluster with very active users of many kinds ofinteractive media. Most Producers can be found in secondary edu-cation, and of all clusters they are on average most active withuploading photos, maintaining weblogs and downloading musicand films. An interesting result from our data is the intensive useby Producers of non-producer applications, especially social soft-ware as found in the ‘interchanging’ cluster. Producers combinethis with stronger opinions about these media than other userclusters. Producers come close to descriptions of the Net genera-tion by engaging in a wide range of content consuming and pro-ducing activities.

4. Discussion

In this paper we investigated the existence of patterns in youngpeople’s use of interactive media. Our results show a diversity ofuser patterns rather than a homogeneous use of interactive media.

1164 A. Van den Beemt et al. / Computers in Human Behavior 26 (2010) 1158–1165

We found groups of young people who reported making intensiveuse of interactive media. This intensity goes together with diversityin kinds of media and in opinions about specific media activities.

We distinguished four clusters of interactive media activitiesand labeled them following current literature: browsing, perform-ing, interchanging and authoring. These four clusters form onedimension of activities ranging from consumption (browsing) toproduction (authoring). Furthermore, we distinguished four clus-ters of interactive media users. We hypothesized these user clus-ters as different subcultures (Van den Beemt, Akkerman, &Simons, 2007) and labeled them, respectively, as ‘Traditionalists’,‘Gamers’, ‘Networkers’ and ‘Producers’. Each user cluster relatesto a specific activity cluster: Traditionalists to browsing, Gamersto performing, Networkers to interchanging and Producers toauthoring. However, most cluster members engage in other activ-ities as well (see Fig. 1). These user clusters form a second dimen-sion ranging from consumption (Traditionalists) to producing(Producers). In short, Traditionalists only make use of the basicfunctionalities of interactive media, that we labeled as ‘browsing’;Gamers appear to be content-driven participants, prefer playing to-gether and enjoy the production and pretending aspects of games;Networkers appear to be friendship-driven, focused on communi-cation with peers and combine the use of their profile pages withMSN; Producers appear to come closest to the Net generation witha diverse and intensive use of many interactive media for both pro-duction and interactive consumption of content, including mediatypical for the other user clusters. Producers appear to be both con-tent-driven and friendship-driven participants.

Because our current sample was not drawn randomly, some re-sults indicate a need for further research with a larger, more repre-sentative group of respondents. For instance, our results show ahigh percentage of Gamers among higher education students. Thiscould be explained by the large number of males among the highereducation respondents. The same applies to the large percentage ofnetworking girls on secondary education level. Both results appearto be a gender issue caused by the sample characteristics, ratherthan an effect explained by educational level. Educational leveland gender appeared to significantly influence the respondents’opinions about specific media. This suggests a need for multilevelanalysis with a larger sample in order to explain these macro levelinteractions.

The intensive use of and diversity in kinds of interactive mediaconfirm the findings of the small number of studies available onyoung people’s use of interactive media. The data in our sampleshow that we cannot speak of a generation whose members useall media, all in the same way. Some media, such as MSN or Hyvesare widely used, but with different levels of intensity and having

Fig. 1. Associations between clusters of activities and clusters of users.

different meaning to the users. This diversity implies caution indrawing conclusions about interactive media and young people,especially when interactive media and education are concerned.The small percentage of Producers among the respondents indi-cates that, although most of today’s youngsters engage in tradi-tional activities, not all of them are active with interactive mediaproduction. By result it is not self-evident that all students’ learn-ing improves by using convergence media such as videosite Youtube, photosite Flickr or social networking space Facebook.

The results of this study can be seen as indicators for contempo-rary youth culture. By describing a classification of both activitiesand users, we added two dimensions to the Net generation debate.These two dimensions allow us to describe the use of interactivemedia by young people in a nuanced way. Given the intensity ofinteractive media use and the expressed opinions, these mediaform an important part of young people’s social space and culturalspace (Author, 2007). Where the Net generation debate often fo-cuses on the use of interactive media, or rather young people’s so-cial space, we emphasize the importance of investigating this inrelation to young people’s cultural space. By investigating opinionsabout interactive media, we have taken a first step in describingthis cultural space.

Acknowledgements

The authors thank David Shaffer, Bob Wilkinson and GabriHeinrichs for useful comments on earlier versions of this paper.We also thank the participating schools for their enormous contri-bution to our study.

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