BRANDTZAEG Towards a Unified Media-User Typology MUT

8/19/2019 BRANDTZAEG Towards a Unified Media-User Typology MUT

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Towards a unified Media-User Typology (MUT): A meta-analysis and review

of the research literature on media-user typologies

Petter Bae Brandtzæg *

SINTEF ICT, Department for Cooperative and Trusted Systems, P.O. Box 124 – Blindern, N-0314 Oslo, Norway

a r t i c l e i n f o

Article history:Available online 09 March 2010

Keywords:

Human–Computer Interaction

Media behaviour

User typology

User needs

Internet

Digital divide

a b s t r a c t

Considering the increasingly complex media landscape and diversity of use, it is important to establish acommon ground for identifying and describing the variety of ways in which people use new media tech-

nologies. Characterising the nature of media-user behaviour and distinctive user types is challenging and

the literature offers little guidance in this regard. Hence, the present research aims to classify diverse user

behaviours into meaningful categories of user types, according to the frequency of use, variety of use and

content preferences. To reach a common framework, a review of the relevant research was conducted. An

overview and meta-analysis of the literature (22 studies) regarding user typology was established and

analysed with reference to (1) method, (2) theory, (3) media platform, (4) context and year, and (5) user

types. Based on this examination, a unified Media-User Typology (MUT) is suggested. This initial MUT

goes beyond the current research literature, by unifying all the existing and various user type models.

A common MUT model can help the Human–Computer Interaction community to better understand both

the typical users and the diversification of media-usage patterns more qualitatively. Developers of media

systems can match the users’ preferences more precisely based on an MUT, in addition to identifying the

target groups in the developing process. Finally, an MUT will allow a more nuanced approach when inves-

tigating the association between media usage and social implications such as the digital divide.

2010 Elsevier Ltd. All rights reserved.

1. Introduction

A key to success in Human–Computer Interaction (HCI) re-

search and media development is understanding media behaviour,

to see how to reach the user population in the coming years. From

a methodological point of view, it becomes crucial to be able to

empirically distinguish and measure different types of media use,

in order to enable a more precise analysis of media behaviour;

‘‘How is this person using new media technologies?” However,

none of the existing theories or models in HCI is concerned with

the actual user behaviour, but rather with the requirements, moti-

vations and gratifications related to media usage. Without a firmtheoretical grounding and unified practice of our understanding

of media behaviour, it could be argued that research into media

behaviour is not governed by a framework of principles that sup-

port systematic or rigorous measurements. This situation makes

the existing understanding and methods of media behaviour shal-

low and measurements of media usage random.

An understanding of user behaviour is very difficult to achieve

because media usage is often dynamic and complex, and we are

now in a period of extremely rapid media evolution. Over the last

ten years, the media-user segment has also become more frag-

mented because of the entry of various demographics into the

new media market, particularly with the introduction of the Inter-

net. In parallel, the new media landscape has become more en-

riched and complex due to the arrival of multiple television

channels, mobile technologies, electronic games, a variety of online

services, supplemented with an increasing media convergence

(Heim, Brandtzæg, Endestad, Kaare, & Torgersen, 2007; Mannheim

& Belanger, 2007; Ortega Egea, Menéndez & González, 2007).1 With

increasing access to a variety of new media and more content to

choose from, individual preferences and lifestyles are becoming

more important (Brandtzæg & Heim, 2009; Johnsson-Smaragdi,2001; Swinyard & Smith, 2003). These pervasive variations in media

behaviour suggest an important and relatively underdeveloped re-

search stream for the discipline of HCI research. Moreover, it is cru-

cial to achieve a common framework with some basic criteria to

understand media behaviour in the same way.

Similar to media behaviour, consumer behaviour is also com-

plex. Accordingly, marketers and product developers find it

0747-5632/$ - see front matter 2010 Elsevier Ltd. All rights reserved.doi:10.1016/j.chb.2010.02.008

* Tel.: +47 92 80 65 46; fax: +47 22 06 73 50.

E-mail address: [email protected]

1 Difficulties in understanding media-usage behaviour have also arisen because of

the ‘‘technology that have blurred the line between public and private communica-

tion and mass and interpersonal communication” (McQuail, 2000, p. 16). The media

users also evolve from being passive consumers of mainstream media to taking an

active role in the new media chain (Obrist, Geerts, Brandtzæg, & Tscheligi, 2008).

Computers in Human Behavior 26 (2010) 940–956

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Computers in Human Behavior

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increasingly important to target specific groups by grouping users

according to their similarities, often referred to as ‘‘market or cus-

tomer segmentation”, in an attempt to understand user’s behav-

iour (Assael, 2004). Buyers face many sets of issues and their

behaviours are confounded by various situational factors. Evidence

suggests, however, that a basic logic or structure underlies con-

sumer behaviour. According to Bunn (1993), different taxonomies

and classifications have been valuable in developing a basic under-standing of consumer behaviour. In the social sciences, typologies

also are used to organize complex behaviour into characteristic

patterns or types. Such classification permits exploration of the

nature and consequences of different types ( Johnson & Kulpa,

2007).

For scholars, description and classification play fundamental

roles in the development of a discipline (Hunt, 1983). However,

characterising both the nature of media use and the distinctive

user types is challenging, and the existing literature offers little

guidance here (Livingstone & Helsper, 2007). Hence, this article

claims that the HCI research community can learn from cus-

tomer-segmentation studies used for the general identification of

media-user profiles. Several subsets of data, with reference to the

user, total duration of time consumed, content-category prefer-

ences and interaction with diverse media, have all been shown to

play a role in accounting for the variations in the breadth and

depth of media use (Heim et al., 2007; Livingstone & Helsper,

2007; Zillien & Hargittai, 2009). A more nuanced approach in the

understanding of users and their gradations in terms of media

usage will allow a better understanding of the typical media users

and will contribute to the development of user-centred media and

further support future studies that focus on the social implications

of various media behaviour.

In HCI, an understanding of both the users and the interaction

between user and the media is very important. One central aspect

of the system-development process is to accurately understand

user needs (Blanchard & Fabrycky, 2006). Hence, HCI research

has, for example, been using personas – well-known descriptions

of user archetypes – as a guide in the design process. A weaknessof these archetypes is that they are a no valid method for describ-

ing media behaviour in general. These personas are only based on

data about well-known user groups for a particular technology or

system, rather than being a broad empirical description of new

media users, in general (Herman, Niedermann, Peissner, Henke &

Naumann, 2007). An examination of an individual technology does

not enable us to grasp the complex interactive possibilities which

users take advantage of when they use media technologies. A per-

sona approach does not give information about general user pref-

erences or skills. According to the prevailing multiple dimensions

of the media, knowledge about users should be derived by combin-

ing several segmentation variables rather than by relying on a sin-

gle data base.

Previous research on media behaviour using quantitative meth-ods has mainly focused on how many people use new media and

how frequently they use them. One example of this one-dimen-

sional focus on media behaviour may be seen in the available sta-

tistics on Internet use and the number of broadband connections

used in households and by individuals (e.g. ICT, Eurostat). Studies

tend to oversimplify media behaviour by reporting how many

(such as, ‘‘350 million use Facebook”) or how frequently people

use media (e.g. Losh, 2003) as opposed to the patterns of use,

neglecting the fact that individuals have very different patterns

of use (Brandtzæg & Heim, 2009; Shah, Kwak, & Holbert, 2001).

Moreover, measures that focus on time use of Internet only tend

to homogenize highly disparate activities, overlooking crucial qual-

itative differences within the Internet usage. Therefore, researchers

have started arguing that beyond the binary differentiation of usersversus non-users lie variations in how people use new media

(Livingstone & Helsper, 2007; Zillien & Hargittai, 2009). Conse-

quently, ‘‘the research task has shifted to that of capturing the

range and quality of use, transcending simple binaries of access/

no-access or use/non-use” (Livingstone & Hepster, 2007, p. 674).

Trying to determine whether qualitative differences exist

among users just by looking at the quantity of people using media

is insufficient. It is like trying to ‘‘determine how many people can

drive a car simply by asking if they have ever sat in one” (Lamb,2005 n.p.). Such an approach misses important multidimensional

information about people’s media skills and preferences. In addi-

tion, user surveys and statistics often overlook the important fact

that citizens use different media platforms and services in essen-

tially systematic patterns (Heim et al., 2007). This problem is exac-

erbated by choosing to describe the population using only (or

mainly) the dimensions of gender and age (e.g. Losh, 2003) which

may yield oversimplified explanations of why and how citizens use

media and how they are affected by media technology. And, as

society becomes increasingly media saturated, the importance of

demographic traits is said to have less explanatory power (Kor-

gaonkar & Wolin, 1999).

Little is known about user classification or existing typical user

groups according to the patterns of media use. However, some HCI-

related studies have taken a step in this direction by applying a

media user–typology approach similar to that in market-segmenta-

tion studies ( Johnson & Kulpa, 2007). Unfortunately, the existing

body of research still lacks a common basis for (a) identifying

and describing the variety of ways in which people use new media

and (b) classifying these differences into meaningful categories of

user types.

Currently, HCI researchers and practitioners are confronted

with a wide choice of a multitude of models that describe overall

media use and apply different user typologies. Yet, without a com-

mon ground with respect to constructs and typology, it is impossi-

ble to carry out a research programme that builds up on previous

research. To address these limitations, there is a need for reviewing

the existing research by comparing different media-user typologies

and models with each other. With this in mind, this article pro-vides a unified, comprehensive user typology that constitutes dis-

tinct forms of media behaviour. By illuminating media use more

completely, some typical dimensions of media-usage behaviour

can be determined.

The complexity of the media and inequalities among media

users indicates a need for researchers to develop more sophisti-

cated and nuanced accounts of how people use media (Selwyn,

Gorard, & Furlong, 2005). A prerequisite for such research is the

development of a common typology of users. Notwithstanding an

increasing interest in media-user typologies identified in this re-

view, the research literature still lacks an overview of the charac-

terisation media behaviour in general, and different media-user

types in particular.

In this article, we will use the following definitions:

s The term new media is defined by Rice (1984) as the communi-

cation technologies that enable or facilitate interactivity both

between user and user and between users and information. This

article mainly focuses on television, mobile phones, computers,

game consoles and the Internet.

s Media behaviour is defined here as the totality of human behav-

iour in relation to new media use, including both differentiated

levels of participation (frequency of use) and content/activity

preferences in media usage (forms of use).

s The term user typology is defined as a categorisation of users

into distinct user types that describes the various ways in which

individuals use different media, reflecting a varying amount of

activity/content preferences, frequency of use and variety of use. In general, ‘‘typologies divide individuals or objects into

P.B. Brandtzæg/ Computers in Human Behavior 26 (2010) 940–956 941



groups according to typical behavioural or other patterns and

thus contribute to a clearer view of a diverse and confusing

number of individuals or objects” (Barnes, Bauer, Neumann, &

Huber, 2007). A typology in this article will hence be the deter-

mination of how different patterns of media behaviour are

linked to different user types.

1.1. Objectives

The objectives of the current article are listed below. The overall

objective is to review the existing literature on user typologies of

media behaviour to formulate a unified model of Media-User Typol-

ogy (MUT). This MUT is a first step that unifies and defines the sali-

ent features of complex media behaviour and explicitly suggests

the different categories of users on the basis of the various ways

in which different individuals use media. The initial MUT consists

of eight different user types, reflecting various types of media

behaviour (see for instance Table 6.).

The sub-goals are as follows:

(1) Review the most fundamental research literature on user typol-

ogies intended to reflect the different patterns of media behav-

iour. The primary purpose of the review is to analyse and

assess the current state of knowledge with reference to user

types for new media. It identifies 22 studies and discusses

the similarities and differences in the user typologies that

they use. To the best of the authors’ knowledge, nobody

has yet completed a comparative meta-analysis of different

user typologies; therefore, this review is the first to assess

similarities and differences across such a large body of

empirical material. The comparison of these studies and

typologies will form the basis for the development of a uni-

fied user typology of media behaviour.

(2) Identify the most prominent and relevant theoretical models

that can add value and clarify media behaviour in terms of user

typology. This review is based on an examination of the rel-

evant research literature and the theories used therein. Thesimilarities and differences among the theories are charted

and how the theories can benefit from an approach that

applies a user typology is determined.

1.2. Contributions

An MUT for classifying a media-user typology can contribute to

the following goals:

o First, an MUT will provide a more precise approach for the HCI

community to understand and identify users and to measure

the heterogeneity of media behaviour. Incomplete surveys and

measures have made it difficult to determine the qualitative dif-

ferences among users in the context of new and complex medialandscapes (Zillien & Hargittai, 2009). A typology will allow bet-

ter measures of media behaviour. A user typology describing

the construction of a sub-group or user type, based on user

activity; preferences/content selection; and the frequency and

variety of use will not only contribute to a clearer view of

diverse media behaviour (e.g. Barnes et al., 2007), but will also

indicate how people differ in their digital competence and how

this might develop over time.

o Second, an MUT can help developers of new media to better

match different services to various types of users and to better

understand the differences related to participation inequality in

terms of the digital divide. Furthermore, public services can

promote services that target different user groups. There is to

day a risk that increasing media and Internet exacerbate ratherthan reduce inequalities (Livingstone & Helsper, 2007). Inclu-

sion of information communication technologies (ICT) is one

of the most important topics on the European Union’s EU

2020 main policy agenda to maximise the societal benefit of

ICT usage and to provide better services to citizens (Europa,

2009). HCI researchers and media developers should with a user

typology consider the different user classifications to match dif-

ferent users’ needs.

o Third, an MUT will allow HCI designers and requirement engi-neers to identify and differentiate media behaviour and the user

types in a given media environment. Hence, an MUT will help

the HCI community better understand the fragmentation of

the user population better and develop some common and

reusable personas, thus including the entire diversity of users

and their requirements. A user behaviour analysis based on a

MUT will help extracting common behaviours, estimating user

demands among different user types and identifying objects

on user behaviour scenarios. This will enable a dynamic and

more efficient support for the multiple needs of the user.

o Fourth, to foster growth in the adoption of new media, an MUT

should be able to capture and include both larger and hidden

potential media-adoption groups (Ortega Egea et al., 2007;

Swinyard & Smith, 2003).

o Fifth, an MUT can serve as an initial integrated model and a

template for further research and model development, includ-

ing the further integration and organisation of media behaviour.

o Sixth, an MUT will assist scholars in their understanding of the

social implications of different types of use as an independent

variable or make predictions regarding how diverse user groups

are likely to respond to different forms of media usage. Hence, a

typology may also support the formulation of testable hypoth-

eses regarding the behaviour of distinct media-user types and

how various types of behaviour may link to certain forms of

social implications. For instance, specific types of Internet usage

are found to be linked with the production of social capital

(Shah et al., 2001) and social inequality (Zillien & Hargittai,

2009).

o The meta-analysis and review in this article will also contributeto a better understanding of the changing patterns of user types

from 2000 to 2009 and to the analysis on whether there have

been any noticeable and interesting changes in media behav-

iour over the years.

2. Methods

The review includes a search and meta-analysis of the relevant

research literature.

2.1. Literature search and scope

The objective is to review the state-of-the-art research pub-lished after the year 2000 that is relevant for user types reflecting

distinct patterns of media usage. The following academic archives

were searched: the ISI Web of Knowledge, Springer, ScienceDi-

rect, and the digital library of the Association for Computing

Machinery (ACM). Furthermore, both the general and the scholarly

search engines, Google and Google Scholar, respectively, were used

for the search. The following search terms were used: ‘‘Media-user

typology”, ‘‘media-use typologies”, ‘‘media use”, ‘‘media usage”,

‘‘patterns of media use”, ‘‘user types”, ‘‘user styles”, ‘‘media-user

styles”, ‘‘user profiles”, ‘‘user segments”, ‘‘user stereotypes”, ‘‘user

models”, ‘‘audience typologies”, ‘‘audience usage”, ‘‘audience pro-

files”, ‘‘community typology”, ‘‘online community typology” and

‘‘social networking typology”. The searches were carried out in

February, March, and April of the year 2008 and a follow-up searchwas conducted in October 2009. Furthermore, some main articles

942 P.B. Brandtzæg/ Computers in Human Behavior 26 (2010) 940–956



( Johnson & Kulpa, 2007; Livingstone & Helsper, 2007; Shih and

Venkatesh; 2004) and their reference lists were searched to look

up relevant empirical work.

To limit the scope of the search, we did not include user-gaming

typologies that had been developed in relation to behaviours in

electronic gaming (e.g. Farquhar & Meeds, 2007). The criterion

used for the search was that the studies should provide a classifi-

cation of users based on their media behaviour and/or patternsof media usage. The focus was also mainly on academic work be-

cause there are very few marketing reports that show all the data

needed to assess the validity of their work.

This review had a particular focus on research that approached

new media use in general (television, computers, Internet, different

game consoles, mobile phones), the Internet in general, or particu-

lar Internet services such as online communities or social network-

ing sites (SNSs). SNSs were included because it is widely agreed

that such social services are capable of providing both interper-

sonal interactions and access to information, education, and enter-

tainment and thus might reflect different SNS-user types

(Brandtzæg & Heim, 2008).

2.2. Meta-analysis

A meta-analysis of the following factors was conducted to iden-

tify the common and diverging viewpoints of media-user types, so

that an MUT could be developed:

(1) Theoretical approach used; (2) the methodology and re-

search design; (3) the year of publishing; (4) context of study;

(5) the user types identified; (6) the media platform or services

investigated; and (7) the user behaviours that constitutes the dif-

ferent typologies. One researcher carried out this analysis by read-

ing the complete text of all the reviewed studies. In detail, the

analysis involved the following:

Analysis 1: comparing prominent research theories and how they

apply to the media-user-typology approach, in addition to theirrelevance in explaining user types, using factors 1 and 6.

Analysis 2: studying the type of method and the purpose of the

different studies to achieve a common or best-practice mode

to identify a user typology, using factors 2, 5 and 6.

Analysis 3: acquiring knowledge of how different user types have

changed over the previous few years, using factors 3, 5, 6 and 7. Analysis 4: gaining insight into the context or how different user

types vary across countries, using factors 4 and 5.

Analysis 5: comparing the different typologies that were identi-

fied to determine a common basis for a unified MUT, using fac-

tors 5 and 6. Analysis 6: identifying the different platforms that user types

considered, using factors 5 and 6.

Analysis 7: comparing previous user typologies in terms of themedia behaviour identified and gaining insight into the dimen-

sions that are used to identify a typology, using mainly factors 6

and 7.

3. Analyses and results

A search of the literature yielded 22 studies. This included two

recently published reports from the United States (US) and one

from the United Kingdom (UK), two conference papers, two book

chapters, one online column and 14 articles published in peer-re-

viewed journals.

Table 1 gives an overview of all the 22 studies in terms of the

following characteristics to facilitate comparison and, thereby,the proposed meta-analysis:

The authors of the different studies and the year in which the

research was published.

Research design, including sample size, age and level of the rep-

resentative sample, method used, and the types of new media

that were researched. Media are classified as general media

(including different media such as mobile phones, television,

computers, the Internet and games), Internet in general (Internet

use in general), online shopping (online shopping platform) andsocial networking (social media platform). Theory: the particular theory that was applied in the study .

Context: the particular country/countries included in the user

study.

User typology: the specific media-user typology developed or

media-usage pattern identified.

Year of publication: to see how user typologies have developed

from 2000 to 2009, the studies were organised chronologically

with reference to the year published, starting with the newest

study first.

The studies reported in Table 1 show that media behaviour is

varied. Different user groups across different media platforms typ-

ically use media in a variety of ways. This pervasive variation in

media behaviour suggests an important and relatively underdevel-

oped line of research involving the discipline of audience or user

research – comprehensively describing the variety of ways in

which people use media, and classifying these ways into meaning-

ful categories.

The following sections analyse the results from Tables 1 and 2.

3.1. Analysis 1 – Theories

A typology based on the categorisation of people is not a new

theoretical approach. In psychology, personality types have been

in use for many years ( John & Srivastava, 1999; Jung, 1971). How-

ever, typologies in general ‘‘reflect theoretical assumptions about,

and conceptual organisation of, the salient features of complex

behaviour” ( Johnson & Kulpa, 2007, p. 773).Most of the studies reviewed in this article do not apply any

theory or test hypotheses (see Table 1). This is principally because

they use an explorative approach to gain new insights into the dif-

ferences in user behaviour. Four articles approach media-user

types by applying the diffusion of innovation theory (Rogers,

2003), mainly linking it to categories of adaptors, as shown in Ta-

ble 2. Other studies rely on personality theories or consumer typol-

ogies/segmentation, the Uses and Gratifications (U&G) theory and

technology-acceptance models. In addition, some studies reviewed

herein attempt to develop test instruments for describing user

types (e.g. DeYoung & Spence, 2004; Johnson & Kulpa, 2007).

Table 2 provides an overview of the theoretical approaches that

have been used to understand user typologies. All theories focus on

the individual level of explanation.The different theories outlined in Table 2 are of little use to

achieve a general understanding of various media behaviour from

a typological point of view. This because the theories focus on (a)

individual reactions to, (b) gratifications derived from media usage

or/and (c) intentions to use media technologies by examining the

determinants of technology adoption and usage by individual

users. For example, according to Davis (1989) and the Technology

Acceptance Model, the core constructs ‘‘perceived usefulness” and

‘‘perceived ease of use” influence ‘‘behavioural intention” which af-

fects actual usage or media behaviour. Similarly, the U&G approach

focus on why people use new media, or what gratifications users

derive from media, instead of how people use it ( Jensen & Rosen-

gren, 1990).

U&G theory is often been misinterpreted as a framework thatexplains ‘‘how people use media”. But U&G theory is mainly a




Table 1

An overview of 22 media user typologies from the year of 2000. Analysed with reference to (1) method/sample, (2) theory, (3) media platform, (4) context and year, and (5) user

types.

References Research design/media/context User typologies

(1) OFCOM (2008) Data from the UK among 39 social networking site

users and 13 non-users, including both children and

adults

In-depth interviews

Online usage of community/social networking sites Qualitative in-depth analysis

No theory

Context: UK

(1) Alpha socializers – regular users who use social networking sites

often, but for short bursts to flirt and meet new people

(2) Attention seekers – people whocrave attentionand comments from

others, often by posting snapshots of themselves and friends

(3) Followers – people who join sites to keep up with what theirpeers are doing

(4) Faithfuls – people who wish to rekindle old friendships, often

from school or university

(5) Functionals – people who log on for a particular purpose, such as

looking for music and bands

(2) Brandtzæg and

Heim (2010)

Data from four social networking sites in Norway in

2007 (N 5233), median age of 16 years

Online survey questionnaire with both open-ended

responses and fixed responses

Social networking sites

Cluster analyses and qualitative analyses

Mainly explorative study, but use also Kozinets

(1999) and Rogers (2003), diffusion model

Context: Norway

(1) Sporadics (19%)refers touserswhogivefewreasonsfor visitingthe

community. These users are not very involved in activities, but

rathervisitthe communitysporadically to checktheirstatus from

time to time

(2) Lurkers (27%) are the largest user group and they use online com-

munities mainly to kill time. They engage in several activities,

but to a small degree

(3) Socializers (25%) use online communities mainly for communica-

tion or ‘‘small talk” with others. The main reason for visiting the

community is to socialize with others. Typical for teenage girls

(4) Debaters (11%) are highly involved in discussions, reading, and

writing contributions in general(5) Actives (18%) engage in all kinds of activities within the commu-

nity, including the production of user-generated content (UGC)

(3) Ortega Egea

et al. (2007)

Representative data, of the population aged 15 and

over. European countries (EU15). Sample size:

30,336 respondents

Survey questionnaire (telephone)

Internet usage

Cluster analysis

Theory: Rogers’ Diffusion of Innovations Model and

the Technology Acceptance Model (TAM)

Context: EU

(1) Laggards (16%) useInternetservicesinfrequently anddonot usee-Gov-

ernment services. They rarely use the Internet for private purposes

(2) Confused and Adverse (2%) high variability. Confusion about Inter-

net services, give Don’t Know or No Answer as an answer very

often. Rarely use the Internet for private purposes or for contact-

ing e-Government

(3) Advanced Users (16%) use e-Government services frequently, not

onlyfor administrative tasks(e.g., to searchfor administrativeinfor-

mation, to complete forms, or to carry out administrative transac-

tions). These users are the most frequent online shoppers and are

in sum the most frequentusers with the mostvaried usage pattern

(4) Followers (19%) use the Internet quite frequently, but not on a

daily basis. Use e-Government services, although not as fre-

quently as Advanced Users. They do not shop online

(5) Non-Internet Users (44%) do not use the InternetThe identifiedInternet user typologies show clear differences with regard to

country, sex, occupation, education, and location

(4) Johnson and

Kulpa (2007)

Sample of US college students

Survey questionnaire

Internet usage

Factor analysis

Theory: theory of personality (behaviour typology)

and sociability (e.g. Herring, 2004)

Context: US

(1) Sociability, Internet use related to social behaviour (human con-

nection motives)

(2) Utility, typically instrumental usage and goal orientation towards

utility (efficiency orientation)

(3) Reciprocity, the extent to which online behaviour is characterised

by cognitive stimulation and active involvementThis research

was a first step towards a Brief Test of Online Behaviour (BTOB),

suggested by the following dimensions above

(5) Horrigan (2007) Representative sample (18+): US

Questionnaire Survey (telephone)

Internet and mobile phones

Cluster analysis

No theory/explorative study

Context: US

Elite users (31%) (four groups) are heavy and frequent users of the Internet

and mobile phones and are using user-generated content

(1) Omnivores (8%) arethe most activeparticipants in theinformation

society, consuming information goods and services at a high rate

and using them as a platform for participation and self expression(2) The Connectors (7%) participate actively and use the Internet to

connect with people and to access digital content. Use mobile

devices

(3) Lacklustre Veterans (8%) are not passionate about the abundance

of modern ICTs. Most see gadgets as intruding into their lives

not many see ICTs adding to their personal productivity

(4) Productivity Enhancers (8%) get a lot of things done with ICT, both

at home and at workMiddle-of-the-road users (20%) (two groups)

are task-oriented. They use ICTs for communication more than

they use them for self expression.(5) Mobile Centrics (10%) are attached to their mobile phones and

take advantage of a range of mobile applications

(6) Connected but Hassled (10%) invest in a lot of technology, but get-

ting connected is a hassle for themUsers with few technology

assets (49%) (four groups) keep modern gadgetry at or near the

periphery of their daily lives. Some find it useful, some do not,

while others only use land-line telephones and television




Table 1 (continued)


(7) Inexperienced Experimenters (8%) have less ICT access and fewer

ICT skills, and might do more with ICT if they had more access

and developed more skills

(8) Light butSatisfiedusers (15%) know thebasics ofICT access, butuse

itinfrequently andit does notforman importantpartof theirlives

(9) Indifferents (11%) have fair ICT access, but it does not play a cen-

tral role in their daily lives(10) Off the Net users (15%) are mainly older Americans and are off the

modern information network

(6) Heim and Brandtzæg

(2007)

Representative sample of the population (15+) in

Austria, Germany and Norway

Questionnaire survey (Eurostat/telephone)

ICT/new media in general

Cluster analysis

No theory/explorative study

Context: Germany, Austria and Norway

(1) Non-users (Austria, 47%; Germany, 39%; Norway, 25%) are the

largest group. They spend no time with PCs and the Internet.

The majority 45 years +, have low income and education, have

few persons in the household and low access to ICT

(2) Average users (Austria, 27%; Germany, 51%; Norway, 27%) are the

largest group of ICT users. They do not use ICT on a regular basis

and have poor computer skills

(3) Instrumental users (Austria, 15%; Germany, 5%; Norway, 23%) use

ICT for mainly for practical purposes, for example information

acquisition and e-Government services. They have low entertain-

ment usage, a high score on PC and Internet usage in general,

good ICT access, and a higher education level. This group contains

more men than women

(4) Entertainment users (Austria, 9%; Germany, 5%; Norway, 14%),

spend a lot of time on gaming and advanced usage (see point5). They score high on PC and Internet use in general. There is

a great deal of variation in both education level and income. They

have good ICT access. They are younger than the average ICT user

(though this is not so evident in Germany) and more of them are

men than women

(5) Advanced users (only in Norway, 11%), spend most of their time

on media, using a wide range of different ICTs for a number of

purposes; including programming, downloading, and homepage

design. There is a high variation in both education level and

income. Most of them are younger males (80%). Broadband, live

in urban district. This user group is only evident in Norway

(7) Li, Bernoff,

Fiorentino, and

Glass (2007)

Representative sample for the US online population

and European online population

Questionnaire survey

Internet usage, mainly social computing

Cluster analysis

No theory/descriptive study

Context: US and Europe

Below, the U.S. percentage is given first, then the European

(1) Creators (13%/10%): publish blogs, create and maintain their own

web pages, or upload videos to sites such as YouTube at least

once per month. Younger than the average population

(2) Critics (19%/19%): select and choose media content for utility,several years older than the group of creators

(3) Collectors (15%/9%): save URLs on social book-marking services

(e.g. del.icio.us) or RSS feeds on Bloglines. This group is the most

male-dominated among the user groups

(4) Joiners (19%/13%):usingsocial networkingsites,such as MySpace.-

com or Facebook. Youngest of the social technographics groups

(5) Spectators (33%/40%): read blogs, view videos, and listen to pod-

casts, They are an important audience for UGC. More likely to be

women and to be from a lower-income group

(6) Inactives (52%/53%): do not participate at all in social computing

activities, have an average age of 50, are more likely to be

women, and are less likely to consider themselves leaders

(8) Barnes et al. (2007) A sample of 1011online shoppingusers from France,

Germany and the US. The average age was 36years

within a range from 14 to 82 years.

Online survey Questionnaire, on banners

Media: Online shopping applications Cluster analysis

Theory: consumer typologies and personality

constructs in psychology, extraversion and neuroticism

Context: France, Germany and the US

(1) Risk-averse doubters (15.2%) have low values for shopping plea-

sure and are critical of online-shopping

(2) Open-minded online shoppers (39.6%) are very open to new things

(‘‘extraversion”). They show the lowest perceived risk when shop-

pingonline andat thesame timethe highesttrust in online vendors(3) Reserved information seekers (45.2%) are typically careful and

reserved.They havea relativelyhighperceivedriskwhen shopping

online, but a positive attitude towards it. In general, this cluster is

generally open to purchasing over the Internet.These clusters are

separated especially by the following constructs: neuroticism,

willingness to buy, and shopping pleasureDifferences between

the countries; for example, the percentage of people categorised

as risk-averse doubters in France was as high as 66.2%

(9) Heim et al. (2007) Representative sample Oslo (age: 10–12)


New media in general (Internet, gaming, mobile phones)

Factor analysis

Theory: psychosocial factors

Context: Norway

(1) Communication usage, used mainly Internet for chat, email, gam-

ing online with others

(2) Entertainment usage, watching television, DVD, electronic gaming

(particularly console gaming)

(3) Advanced usage, downloading, programming, and drawing on the

computer (mainly boys)

(4) Gameboy usage, younger boys used Gameboy in particular

(5) Utilityusage, lookingforinformationon theInternetforschool, doing

schoolworkon thecomputer,andwritingandreading email(mainlygirls)

(continued on next page)




Table 1 (continued)


(10) Livingstone and

Helsper (2007)

Representative sample UK (age: 9–19)


Internet opportunities

Descriptive analysis, gradations in

frequency and opportunities of use

Theory: digital divideContext: UK

(1) Basic users (16%) centres on information seeking. Take only up 1–

3 online opportunities and is seen as the first step for everyone

using Internet

(2) Moderate users (29%) takes up 4–5 opportunities and are likely to

use the Internet for information, communication and entertain-

ment, and is viewed as the second step on the Internet ladder(3) Broad users (27%) takes up 6–7 opportunities and adds inn instant

messaging and downloading music. The third step on the Inter-

net ladder

(4) All-round users (27%) adds in a wide range of interactive and cre-

ative users and take up at least eight opportunities online. The

fourth and last step on the Internet ladderThe categories above

define usage as in terms of type and frequency of online opportu-

nities. The authors explain that going online is a staged process,

with systematic differences and that all users begins as Basic

users

(11) Nielsen (2006) Analysed usage on particular

user-generated content sites (UGC)

User statistics

Particular UGC sites

Descriptive, informal study (not an academic

publication, but high impact)

No theory/explorative studyContext: User-generated websites

(not geographically bounded)

(5) Lurkers (90%) read or observe, but do not contribute

(6) Sporadic contributors (9%) contribute from time to time, but other

priorities dominate their time

(7) Active participants (1%) are active users who account for most

contributions and systems activityThe categories above define

user participation according to a” 90-9-1 rule” suggested by Niel-

sen (2006). One example is participation in Wikipedia, where99% of the users is said to not contribute with content, while only

0.2% is doing this. Another example from Nielsen (2006) is that

among the 1.1 billion Internet users, only 55 million (5%) have

weblogs, and that only 0.1% of users post daily

(12) Jepsen (2006) Four Danish newsgroups on the Internet

Online survey questionnaire

Usage of online communities

Segments assigned on the basis of the median and

mean score

Theory: Kozinets (1999) framework for segmenting

participation in a virtual community

Context: Denmark

(1) Insiders have strong ties to the other members of the community.

Have also strong interests in the consumption activity, which is

central to the user’s self-image or social identity

(2) Devotees, maintain a strong interest in consumption, but have

few social attachments

(3) Minglers maintain strong social ties while being marginally inter-

ested in consumption activity

(4) Tourists have ties neither to the content, nor to other people in

the community. They simply drop by the community every

now and again with only superficial interest and few social ties

(13) Brandtzæg,

Heim, Kaare, T.,

and L. (2005)

Representative sample for the capital of Oslo

(7–12 years)

Questionnaire survey (paper in classroom) New media in general (Internet , gaming, mobile phones)

Cluster analysis

Mainly explorative study, but also uses digital divide

theory

Context: Norway

(1) Non-users (40%) spend almost no time with PCs and the Internet.

Mostly girls (75%) and younger children

(2) Advanced users (12%) spend the most time on media in general,using a wide range of different media technologies for a number

of different purposes, including advanced usage such as pro-

gramming and homepage design. Mostly boys (66%)

(3) Entertainment users (25%) primarily play console games and

watch television. Mostly boys (74%)

(4) Utility users (23%) use the Internet for information acquisition,

email, and schoolwork. Watch less television than others. Girls

(54%) and boys (46%) in this group

(14) Selwyn et al. (2005) Four regions in the west of England and South Wales,

representative for population density, economic activity,

and education

Household questionnaire survey of 1001 adults with 100

in-depth follow-up interviews

Internet usage

Frequency analysis

Theory: digital divide and Howard et al.’s (2001) user

typologyContext: UK

(1) Broad frequent users (13%)use the Internet frequently and for

three or more different applications/purposes

(2) Narrow frequent users (18%) also use the Internet frequently, but

for one or two different applications/purposes

(3) Occasional users (11%) use the Internet occasionally and/or

sporadically

(4) Non-users (58%) had not used the Internet during the previous

12 monthsResults from interviewsIllustrate how the different

user types differ considerably when it comes to the value, useful-ness, and amount of interest they attach to Internet services.

While ‘‘(non) use of the Internet is best understood both in terms

of social structuring and an individual’s personal circumstances”

(p. 22)

(15) Shih and Venkatesh

(2004)

Sample of 910 US households that owned computers

(Mean age 41,7) Questionnaire survey (telephone)

Computer and Internet usageMeasuring variety of use and

rate of use and regression analysisTheory: Diffusion theory

Rogers (2003)

Context: US

(1) Intense users (30%), describes situations in which an innovation is

used to a significant degree in terms of both rate of use (time

spent per week) and variety of use (number of applications)

(2) Specialized users (20%), applications as specialized tools

(3) Non-specialized users (20%), refers to a pattern of use in which

variety of use is more critical than rate of use, describes usage

based on trial and error

(4) Limited users (30%) have a low variety and a low rate of use




Table 1 (continued)


(16) DeYoung and

Spence (2004)

Not representative (age 17–67)

323 participants


Factor analysis

Media: Interne, computer, mobile phones

Theory: personality theory (e.g. John & Srivastava, 1999)

and computer attitude (Whitley, 1997)Context: Canada

(1) Interest

(2) Anxiety

(3) Approval

(4) Confidence

(5) Internet Transactions

(6) Entertainment

(7) Complex Design PreferenceTechnology Profile Inventory (TPI)scores were compared with regard to information technology

use and experience. Correlations were found between total TPI

score and all usage variables, as well as between total TPI score

and experience

(17) Roberts et al. (2004) National random sample of U.S. children’s and

adolescent’s aged 8–18 year old


A variety of new media

Cluster analysis

Theory: Explorative study

Context: U.S

(1) Media Lite (18%), spend least amount of time with media. Primar-

ily spending time on television, music and print. Lower access to

media and more restrictive media environments. Least likely to

claim many friends, but is happy at school and getting good

grades. More likely to be girl

(2) Interactor (16%), embrace computers. Less likely to have access to

media in their bedroom, but most likely to have computers and

online access at home. Use computer or read. Use computers to

‘‘kill some time”. 51% are boys. Average age 13 years

(3) VidKid (15%), spend the most time with media, television or video

accounting for nearly half. Medium access to media in their bed-

room, home access generally low to computers, but high on video

game consol. Time used on media high, mainly entertainment.Pro-media environment

(4) Restricted (15%), kids living in the most controlled media environ-

ments. Reports 6 h media use daily use typically four different

kinds of media a day, more likely to use computers. Slightly more

likely to be boys, wealthy families and good grades at school

(5) Indifferent (18%), kids with high access to media both in their

home and bedroom, but spend less time with media than most

youths. They report in total 5½ hours per day on media use.

The report having a lot of friends and earn good grades at school

(6) Enthusiasts (19%) are avid media users, reporting most media

exposure and are living in the richest media environments. Most

likely to use almost every medium, and they spend more time

with almost every medium than any other of the kids. They

report 13 h of media use daily. Urban areas

(18) Kau et al. (2003) 3712 online shoppers, aged 15–65 years (not

representative)


Internet usage with a particular focus on online shopping

Cluster and factor analysis

Theory: not a very clear theoretical approach, but it is

about trust, attitudes and media usage in general. More

descriptive.

Context: Singapore

(1) On–off shoppers (11%) collect information online then shop off-

line. Experienced users. Younger age-group

(2) Comparison shoppers (28,5%) compare products, prices, brands,

and promotional offers before making a purchase decision

(3) Traditional shoppers (9,5%) do not surf the Internet for shopping-

related information. Adult users

(4) Dual shoppers (22,4%) compare brands and product features. Male

and young

(5) e-Laggards (5,5%) have less interest in information seeking and

are less experienced

(6) Information surfers (23,1%) looks for promotional offers. They

have good navigation expertise and strong online purchasing

experience

(19) Sheehan (2002) Partly generalizable to the total population of the US

Internet users (N 3724), only 889 completed the survey

Questionnaire survey online (email)

Media: Internet usage, but a focus on online shopping

Cluster analysis

Theory: privacy typologies and consumer typologies

Context: US

(1) Unconcerned Internet Users (16%) reported minimal concern

regarding Interne use

(2) Circumspect users (38%) felt a low to moderate level of concern

with most situations

(3) Wary Internet Users (43%) felt only a moderate level of concern

with most situations

(4) Alarmed Internet Users (3%) were very concerned with privacy in

all situations

(20) Shah et al. (2001) Demographically balanced sample from an Internet

panel (N 3388)

Questionnaire survey online

Internet , (partly newspaper and television use)

Factor analysis

Theory: uses and gratifications and theory on media use

Context: US

(1) Social recreation, participation in chat rooms and game playing

online

(2) Product consumption, purchasing activities online (purchased a

book, clothing, videos or music)

(3) Financial management , made banking transactions, and made a

stock transaction

(4) Information exchange, searching for information, and sending

email (explored an interest or hobby, searched for information

for school or educational purposes, email usage)The four specific

types of Internet usage have significant and systematic links with

the production of social capital, in terms of civic engagement,

interpersonal trust and contentment. ‘‘Information exchange

was a key contributor for individuals’ social capital, with high

activity in civic activity and trusting attitude

(continued on next page)




theory about ‘‘gratifications sought” – a theory that relates to the

‘‘expectancy-value theory” (Palmgreen & Rayburn, 1985), where

the role of personal motivation for using a media technology is rec-

ognized. Here, personal attitudes towards a medium are formed by

past experiences, expected rewards and personal preferences

( Johnsson-Smaragdi, 2001), leading to ‘‘the proposition that media

use is accounted for by a combination of perception of benefits of-

fered by the medium and the differential value attached to these

benefits” (McQuail, 1994, p.305). However, the gratification per-

spective have came in for theoretical criticism, since the entire pro-cess and participatory aspect of media behaviour seems to fall

outside this framework (e.g. Carey & Kreiling, 1974). Carey and

Kreiling (1974) are also claiming that some people use media with-

out any purpose, and are therefore critical to that uses-and-gratifi-

cations approach that considers individuals of media to be

purposive in their choice of media and to actively seek media to

fulfil their needs for a variety of uses.

Taken together, all the theories and models above per se ap-

proach usage behaviour as a dependent – rather than as an inde-

pendent – variable (except Assael (2004)), and fails to explain

the nature and process of media behaviour as such. By focusingon describing why people use new media, spotting how people

Table 1 (continued)


(21) Howard et al. (2001) Representative (N 12638) sample of the US

population

Questionnaire survey (telephone)

Analysis based on Internet experience and frequency

of logging on from home

Internet usage Theory: (Rogers, [1962] 2003) diffusion theory

Context: US

(1) Netizens ((16%/8%) = 16% of the adult Internet population and 8%

of the adult population). These are experienced users with home

access. Incorporated into everyday work and social life. Aggres-

sive and innovative. Men, well-educated, well-to-do, Caucasian

(2) Utilitarians (28%/ 14%). These are experienced users with home

access. Less intense than netizens, perceive the Internet as a tool(3) Experimenters (26%/ 13%) have 1–3 years experience and home

access. Use the Internet as an information retrieval utility

(4) Newcomers (30%/15%) have less than 1 year of Internet experi-

ence. They are apprentices; learning their way around, enjoying

the fun aspects of the Internet, such as games, chat, and Instant

Messaging. Likely to have access in only one place, most often

at home

(22) Johnsson-Smaragdi

(2001)

Representative for children aged 9–16 in 10

European countries

Questionnaire survey (paper)

All media

Cluster analysis

Theory: expectancy-value theory (Palmgreen & Rayburn,

1985), and individual styles of media use (see Hasebrink,

1997)

Context: Europe

(1) Low media users (44%) spend little time with media technology

(2) Traditional media users (20%) use old media, such as television

and electronic consol games (mainly Play station and Nintendo)

(3) Specialists (28%) were subdivided into four groups: Television,

Book, PC, and Games specialists. They spent more time on one

of the four types of technologies than on the others

(4) Screen Entertainment Fans (8%) were subdivided into two sub-

groups: television and video, and television and gamesThere

were differences between the countries in regard to how large

the different user profiles are distributed

Table 2

An overview over relevant theoretical models in regard to a media-user typology.

Theories & reference Variables/processes Main user focus Typology/classification

Personality types (e.g. Jung, 1971) Categories of membership

that are distinct and

discontinuous (e.g.

extravert or introvert)

Categorize people in groups

based on their psychological

profile

Carl Jung (1971) asserted that individuals are

either ‘‘extraverted” or ‘‘introverted” their

dominant function. Jung defined eight

personality types: from Extraverted Sensing to

Introverted Feeling

Diffusion of innovation (Rogers, 2003) Explain how, why, a nd a t

what rate people adopt new

ideas and technology

innovation.

Focus solely on adoption of

innovations, and classify

people according to their

adoption rate.

This model offers the following categorisation,

which is based on people’s adoption rates of

technological innovations over time:

Innovators (around 2.5%), Early Adopters(13.5%), Early Majority (34%), Late Majority

(34%), and Laggards (16%)

Technology-acceptance models

(e.g. Venkatesh, Morris,

Davis, & Davis, 2003)

Individual adoption

processes. Media use is

treated as a dependent

variable

Focus on technology

acceptance, and very work-

related processes. Looks at

the individual as more or

less one-dimensional

No specific user types or classifications

Uses and gratifications theory

(Katz, Blumler, & Gurevitch, 1974)

Motivations and

gratification needs of why

people use media. Media

use is treated as a

dependent variable

Focus mainly on

motivational aspects in

general, which might

explain ‘‘why” certain

media behaviour occurs, but

not its nature.

No user types: four motivation needs according

to McQuail (1994): (1) information, (2)

entertainment, (3) social interaction, and (4)

personal identity

Market Segmentation (Assael, 2004) Explains who and how

people is buying and using

the product

Main focus is brand usage

and product usage

No general classifications, but some general

criteria for behavioural segmentation is based

on (1) brand usage, (2) product category usageand (3) level of use (heavy or light)




use such new media (the actual media behaviour) is more or less

ignored in the existing theories and models. The word ‘‘use” is mis-

construed or to a little degree elaborated in these because the ac-

tual and various media-usage behaviour is overlooked, as

elaborated in the introduction. Consequently, the result of this

analysis emphasises the importance of a model that describes

media behaviour more fully.

However, approaching media behaviour from the standpoint of personality-type theory does have its uses. Some researchers have

recognised that human encounters with new media reflect some of

the complexities of the human personality (DeYoung & Spence,

2004). Thus, the construction of both ‘‘market segmentation” and

media-user typologies might have its roots in clinical psychology

and is similar to the development of personality types (Barnes

et al., 2007). Hitherto, little theoretical work has been done to ap-

ply this or other theoretical approaches to further develop a theory

for media-user typology.

Further, both the U&G and the Diffusion of Innovations theories

give some obvious inputs for a model MUT, in which possible med-

ia behaviour should be distinguished in terms of types. The U&G

theory and the gratifications – namely, information, entertainment

and social interaction – should be the central dimensions that re-

flect different user types. In fact, these are reflected in most user

types listed in Table 1. The different adaptor categories suggested

by Rogers (2003) and how they are identified by diverse attitudes

might serve as important predictors and explanations for the dif-

ferent user types that are suggested later in this article and will

consequently serve as a theoretical argument for the classifications

used in the proposed model.

However, the most important issue and aim in the present arti-

cle is to describe a common ground to approach media behaviour

using a typology. User research is, as pointed out by Dervin

(2003), a chaos of theories, concepts, approaches, methods, and

findings that plague researchers within and between fields and

bewilder policy makers and practitioner observers.

3.2. Analysis 2 – Method and sample

The media-user typologies identified in Table 1 are based

mainly on the users’ responses to survey items measuring media

usage. The principal methods applied for identifying the numbers

among the user types registered in Table 1 are cluster analysis

(11 studies) and factor analysis (5 studies). Cluster analysis classi-

fies a large number of cases into relatively homogenous groups

(clusters) and yields typologies, whereas factor solutions yield pat-

terns of usage or underlying user traits. Compared with factor anal-

ysis, clustering of variables has the following advantages:

(i) it identifies the key variables that explain the principal

dimensionality in the data, rather than abstract factors;

(ii) it allows much larger correlation or covariance matrices tobe analysed; and

(iii) it greatly simplifies the interpretation of different user types.

As long as the focus of research is the identification of types (as

opposed to traits), cluster analysis is preferred over, for example,

factor analysis. However, both approaches are possible. In addition,

the importance of combining qualitative investigations concerning

user types to get more in-depth knowledge on how different user

types behave and develop over time needs to be stressed here.

Until now, little is known about different user types from a quali-

tative perspective, excluding a few studies (OFCOM., 2008).

With reference to the samples registered in this review, most

studies use a convenience sample that charts the use of the adult

population. Some of the larger studies chart the media usage of children down to 15 years, in addition to charting that of the adult

population (e.g. Heim & Brandtzæg, 2007; Horrigan, 2007). Only

five studies include children in their sample, whereas the others

investigate a broader part of the population; none of the studies in-

clude children younger than eight years. However, the typologies

in these studies involving children appear to correspond quite well

with studies targeting the adult population, indicating the user

types across a broad spectrum of ages.

3.3. Analysis 3 – Year of publication and evolution of typologies

One of the aims of this article is to detect the changing patterns

of media usage over time; however, it is difficult to determine

whether the characteristics of the typologies have changed over

time. The reason is that the studies whose purpose and results

are presented in Table 1 differ in their research design, context of

research and media studied. Notwithstanding these consider-

ations, some changes in pattern may be noted. There is a greater

focus on the use of e-Government services in the later studies con-

ducted during 2007. Furthermore, in later studies, users are classi-

fied as active participants in social computing, community, or SNS

usage and user-generated content (UGC). The most noticeable

change is that a greater number of user types are included in some

of the later typologies (e.g. Horrigan, 2007), which might suggest a

trend towards more fragmented or differentiated usage among

new media users and a greater sensitivity on the part of the

researchers to different usage types. This might equally support

recognition on the part of the researchers regarding the impor-

tance of studying user types. In total, 10 out of 22 studies were

published after 2006, which indicates a large increase in the study

of user types between 2006 and 2008. A greater focus on the spe-

cific media, how people use these media and the changes under-

gone by these usage patterns might be a good indicator of how

the digital literacy of people changes over time. Additional knowl-

edge in terms of longitudinal studies is needed, however, to exam-

ine how these patterns evolve and how people change from one

user type to another.

Another factor that is increasingly important with reference touser type is the focus on lifestyles (Brandtzæg & Heim, 2009; Deck-

er, 2006; Swinyard & Smith, 2003). Several trends both in our

usage of media and in society indicate why scholars should con-

sider people’s lifestyles also when focusing on user types. The

gap between usage behaviour and behaviour in everyday life is

increasingly narrower because the everyday lives of people have

become increasingly information mediated (Silverstone & Haddon,

1996), as noted in the introduction. Consequently, the use of media

technology can change because of changes in both daily routines

and lifestyle preferences or hobbies, and vice versa (Swinyard &

Smith, 2003). This also corresponds to the term ‘‘egocasting” (Ro-

sen, 2004). Arguing that media technologies have become increas-

ingly adjusted to the satisfaction and expression of individual

choice, Christine Rosen (2004) describes ‘‘egocasting” as the thor-oughly personalised and extremely narrow pursuit of one’s per-

sonal taste. Donna Haraway (1991) has also acknowledges the

interdependences of people and technology and how the boundary

between them has become blurry. Future user typologies may

therefore not only reflect a media-user typology, but also a ‘‘life-

style typology”, which may indicate a future dependence on the

personality theory for an understanding of media-user types.

3.4. Analysis 4 – Context of research or cross-country perspective

Majority of the studies reviewed were conducted in a US (9

studies) or European context (11 studies), with one study each in

the Canadian and Singaporean contexts. Only five studies have

compared the users in several countries, but these studies showdifferences in the sizes of user types (weights of the clusters)




within the countries considered (e.g. Barnes et al., 2007; Ortega

Egea et al., 2007), which supports the assumption that structural

and cultural factors also influence how people use new media.

Media users cannot, therefore, be seen as a homogenous global

community. Nonetheless, it should be noted that the different

nations also share some similarities in terms of media use because

the weights of the clusters display roughly the same pattern, at

least in Europe and the United States. This again supports the claimmade in our article that there is a universal media-user typology,

regardless of the country.

3.5. Analysis 5 – Comparing user typologies

Most of the studies (10) reported in Table 1 document four user

types, four studies document three user types, and the rest of the

studies identify around five to six types, with 10 being the greatest

number of types identified. One striking finding is that all the stud-

ies use different labels for their user types, with none of them relat-

ing their results or typology to each other. One reason for this

might be that the diverse typologies recorded have different pur-

poses; for example, some studies just examine the online shopping

pattern (Barnes et al., 2007; Kau, Tang, & Ghose, 2003 ), whereas

others focus on usage behaviour in SNSs or online communities

(Brandtzæg & Heim, 2009; Jepsen, 2006; OFCOM, 2008). Another,

and maybe more important, reason is that a common empirical

and theoretical framework of media-user typology is nonexistent,

as pointed out in the introduction. Most of the studies reported

try to establish a typology, but the researchers are not aware of

other relevant researches and typologies.

To achieve a more unified understanding of the different user

typologies, it is necessary to examine in more detail how to orga-

nise the different user types so that they fit together, and using this

as a basis, define a common set of meaningful user types. Table 3

presents the way in which we have organised the suggested user

types, based on how different user types were labelled and justi-

fied in the previous studies reviewed in Table 1.

3.6. Analysis 6 – User types and media platforms

Only a few studies (7) investigate a broader part of the media

landscape (see Table 5), focusing on a variety of ICTs. All studies in-

clude the Internet, but seven focus only on particular parts or

applications of the Internet, such as online shopping (3) and SNSs

or social computing (6). The reason for the particular focus on pur-

chasing activities might be the need to segment the shopping user

population, which obviously consists of a diverse online behaviour.

Moreover, SNSs have lately been integrating several different

applications and services on the Internet, such as email, chat, vi-

deo/television, music, photographs, gaming and shopping, and

hence should be regarded roughly as the new Internet (Brandtzæg

& Heim, 2008). The variety of opportunities in these sites might

also indicate that they involve different user types and highlight

a need to understand these types.

Table 4 reports the expected distribution of user types in per-

centages, based on the maximum and minimum percentages for

the different user types in the 22 studies reviewed in Table 1. Ta-

ble 4 also shows how each user type is expected to be distributed

across different platforms (new media in general, shopping, SNSs,

and the Internet in general).

Table 3

Organising previous media-user types identified in the review from Table 1.

User types labelled in previous studies User types defined Justification

Non-Internet users, Off the net, Inactives, Non-users, Non-users,

Anxiety,

Non-users Non-users of the media investigated, and a quite common

category. The largest of all user types in representative studies(see Table 4)

Followers, Sporadics, Laggards, Confused and Adverse, Followers,

Indifferents, Indifferent, Media Lite, Average users,

Inexperienced experimenters, Risk-averse doubters,

Spectators, Connected but hassled, Basic users, Occasional

users, Limited users, Approval, e-Laggards, Traditional

shoppers, Newcomers, Low media users

Sporadics Identified in 20 studies. One of the most evident user types. Users

that are newcomers and are low level or sporadic users of the

particular media studied

Debaters, Contributors, (Creators). Debaters Bloggers and debaters in SNSs are only identified in two/three

studies. This is a quite new, up and coming user type, because of

new social media and easier tools for blogging, discussion, and

debating

Attention seekers, Entertainment usage, Gameboy usage, VidKids,

Entertainment users, Entertainment, Social recreation,

Moderate users, Experimenters, Screen Entertainment Fans

Entertainment users Entertainment users were referred to in 10 studies. Probably an

up and coming user type, because of the high increase in gaming

and the convergence of television and the Internet

Alpha socialisers, Faithfuls Socializers, Socializers, Sociability, TheConnectors, Joiners, Communication usage, Minglers Socializers Identified in nine studies. It is also a quite new and increasing usertype because of the advent of social media applications

Interactors, On–off shoppers, Lurkers, Lurkers, Tourists Lurkers Only identified in five studies, but account for the biggest user

type in SNSs, and in regard to UGC in general. People using the

media for ‘‘goofing off”, lurking, or time-killing, and/or ‘‘window

shopping” on the web

Functionals, Utility, Productivity Enhancers, Spectators, Utility

usage, Devotees, Utility users, Narrow frequent users ,

Specialized users, Internet transactions, (Restricted), Reserved

information seekers, Financial management, Comparison

shopper, Broad users, Dual shopper, Information exchange,

Utilitarians

Instrumental users Identified in 16 studies. Is a quite common user type related to

media in general and the Internet in particular. Users that use

media for utility and as an information tool, both at work and in

private. Not so obvious in SNSs

Actives, Omnivorse, Lacklusters Veterans, Collectors, Reciprocity,

Advanced usage, Insiders, Advanced users, Broad frequent

users, Intense users, Enthusiasts, Open-minded online

shoppers, Confidence, Complex Design, Unconcerned Internet

users, Preferences, Information surfer (shopping), Netizens,

Specialists, All-round users, Active contributors

Advanced users Identified in 20 studies. Along with Sporadics, it is the most

common user type. This type represents users that use a wide

range of media frequently, using the most advanced facilities

compared to the rest of the user population




The number of user types varies according to the platform.

However, a complete user typology could be relevant for all media,

regardless of the platform.

First, it could be relevant on a technological level, informa-

tion systems are undergoing rapid changes in terms of how dif-

ferent platforms for information converge across previously

disparate families of technology – for instance, we can use amobile telephone to browse the Internet, the Internet to make

phone calls, and the television to check emails. This evolving

media convergence suggests a common user typology across

media platforms.

Second, it could be relevant on the user level – for instance, peo-

ple using shopping as a platform, could be observed as both

‘‘debaters” and ‘‘socialisers”. Future studies can also be expected

to yield results influenced by the increasing use of social media

in the domain of ‘‘shopping”. Similarly, SNSs such as Facebook offer

services for both entertainment and matters of usefulness, such as

business and politics, which also suggests a possible outcome of

both entertainment users and instrumental users in future studies

on SNSs. As shown in Table 1, the most recent typologies (e.g. Horr-

igan, 2007) also indicate a trend towards development of severaldistinct user categories. Thus far, the four user types with the larg-

est percentages in the respective studies are instrumental shop-

pers, non-users of media in general, non-users of SNSs, and

lurkers in SNSs.

3.7. Analysis 7 – Dimensions of media behaviour identified in previous

researches

Finally, to arrive at a unified user typology, it is necessary to

analyse in greater detail how the previous user typologies identify

media behaviour in terms of frequency and variety of usage, media

platform or service studied and preferences for media activity.

Summing up the data presented in Table 5, the results show

clear differences in the typologies. These are related to different

media platforms, particularly activity or content preferences. Fre-

quency and variety of use are important variables for determining

user types, as are activity preferences related to entertainment, e-

Government services, and more advanced types of usage. More-

over, recent studies and typologies indicate the creation of UGC

and socialising as important user dimensions, reflecting the

increasing importance of the social media. In sum, a user typologyshould create a baseline with reference to a broad spectrum of

Table 4

User types and expected distribution (maximum and minimum percentages) over the diverse media platforms.

User types Media in general Internet Online-shopping Social network sites

Non-users (%) 37–40 15–58 10 53

Sporadics (%) 16–35 11–20 5–15 9–19

Debaters (%) n/a* n/a n/a 11

Entertainment users (%) 7–25 29 n/a n/a

Socializers (%) 7 9–19 n/a 25

Lurkers (%) 16 n/a 11 27–90Instrumental users (%) 15–23 14–44 45–50 n/a

Advanced users (%) 8–19 7–30 23–40 1–18

Number of user types 6 5 5 6

Note. n/a = not applicable distribution of this user type.

Table 5

A summary of dimensions of media behaviour identified in previous research.

Study Usage Platform/applications used Activity/preferences

References Frequency

of use

Variety

of use

Different

media

Online

shopping

SNS/online

community

Internet in

general

UGC, user

as producer

Advanced

usage (e.g.

programming

Entertainment

usage (e.g.

gaming)

eGov/public

info

OFCOM (2008) X XAuthor (forthcoming) X X

Ortega Egea et al. (2007) X X X X

Johnson and Kulpa (2007) X X X

Horrigan (2007) X X X X X

Heim and Brandtzæg (2007) X X X X X X X

Li et al. (2007) X X X X X

Barnes et al. (2007) X X

Heim et al. (2007) X X X X

Livingstone and Helsper (2007) X X X

Nielsen (2006) X X X

Jepsen (2006) X X X X

Brandtzæg et al. (2005) X X X X X

Selwyn et al. (2005) X X X

Roberts et al. (2004) X X X X X

Shih and Venkatesh (2004) X X X X X

DeYoung and Spence (2004) X X X X

Kau et al. (2003) X X XSheehan (2002) X X

Shah et al. (2001) X X X X X

Howard et al. (2001) X X X X X

Johnsson-Smaragdi (2001) X X X X X

Results 12 19 7 7 5 11 3 6 12 4




usage that reflects the different opportunities that users have in

the new media landscape.

4. Towards a model for an MUT

On the basis of the seven meta-analyses carried out in this arti-

cle, a first step towards developing a model for an MUT is sug-gested. The model consists of eight user types, which are

described in more detail in Table 6. The initial model for the

MUT provides a basis for stringing the methods and results of pre-

vious reports into a unified framework and for establishing a com-

mon ground for determining a single user typology, based on the

level of frequency (none/low/medium/high) and variety of use

(none/low/medium/high), activity preferences (entertainment/ad-

vanced/public services/user-generated content), and the most typ-

ical media services and platforms that different user types are

associated with (as partially presented in Tables 3 and 4). Table 6

serves as a comprehensive framework for determining the differ-

ent user types.

The typology of users suggested in Table 6 is based on distinct

user behaviour, rather than on their explicit goals or motivations.Thus, the model for the MUT partly attributes actions to goals, be-

cause the correlation between various behaviours and different

motivations of the users can be interpreted, as described in the

U&G theory. However, it is important to specify that the user typol-

ogy, similar to the one presented herein, is more than just a classi-

fication of different users reflecting their actual media behaviour.

The user types presented in this model are based on a series of

logical arguments and empirical observations that denote the com-

plete user typology (see Doty & Glick, 1994).

Furthermore, they can help us to (a) measure and define varia-

tions in media usage, (b) understand the different requirements

and motivations that users in different segments in the population

have for using media or (c) interpret how diverse user groups are

likely to respond to different forms of media usage in terms of psy-chological and social implications and whether and how these

show cross-cultural differences (Barnes et al., 2007). The formula-

tion of such an MUT will constitute clear progress in understanding

the dynamic relationship between diverse individuals and their

media environment.

5. Discussion

The review presented in this article clearly shows how media-

usage behaviour and typologies have evolved and changed since

the year 2000 up to 2009. However, the user types reviewed orig-

inate from quite different studies and are somewhat dependent on

the sampling outcome, the thoroughness of the researcher and the

creativity in labelling the types. Nevertheless, by using conceptual

and empirical similarities across different user typologies that have

been presented in previous research our findings suggest some

general user types that reflect distinct categories of user behaviour

that might be valid. This article also suggests a commonality be-

tween the types in the MUT.

The initial MUT model presented in this article may be fluid or

ideal. Combinations of user types or hybrid user types among those

that have already been determined in the MUT may be found in thefuture, in addition to new user types. The first point is that user

categories might not be mutually exclusive. As stated by Bowker

and Star (1999) in the book Sorting things out , no real world–clas-

sification systems meets this requirement because mutual exclu-

sivity may be impossible in practice. The second point is that

there probably will exist hybrid user types that are combinations

of the initial ideal types defined, because the same users could

be defined as different user types in terms of various media plat-

forms: A ‘‘sporadic SNS user” might, for example, also be an

‘‘instrumental user” in ‘‘general media”. In other words; the same

users can have different user profile types depending on the

platform.

Therefore, the model suggested in this article does not yield an

absolute typology of people’s media use. Rather, it is a rough gen-eralisation of the present media landscape and its user’s media

Table 6

An initial unified Media-User Typology – MUT and the four criteria for defining types by media behaviour.

User types Frequency of use Variety of use Typical activity Typical media platform

(1) Non-users No use No use No All

(2) Sporadics Low use Low variety No particular activity. No contact with e-

Government services. The Internet is

rarely used for private purposes. Low

interest, less experienced

All

(3) Debaters Medium use Medium variety Discuss ion and information acquisition

and exchange. Purposeful action

Blogs and SNS

(4) Entertainment users Medium use Medium variety Gaming or passively watching videos, but

also advanced use, such as UGC,

programming, and shopping

New media in general

(5) Socializers Medium use Medium variety Socializing, keeping in touch with friends

and family, and connecting with new

acquaintances. Active social life, but less

organised and purposeful, more

spontaneous and flexible.

SNS

(6) Lurkers Medium use Low variety Lurking, time-killing SNSs, user-generated sites, shopping,

and new media in general

(7) Instrumental users Medium use Medium variety Choose media content for information and

civic purposes, utility oriented, often work

related, searching for e-Government or

public information. Low on entertainmentuse. When shopping, comparing brands

and promotional offers

New media in general, including

Internet , and online shopping

(8) Advanced users High use High variety All (gaming, homepage design, shopping,

programming, video, e-Government and

UGC, etc.)

All




behaviour. The media landscape, users, and usage behaviour are

subject to continuous change and increasing in complexity. The

latest trend is for users to move from being passive consumers to-

wards playing a more active role in the media chain in UGC sites or

SNSs. Social media platforms, such as Facebook, Twitter, and My-

Space, have become a dominant environment for online social

interactions in a very short span of time. In addition, media behav-

iour might become increasingly fragmented, which again will addto the already existing number of user categories or sub-categories

as time goes on. However, as Bowker and Star (1999) pointed out, a

structured classification, rather than using a high-level semantic

typology, is useful in spite of its limitations because we may fall

into a lowest-convenience classification without it.

Studies reviewed in this article point to the fact that most stud-

ies identify only four user types (see Table 1). So few categories

make the user typology ‘‘easy-to-understand”, but might on the

other hand give a too superficial picture of the existing usage pat-

terns. However, most of these user studies include only a limited

set of media services or platforms as shown in Table 4. The initial

MUT, consists of eight user types and includes a more comprehen-

sive picture over various user behaviour. A greater number of user

types are also reflected in the most recent typologies (e.g. Horrigan,

2007) indicating a trend towards the development of several dis-

tinct user categories. Furthermore, the suggested MUT includes

media behaviour across all media platforms which indicates a

great variation in media behaviour and more user types. In any

case, future research should further elaborate the usefulness

according to different numbers of user types identified.

Critics of the typology might also observe that the types can be

quite strongly stereotyped and that the many dimensions of an

individual’s media behaviour tend to be oversimplified. However,

such oversimplification may be the very thing that will help HCI

researchers better understand different types of user behaviour.

This might also be a plausible reason for the popularity of such

typologies and the reason this article claims that the HCI commu-

nity can learn from customer-segmentation studies for the general

identification of media-user profiles according to their media-con-sumption preferences and participation levels. It is important to

gain more knowledge on what types of user behaviours and user

preferences are linked to certain user characteristics in the general

population, to finally understand the complexity in an increasingly

fragmented user population.

Furthermore, this article claims that we can find a universal

typology across different cultures. Culture influences the lifestyle,

and the lifestyle influences the way we communicate and interact

with new media technologies. Anyway, there will be some general

similarities across different cultures according to media-usage pat-

terns, which also are reflected in the 22 studies. This also corre-

sponds with other research on media usage in developed

countries. In the long run most national differences will disappear,

predicts Scott Campbell (2007). Such a universal typology is alsowhat companies would like to achieve, because it costs more to

provide different services in different parts of the world than it

would to offer the same things everywhere (The Economist, 2009).

A media-user typology appears to provide a parsimonious

framework for describing complex media behavioural forms and

for explaining the outcomes, such as how different usage condi-

tions relate to social implications that include social capital (Shah

et al., 2001). Typologists usually achieve parsimony by providing

elegant descriptions of their typologies and glossing over complex

processes. Similarly, marketing researchers strive to identify broad

trends and patterns corresponding to the consumers’ daily life, lei-

sure behaviour, and spending habits (Decker, 2006).

The most severe criticism is that typologies traditionally have

been viewed only as classification systems rather than as theoret-ical frameworks. Rich (1992) argues that typologies are classifica-

tion schemes and, as such, provide ‘‘a means for ordering and

comparing organisations and clustering them into categorical

types” (Rich, 1992). On the contrary, McKelvey (1982) defines

typologies as essentialism, which is a theory of classification.

Moreover, Bacharach (1989) pointes out that typologies are more

abstract than simple categorical devices.

As shown in the review section of different theories, none actu-

ally explains a framework to understand the process of individualmedia behaviour. However, the need to gain more knowledge

about users and usage of new media has been growing for some

time now. What is meant by media behaviour and how can the dif-

ferent types of behaviour be determined? This is important partly

because of the complexity of media systems and partly because of

the importance of users who handle new media. As noted by Zillien

and Hargittai (2009), research should not only focus on a techno-

logical artefact, but should also consider patterns of usage when

studying the digital divide and social implications of technologies.

This implies that we should, as in this MUT, go beyond the binary

differentiation of users versus non-users.

User behaviour is complex, and by using an MUT, identification

of the types of media behaviour and the potentiality to be able to

build restrictions into technological and social systems should be

enabled, so that only select types of behaviours bridging the digital

divide are encouraged. Previous research has found that going on-

line is a staged process, populated with people who either take up

more or fewer opportunities (Livingstone & Helsper, 2007). Simi-

larly, the eight distinct types of users in the MUT model have been

identified on the basis of their behavioural patterns previously

published in 22 studies.

The user types reflect substantial differences in the patterns of

media usage. These differences could also be viewed as a staged

process in which the benefits of media use depend on the different

skills and expertise (as in Livingstone & Helsper, 2007). The Ad-

vanced users engage in almost all the activities within the new

media, whereas the Debaters interact with others for the purpose

of discussion. Instrumental users are skilled and use new media

for the purpose of utility. The Socialisers and Entertainment usersprimarily use new media for either ‘‘small-talk” activities or gam-

ing reflecting less organized and purposeful action, compared to

Debaters and Instrumental users. Whereas the Sporadics, are less

active, but ‘‘socially curious” in that they sporadically check to

see whether anybody has contacted them. The Lurkers seem to

log on for the purposes of ‘‘time-killing” and consuming entertain-

ment rather than social interaction, whereas Non-users lack access

to, or ability or interest in using new media. These different modes

of media behaviour also reflect the different levels of skills of users

and the advancements in new media, as suggested in the pyramid

in Fig. 1, where the highest and most skilled, at the top, are the Ad-

vanced and the lowest, at the bottom, are the Non-users. Over

time, people develop more advanced communication skills or

user-type patterns in new media, because people start off asNon-users, gradually transform to Sporadics or Entertainment

users, and progress to higher levels in the ‘‘user-pyramid” (see

Fig. 1), either as Socialisers, Debaters or Advanced users. Future re-

search should investigate how and in which directions these pat-

terns expand over time. In this process Debaters and

Instrumental can be viewed at the same level in the pyramid as

shown in Fig. 1, because the overlap to some extent – their engage-

ment are organized and purposeful, and they are medium in vari-

ety of use and frequency of use. Entertainment users and

Socializers are also on the same stage, as both reflect less purpose-

ful and less organised action. However, when measuring media

behaviour the distinctions between these users types should be

made clear, because they reflects different activities, but in a

staged process, as shown in the pyramid, this might not be animportant factor.




It is argued here, similar to that of Dewey (1994), that the social

scientist’s true purpose is to facilitate the type of actions that lead

to good consequences for the society at large. An MUT could be an

initial scheme guiding and giving directions to identify the advan-

tageous usage patterns in terms of digital literacy, associated with

the Instrumental and Advanced usages. Both Socialising and Enter-

tainment usages, however, could serve as good starting levels to

build up digital literacy and take up of media opportunities over

time. This claim corresponds to the progression of online activitiesidentified by Livingstone and Helsper (2007), suggesting that the

route to advanced usage is reached through facilitating entertain-

ment and communication online. That being said, still little is

known about the long term social role for new media and what

kind of usage is good or bad for the individual ( Heim et al., 2007;

Livingstone & Helsper, 2007).

5.1. Future studies

First, further theoretical statements should be subjected to

quantitative modellingand rigorous empirical testing of this model.

To gain more insight into the distribution of media behaviour and

the user types that display it, studies that are both more represen-

tative and cross-national in scope are needed, in addition to morequalitative research. Such samples will be able to both identify all

theformsof user typesand investigate thedistribution of thediffer-

ent media-user types more clearly, in addition to elucidating how

the different user types reflect different literacy statuses.

Second, theoretical developments should also take into account

the theoretical models (see Table 2) analysed in this article, but

should in addition look into important research on information

behaviour (e.g. Wilson, 2000) as well consumer behaviour re-

search, marketing, psychology, health communication research,

and a number of other disciplines that take the user as the focus

of interest, rather than the system.

Third, there is still a need for a common measure that can grasp

the different user types in the model. This work should mainly be

guided by the MUT model (see Table 6, in particular) and the re-sults of the general review provided in this article, especially with

reference to preferred research design. A development of a MUT-

measurement should in addition look into lessons learnt from

the measurements and models provided by Shih and Venkatesh

(2004) and Johnson and Kulpa (2007), as mentioned in Table 1.

Finally, future research should aim to discover the social impli-

cations associated with certain types of media behaviour. This

might also contribute to knowledge about the type of actions that

lead to good consequences for both the society and the individual.

6. Conclusions

This article aimed to classify diverse user behaviours into mean-

ingful categories of user types, according to the 1) frequency of use,

2) variety of use and 3) content preferences. This was done to reach

a common understanding of media behaviour. In general previous

research to understand the process of media behaviour reflects a

relatively underdeveloped research stream for the discipline of

HCI research. To reach a common framework, a review of the rele-

vant research was conducted. An overview of the literature (22

studies) regarding user typology was established and analysed.

This is the first existing overview of user typologies with reference

to new media behaviour and it provides a summary of previous re-searches (22 studies) - from the year 2000 - on user typologies, in

addition to quoting relevant theories. The analysis of this volumi-

nous body of research has provided results that serve as a common

basis for developing a unified model of media-user typology—an

MUT, consisting of eight user types.

These findings underscore the importance of involving several

qualitative aspects related to the process of usage patterns in

understanding the user behaviour in new media more fully. In

the future, all these user types are predicted to be found, regardless

of the media platform. The user typology is also claimed to be uni-

versal across different cultures. Evidence from user types identified

in cross-country studies reported herein suggests that a common

structure underlies media-user behaviour among different

countries. The MUT classification might therefore be valuable indeveloping a basic understanding of media behaviour.

Fig. 1. User hierarchy in the MUT - describing the possible migration of different types of media behaviour. As in Table 6, this figure list four criteria for defining types by

media behaviour: (1) variety of usage, (2) frequency of usage (time-use), (3) content/activity preferences and (4) media platform/service the person use.




Understanding the users is one of the key issues in HCI. There is

a lack of approaches providing a novel and accurate viewpoint to

the understanding of the user behaviour in new media. The exist-

ing theories (e.g. Davis, 1989; Rogers, 2003; Venkatesh, Morris, Da-

vis, & Davis, 2003) do not fully explain the nature of media

behaviour, but rather the factors determining media behaviour.

Therefore, for the field of HCI, the challenge remains to provide

concrete guidance for media system design and to fully understandthe social implications of different media behaviour. This is crucial

because media technologies expand and converge into each other

and more types of people use them for a variety of different things,

it becomes harder to understand and assess the increasingly frag-

mented media behaviour, in addition to the failure to highlight

participation inequalities in terms of a digital divide. Applying a

user-typology approach, such as the MUT presented in this article,

will produce a better understanding of the user. Hence, this article

claims that the HCI research community can learn from customer-

segmentation studies (e.g. Bunn, 1993) used for the general identi-

fication of media-user profiles.

This article examined how an MUT would provide a more holis-

tic, unified and precise measure of media behaviour. A holistic and

structured overview of the huge variety of media usage in terms of

an MUT will also help researchers and practitioners in their efforts

to identify different user needs per user type for the development

of new media. To introduce new services successfully, the media of

the future should learn from the manner in which different user

types communicate, the platforms that they use and the manner

of use, and accordingly adapt their services to achieve a match be-

tween different users and their preferences. This could also be a

starting point to define the essential types of behaviour according

to their digital competence, so that users can cope successfully in

an increasingly digital society.

An MUT for identifying typical user types in new media is an

important contribution to (a) the understanding and measure

media behaviour, (b) digital divide, (c) measuring the impact or

implications of different media usages, and (d) determining how

usage and user skills change over time. A unified model of the usertypes will allow practitioners and academics to communicate with

a common understanding of how a specific user type is identified

and how distinct usage patterns may be unique. And it will allow

media developers to improve the user experience by providing tar-

geted services to users according to their preferences or media

behaviour. The initial model for an MUT will provide a useful start-

ing point for a common approach to understand media behaviour

and scientific study of media-user typologies. However, to create

a common MUT that will be truly adopted by researchers and

designers, further research and evidence is needed as well as great-

er dialogue and collaboration between theorist and designers of

new media systems.

Acknowledgments

The research leading to this article has received funding from

the CITIZEN MEDIA Project (038312) in the European Community’s

Sixth Framework Programme (FP6-2005-IST) and the RECORD-pro-

ject, supported by the Verdikt program in the Research Council of

Norway. I would like to thank all the partners involved in these

projects, in particular Jan Heim, Amela Karahasanovic and Marika

Lüders, all at SINTEF ICT, for the reviews and suggestions.

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BRANDTZAEG Towards a Unified Media-User Typology MUT