brandtzaeg towards a unified media-user typology mut
TRANSCRIPT
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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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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
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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
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( 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
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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
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Table 1 (continued)
References Research design/media/context User typologies
(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)
Questionnaire survey
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)
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Table 1 (continued)
References Research design/media/context User typologies
(10) Livingstone and
Helsper (2007)
Representative sample UK (age: 9–19)
Questionnaire survey
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
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Table 1 (continued)
References Research design/media/context User typologies
(16) DeYoung and
Spence (2004)
Not representative (age 17–67)
323 participants
Questionnaire survey
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
Survey questionnaire
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)
Survey questionnaire
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
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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)
References Research design/media/context User typologies
(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)
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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)
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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
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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
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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
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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.
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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.
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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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