CONDITIONS FOR TECHNOLOGY ACCEPTANCE
Broadening the Scope of Determinants of Ict Appropriation
Pieter Verdegem and Lieven De Marez
Research Group for Media & ICT (MICT), Ghent University (UGent)
Interdisciplinary Institute for Broadband Technology (IBBT), Korte Meer 7, 9000 Ghent, Belgium
Keywords: User research, technology acceptance, Ict appropriation, adoption determinants, usage determinants.
Abstract: Regarding the ICT industry, we have a fast evolving sector being under pressure due to a growing number
of failing innovations. Companies are forced to be the first on the market and for that reason thorough
insights in user preferences are indispensable. New technological innovations often fail because too much
attention is given to (technical) product-related features without taking into account the most important
parameters of user acceptance. In this paper we highlight some theoretical considerations on this matter.
First of all, we propose an approach in which more traditional and often scattered vision(s) on adoption
determinants are broadened into an integrated framework. The approach should provide a stronger base for
better targeting of (new) users of technologies. Second, we elaborate on this by rethinking these
determinants with regard to the later adopters. Later adopters (or even non-users) are often ignored in
technology acceptance research. However, especially for policy purposes, the understanding of why people
do not adopt or do not use ICT is strongly relevant in the light of the development of the information
society. Both approaches – focusing towards early as well as late adopters – are illustrated by case studies
starting from a common framework.
“Consumer research has shown that knowledge about
the user tends to be limited … It is quite self-evident
that both social and commercial policies will benefit
from accurate insight into the different parameters
determining the success (acceptance and use) of these
technologies according to a user’s point of view.”
(Burgelman, 2000: 236)
1 INTRODUCTION
Conditions for ‘technology acceptance’ have always
been a central pillar in all kinds of approaches of
studying the acceptance and appropriation of new
innovations: ranging from the diffusion theory-based
approaches focusing on perceived technology
characteristics since the early 60’s, over more usage-
oriented theoretical approaches since the 80’s to
more industry-oriented studies/approaches focusing
on image- and network-related determinants.
However, in today’s ICT-environment a broader
and more comprehensive framework for under-
standing determinants or conditions for technology
acceptance is more than ever needed, in order to
obtain the necessary insights to face the challenges
of both ICT managers and policy makers. Due to the
exponentially increased offer of ICT-innovations
(and as a consequence more failing technologies), all
stakeholders involved are desperately seeking for
accurate insights into adoption determinants as a
basis for more effective introduction and targeting
strategies (Lin, 1998: 95; Talukdar et al, 2002: 97;
Ziamou, 2002: 366; Chen et al, 2002: 706;
Venkatesh et al, 2003: 426). From a policy point-of-
view such insights into drivers and barriers for
adoption and usage of ICT are necessary in order to
set up adequate e-inclusion measures (Chaudhuri et
al, 2005: 737-739; Milner, 2006: 177; Trkman et al,
2008: 102).
In this paper we introduce a framework that
could help to refine our thinking on this. First, we
broaden the scope on adoption determinants by
integrating the existing but fragmented approaches
into a more comprehensive one. This becomes more
important for industrial and marketing purposes, as a
thorough understanding of the user – the customer –
is necessary for acceptance. Second, we elaborate on
this by paying attention to approaches that go
beyond adoption diffusion. More specifically, policy
makers are seeking to understand parameters that
have an influence on the impact of ICT adoption and
use, in order to formulate effective measures in the
light of overcoming digital inequalities.
292
Verdegem P. and De Marez L. (2008).
CONDITIONS FOR TECHNOLOGY ACCEPTANCE - Broadening the Scope of Determinants of Ict Appropriation.
In Proceedings of the International Conference on e-Business, pages 292-299
DOI: 10.5220/0001906802920299
Copyright
c
 SciTePress

2 DETERMINANTS FOR ICT
ACCEPTANCE
2.1 Broadening the Scope on Adoption
Determinants
With ‘adoption determinants’ we refer to parameters
that influence technology acceptance in terms of the
actual adoption decision (De Marez, 2006: 189-192).
For a long time and to a large extent influenced by
the dominant technological deterministic paradigm,
demographic variables were supposed to have an
important influence on that adoption decision (see
Rogers, 1983; Rogers, 2003). However, many
scholars have stated that – in addition to the more
traditional parameters – this view should be
extended to an approach based on ‘attitudinal’
adoption determinants (Bergman et al, 1995; Plouffe
et al, 2001; Atkin et al, 2003; Leung, 1998).
.
Attitudinal determinants are related with more
subjective perceptions of innovation characteristics
and personality traits.
The approach of this attitudinal adoption
determinants was mainly inspired by the diffusion
theory, in which innovations were supposed to have
a set of five characteristics (relative advantage,
complexity, compatibility, trialability and
observability) of which the subjective perception
determines one’s attitude towards the technology,
and one’s innovativeness or timing of adoption
decision (Rogers, 1983; Rogers, 2003)
.
The
perception of each of these characteristics is
assumed to have a strong relationship with the
innovativeness of an individual. Innovators and early
adopters, for example, are assumed to have a higher
perception of relative advantage than the (later)
majority segments, together with a lower perception
of complexity of the innovation (contrary to the later
adopters).
Over the years, the increasing attention paid to
these ‘attitudinal’ adoption determinants resulted in
a considerable yet cluttered extension of the original
set of five adoption determinants. The convergence
with social psychology theories such as the Theory
of Reasoned Action (TRA) (Fishbein, 1967;
Fishbein & Ajzen, 1975), (Decomposed) Theory of
Planned Behaviour ((D)TPB) (Ajzen, 1991; Taylor
& Todd, 1995) and Technology Adoption Model
(TAM) (Davis, 1986; Davis, 1989) in particular led
to an extremely valuable - yet fragmented - increase
in (research on) adoption and determinant models.
Some scholars consider one or two extra
determinants (Holak & Lehmann, 1990), while
others considered eight (Plouffe et al.; 2001), ten
(Choi et al., 2003) or more determinants.
Downside of this increased attention is that
researchers nowadays are confronted with a ‘lack of
overview’, since the increased multidisciplinary
interest entails a cluttered and inconveniently
arranged entirety of determinants. Evidently, more
accurate insight into adoption determinants requires
an insight in more than the five determinants of
Rogers’ diffusion theory, but it remains unclear how
many and which determinants should be taken into
account. Since a convenient overview of
(potentially) relevant adoption determinants for ICT
innovations is still lacking to date (Busselle et al,
1999; Randolph, 1999; Hadjimanolis, 2003 – an
exception is the work of Venkatesh et al, 2003) we
conducted a meta-analysis on determinants for ICT
adoption (De Marez, 2006). Comparable to the
development of UTAUT (Unified Theory of
Acceptance and Use of Technology, Venkatehs et al,
2003: 446-465), we started from different studies
and existing theoretical models (in the field of
communication, marketing as well as social
psychology) whose central building block was
mainly diffusion theory’s set of five determinants.
This resulted in an extension to 19 determinants, in
which we distinguish ten innovation-related
characteristics (perceptions), eight adopter-related
characteristics, and the impact of the marketing
strategy (see table 1 below).
Table 1: Extension of adoption determinants (De Marez et
al, 2007: 82).
ADOPTION
DETERMINANT
ASSUMED
RELATION WITH
INNOVATIVENESS
INNOVATION RELATED CHARACTERISTICS
Compatibility +
Complexity -
Cost -
Enjoyment +
Observability +
Relative advantage +
Reliability +
Tangibles +
Trialability +
Visibility +
ADOPTER RELATED CHARACTERISTICS
Control/Voluntariness +
Image/Prestige +
Innovativeness +
(product) Knowledge +
Opinion leadership +
Optimism +
Social influence +
Willingness to pay +
IMPACT OF MARKETING STRATEGY
Marketing (impact) +
Clearly, innovativeness and adoption decisions
seem to be determined by more characteristics than
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293

the original five initiated by Rogers’ diffusion
theory. The perception of ‘relative advantage’ for
example, can express itself in several dimensions.
The ‘perceived cost’ and ‘tangibles/aesthetics’ are
the most important of them. Most scholars relegate
to Rogers’ work in his conceptualization of
‘observability’ in terms of the perceived result
demonstrability, while some others distinguish the
latter from ‘visibility’ as the degree to which the
innovation is visible to others in its own right. It is
also important to account for the ‘perceived
enjoyment’ of using the innovation (the so-called
likeability), and ‘reliability’ as a dimension of
perceived risk that is not covered by other
determinants (‘reliability’ in this context refers to
‘performance risk’). ‘Innovativeness’, on the other
hand, is the most important personality
characteristic. It covers a multitude of sub
dimensions such as ‘venturesomeness’, ‘novelty
seeking’, ‘cosmopolitanism’, ‘variety seeking’,
‘information seeking’, etc. ‘Opinion-leadership’
needs to be considered as a separate dimension, just
as a person’s ‘optimism’ towards technology,
‘product knowledge’, ‘willingness (and ability) to
pay’, the ‘perceived impact on one’s personal
image’, the ‘perceived control’, ‘impact of social
influences’ and the ‘impact of marketing, advertising
and promotional strategies’.
If industry strategies nowadays require more
profound insight in more than the traditional five
determinants, it will largely boil down to an insight
in these 19 determinants. It will probably never be
the case that all these determinants are relevant, but
if prior-to-launch research could reveal which
determinants are the most important drivers and
barriers for which segments, this would allow to
adjust the approach of different segments. Question
remains, however, how to acquire such prior-to-
launch insight?
2.2 Elaboration of Determinants with
Regard to ‘Later Adopters’
Another challenge of research concerning the
acceptance of new technologies – especially for
policy strategies – is how to gain insight in the
profiles of later adopters. That are individuals to
whom traditionally less attention is given in
innovation studies (Selwyn, 2003: 100-101; Roe &
Broos: 91). People who step later into the innovation
circle or who even resist to do this, are often left
aside. However, research of non or later adoption
could offer fundamentally added value. First,
industry or managers could learn substantially not
only of why people adopt a new technology but also
why they are not willing to adopt. This could
provide insights in how to adjust the innovation (in
all its dimensions: product, distribution,
communication) in order to stimulate appropriation
by the overall population. On the other hand, in view
of the pervasiveness of ICT in society and the
increasing dependence on ICT in everyday life,
policy makers are obliged to think about policies
that prevent exclusion of groups of citizens in the
development of the information society. Insights in
the parameters of adoption by later adopters is
therefore of crucial importance.
The adoption of a certain technology (as for
which the determinants are discussed in 2.1.)
however, cannot be the sole focus when studying the
factors that influence technology acceptance. This
would be too much a technology deterministic and
diffusion-based approach, mainly serving ‘industry
purposes’ (how to approach the most interesting
segments of innovators, early adopters, early
majority as good and as soon as possible?). A more
elaborated focus on technology acceptance not only
requires a focus on adoption, but also on usage
determinants. In addition, a thorough understanding
of technology acceptance not only asks for a focus
on the first segments in the diffusion curve, but also
on the later segments in that curve (late majority and
laggards).
Attention for digital inequalities is, both in
scholarly publications as well as in political studies
and in the popular press and media is, an obvious
result of the euphoric ‘cyberbole’ that characterized
much of the rhetoric of new technologies since the
mid-1980’s (Gunkel, 2003: 500). Hence, profound
insights in why people lag behind in the adoption
and use of new technologies, are important in view
of the development of the information society for all.
More insights are necessary, especially when we can
conclude that business strategies and policies that
were successful in, for instance, increasing internet
penetration in the early days, may no longer be
appropriate to reach the rest of the society. And this
is most probably so in societies where a majority of
people are already connected to the internet. Thus,
policies also need insights in the most important
drivers and barriers that have an impact on the
individual’s decision to appropriate an ICT product.
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3 CASE STUDIES
3.1 Broadening for Industry &
Marketing Purposes
The above-mentioned question was also the central
question in two recent case studies conducted by
Research Group MICT-IBBT. Both studies were set
up to acquire the necessary insights in attitudinal
adoption determinants for two ‘mobile innovations’,
in preparation for their commercial launch in
Belgium. In the first case-study (2006) a sample of
269 respondents was questioned about their attitudes
towards a new ‘mobile news’-application in the
context of the IBBT-project ROMAS. In the second
case study (2007) a representative Flemish sample of
405 respondents was questioned about their attitudes
towards mobile television services. In the first study,
data were collected by means of an online survey
(after a two months period in which the respondents
could test the mobile news application). In the
second study, data were collected by means of 40
minutes during CAPI-interviews (in which
respondents were shown short movies on DVD in
order to familiarize them with mobile tv applications
and usage moments). In both studies potential
adopter segments (innovators up to laggards) for the
innovations were forecasted by means of the Product
Specific Adoption Potential scale (De Marez,
Verleye, 2004a,b), and the 19 determinants were
transformed into a battery of 47 Likert statements
(cf. table 2), to be answered on 5-point agreement
scales (varying from 1: ‘I do not agree at all’ to 5: ‘I
fully agree’).
Table 2: Operationalisation of determinants in 47 Likert
statements (applied to the mobile news/TV cases).
COMPATIBILITY — LIFESTYLE AND
PERSONALITY
7. Consultation of mobile news/TV services fits my lifestyle
39. If I buy a new mobile, it has to be a model that fits my
personality
COMPATIBILITY — (TECHNOLOGICAL)
30. I am interested in subscribing to mobile news/TV services?
but I would mind if that would imply an investment in a new
device.
13. Mobile news/TV services are only interesting to me as a
part of the subscription on other mobile services.
COMPLEXITY/COMFORT LEVEL
8. I fear that mobile new/TV services application offers
different possibilities, which makes It rather complicated.
20. The mobile news services application seems very user-
friendly to me.
29. The mobile news/TV services application offers different
possibilities, which makes it rather complicated.
CONTROL/SELF-EFFICACY
46. I have no problem to sort out on myself how mobile
news/TV application work and must be installed.
Table 2: Operationalisation of determinants in 47 Likert
statements (applied to the mobile news/TV cases) (cont.).
COST (RELATIVE ADVANTAGE)
1. Subscription on mobile news/TV services seems expensive
to me.
5. Mobile news/TV services will probably cost too much for
many people.
EFFECTIVENESS (RELATIVE ADVANTAGE)
36. Mobile news/TV services will certainly make some things
easier for me.
ENJOYMENT
4. Mobile news/TV service seems very user friendly to me.
IMAGE PRESTIGE
33. Subscribing to mobile news/TV services applications
would have a positive impact on my image and social status.
38. Subscribing to mobile news/TV services beams out a
certain standing.
INNOVATIVENESS
6. I think to be among the first to subscribe to such mobile
news/TV services.
34. Based on what I already knew about the application and
what I have learned today, I will certainly search for more
information about subscribing to these services.
MARKETING STRATEGY
26. If I would subscribe to a mobile news/TV application, it
would be important to me that it is provided by a well-known
'brand'.
27. If I would consider mobile news/TV adoption, I would
first check the ads, brochures and promotions.
OBSERVABILITY — RESULT DEMONSTRABILITY /
COMMUNICABILITY
24. I am perfectly able to explain the strengths and the
weakness of mobile news/TV services to others
OBSERVABILITY — VISIBILITY
12. One of the nice things of a mobile news/TV application is
that it is something to show off with among friends.
17. I see many people in my environment who use mobile
news/TV services.
OPINION LEADERSHIP
15. If mobile news/TV would be introduced on the market,
people in my environment will certainly come to me for
advice.
OPTIMISM
44. The fast technological developments are a good thing.
45. If you don't want to run behind, adoption of new
technologies is necessary.
PERCEIVED RISK (FINANCIAL)
18. I fear that subscribing to a mobile news/TV application
would be way above my budget.
PERCEIVED RISK (IMPLEMENTATION)
23. If I would have to use such mobile news/TV applications
on my own, I don't think I would manage.
PERCEIVED RISK (SOCIAL)
21. If I would use mobile news/TV services, people in my
environment would look odd at me.
PRODUCT KNOWLEDGE
19. I recently send something about mobile news/TV services
or recently talked to someone about it.
35. I consider myself well-informed about the possibilities and
(dis)advantages of mobile news/TV services.
RELATIVE ADVANTAGE
11. The advantages of mobile news/TV services are clearer to
me than the disadvantages.
40. I don't see where or when to use mobile news/TV services.
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295

Table 2: Operationalisation of determinants in 47 Likert
statements (applied to the mobile news/TV cases) (cont.).
RELATIVE ADVANTAGE
11. The advantages of mobile news/TV services are clearer to
me than the disadvantages.
40. I don't see where or when to use mobile news/TV services.
RELIABILITY
31. I doubt the reliability and proper functioning of the mobile
news/TV services application.
SOCIAL INFLUENCE
9. Most people in my environment will certainly be enthusiast
about the mobile news/TV application.
SOCIAL INFLUENCE — COMPLIANCE
2. If 'mobile news/TV usage' would be considered as 'trendy'
in my environment, I would certainly consider subscribing to
it.
10. My direct environment will probably expect me to be one
of the first to use mobile news/TV services.
32. Even if I am interested, I would not subscribe if my
environment would be negative about mobile news/TV
applications.
SOCIAL INFLUENCE — IDENTIFICATION
47. If I would use mobile news/TV services, it would certainly
tell something about me and my personality.
SOCIAL INFLUENCE — INTERPERSONAL
COMMUNICATIONS
3. Before subscribing to a mobile news/TV application, I
would like the advice of some people.
16. Mobile news/TV services will certainly be a topic of
discussion among my friends and family.
SOCIAL INFLUENCE — NETWORK
EXTERNALITIES
37. I am interested in subscribing to mobile news/TV services,
but only if there are sufficient people in my direct environment
doing so. Otherwise, the application wouldn't have much value
to me.
TANGIBLES (RELATIVE ADVANTAGE)
25. As the mobile news/TV services is presented and testable
now it has an attractive design and style.
14. If I would consider buying a new mobile, design would be
a very important buying argument to me.
TRIALABILITY — PHYSICAL
41. I would like to try out mobile news/TV services before
subscribing to them.
TRIALABILITY — VICARIOUS
28. Before subscribing or adopting mobile news/TV services I
prefer to look around for a while and see how others are
experiencing the application.
VOLUNTARINESS
42. If I would subscribe to mobile news/TV services, it would
completely be my own decision. No one would influence me
in making that decision.
WILLINGNESS-TO-PAY
22. Even if it costs a bit more, mobile news/TV is something I
really want.
The transformation of determinants into a scale
of 47 items is the combined result of desk research
and qualitative research by means of focus group
interviews. A first phase of desk research resulted in
a long list of statements of 19 determinants used in
other studies and models (both diffusion theory
based models as well as social psychology based
models). In addition to this, the long list was verified
in four focus group interviews with the goal to select
the best way to translate the item into a statement.
All 269 (mobile news study) and 405 (mobile
television study) respondents completed the entire
questionnaire. The most important results show a
striking difference between the attitudes or
determinants for both innovations. In the average
agreement scores, for example, it can be noticed that
a determinant as ‘tangibles’ (14, 25) is more
important for mobile television than for mobile
news. Regarding ‘reliability’ (31) people seem to be
more sceptical for mobile news, while the ‘perceived
control’ (46) seems to result in a higher score for
this new mobile application. ‘Product knowledge’
(19, 35) on the other hand is lower for mobile
television; etc … . With an R² ranging between .503
and .795 for the earlier adopters and early majority,
these 47 ‘determinant operationalisations’ certainly
seem to be a good set of variables to explain the
variance in the dependent variable ‘adoption
intention’. Even for the later adopter segments this
R² still ranges between .34 and .42. Detailed
information about the psychometric reliability and
validity can be found in De Marez et al (2007: 86-
88).
Thus, for both technological innovations, this set
of attitudinal determinant statements explains
adoption intentions quite well, but there remain
many differences in the significant determinants for
the different innovations and adopter segments.
‘Lifestyle compatibility’ (39) for example is only
significant for the mobile television’s innovators, not
for mobile news. Also the ‘cost perception’ (1) is
only significant in the mobile television case
(laggards). ‘Trialability’ (41) then is significant in
both cases, but not for the same segments. Other
determinants such as the perceived impact of
adoption on one’s ‘image’ (33) was only significant
for mobile news’ innovators and laggards. So, we
can notice many differences in attitudes, as well
when compared over the two cases, as compared
over the different adopter segments. This
emphasizes the need for a product- and segment
specific approach when studying adoption
determinants.
3.2 Elaboration for Policy Purposes:
Analysis of Non-Adopters
The need for more profound insights in why people
do not use ICT innovations, for instance computer
and internet, is an important question for policy
makers. For instance, as more people are connected
and taking full advantage of new possibilities that
are offered via internet, government cannot ignore
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those groups that are not yet connected. So, policy
makers should at least take the initiative to set up
measures that can help people – who risk to be
excluded – to enhance their participation in the
information society.
Research Group MICT-IBBT was commissioned
with this research question by Fedict (The federal
public agency for information and communication
technology) of the Belgian federal government. As
the responsible agency for stimulating ICT
acceptance and use in society, Fedict needed a
scientific supported base for setting up new
initiatives. The research results presented below
draw on the experience that the Belgian government
has acquired through the ‘Internet for all’ project in
2006. The latter was set up in collaboration with
ISPs, PC manufactures and retailers, and entailed the
provision of an affordable package (€750 - €1000)
deal to customers, consisting of a PC, an internet
connection plus a training session. It was calculated
that the project contributed to 16% of the increase of
new internet connections over a period of one year
(Verdegem & Verhoest, 2008: 38). A critical
evaluation of the ‘Internet for all’ project revealed
different elements, two of which inspired our
research. The first was merely the confirmation of
what could be expected. Not all of the groups in
society were equally well served by the campaign.
For instance, for some individuals the proposed
offering was too expensive. The second source of
inspiration was an incidental call of a representative
of a professional organization of physical therapists
that proposed to target the campaign also towards
the members of his organization. These two
observations triggered a reflection that inspired the
new policy approach and adjoining research.
The new approach is articulated around the
concept of ‘relative utility’, a sociological
reinterpretation of the economic concept of
‘marginal utility’. Contrary to the other case studies
illustrated in this paper, of which the goal was to
broaden the insights concerning adoption
determinants, this case is focused on the elaboration
and interpretation of parameters of ICT
appropriation. By paying attention to both the
adoption as well as the usage decision we wanted to
provide input for measures that would help to
stimulate ICT adoption and use. Following the
relative utility approach, the assumption is that the
specific combination of conditions in terms of access
to ICT, skills to master the devices and attitudes
towards the technology, has an impact on whether
people will use ICT or not. More specifically, based
on the combination of perceptions of people towards
access, skills and attitudes (ASA) it becomes
possible to determine a hypothetical ‘turning point’
for ICT use, namely the point at which the benefits
will outweigh the cost of appropriating an ICT
product for a certain category of users.
On a practical level, in order to set up effective
e-inclusion measures, the advantage of this method
is that groups of individuals with relatively
homogeneous ASA-profiles, can easily be identified
and reached by policy makers. Very often they are
represented by professional or social organizations
that know how to reach them and are willing to
cooperate with government. A specific offering can
then be proposed to these groups, taking into
account the specificities of their ASA-profile and
socio-economic background.
The approach draws upon the assumption that
members of socio-demographically and socio-
economically homogeneous groups yield similar
perception in terms of access, skills and attitudes
towards ICT. This hypothesis was tested by means
of a quantitative survey (personal interviews with
184 respondents). The research population was
composed of a theoretical sampling, meaning that
we selected individuals based on a limited number
of characteristics, i.e. variables of which previous
research has shown that they are of major
importance for (non-)adoption of ICT. In the
research we recruited individuals (non-users) from
ten groups, varying from single mothers with
children to physical therapists. This resulted in
certain prototypical profiles, exemplary for the
societal diversity without being representative for
the overall population (for detailed information see
Verdegem & Verhoest, 2008).
In order to map the respondents’ perceptions of
computer and internet use at home, we presented
them with a list of statements. The statements were
based on the same adoption determinants that are
mentioned above (see table 2). A number of these
statements aimed at obtaining information about the
respondent’s specific ASA-profile: 1) positive or
negative attitudes towards computer and internet at
home; 2) the presence or lack of skills and
competences towards using ICT and 3) the presence
or absence or barriers to access ICT. Other
statements served as measurement scales to gain
insights in more generic factors such as, for
example, the influence of social networks or
marketing strategies of the ICT industry.
Based on the answers of the respondents on the
statements cluster analysis revealed five distinctive
groups of domestic non-users of computer and
internet:
• Incapable refusers;
• Self-conscious indifferents;
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297

• The willing but incapable;
• Skilled ICT-lovers with limited access;
• Price sensitive pragmatists.
The clusters demonstrate that non-adopters or non-
users should not be seen as one generic group. Each
profile represents a different combination of the
factors investigated, in which each factor carries a
different weight. Statistical testing was also
conclusive about the relationship between the group
membership (from theoretical sampling) and the
membership of the ASA-profiles (Pearson Chi-
Square p ≤ 0,01). As such, we found empirical
foundation for the assumption that homogeneous
groups – in terms of socio-demographic and socio-
economic characteristics – result in generic ASA-
profiles.
Following on this quantitative research
qualitative in-depth interviews and focus group
interviews were organized to refine our thinking of
why people do not use ICT and to examine which
leverages could lift them over the turning point
between non-usage and usage. The results of both
research stages show the advantage of the approach
proposed and offers the opportunity for policy
makers to set up measures to stimulate later adopters
to ICT appropriation. These measures could be more
effective as they are based on strategies of
segmentation and differentiation, taking into account
the different profiles of these individuals. The
elaboration of adoption and usage determinants is
thus necessary to gain insight in a group of
individuals that are often ignored in innovation
research.
4 CONCLUSIONS
Our research results clearly show the need for a
thorough understanding of user attitudes towards
ICT acceptance. As more technological innovations
are introduced in rapid succession and an increased
number of those innovations is failing, accurate
insights in the determinants towards adoption and
use become increasingly important. We could state
that both our theoretical reconsiderations as well as
the empirical foundations of them, provide ICT
managers as well as policy makers with useful input
in support of their innovation strategies. As a matter
of fact, the development of an information society
for all serves both economic as social purposes.
The approach proposed started from the same
common framework, i.e. more traditional adoption
determinants who are founded by technological
deterministic inspired paradigms. We illustrated that
these parameters, who have an impact on technology
acceptance, should be reconsidered. The described
elaboration contains both an exercise of broadening
and deepening.
First of all, it is important to examine which
determinants are of major relevance in order to
forecast how new innovations should be brought to
the market to persuade the potential (first) adopters,
or those interested in the product. Not only the
product development in terms but also the targeting
and marketing campaigns strongly ask for accurate
insights into user preferences. Particularly in the
(pre-)launch phase.
In addition, the framework of adoption
determinants should also be re-evaluated with regard
to later adopters. People who enter the adoption
process in a later stadium – or who even resist to
adopt – may have clear reasons for that. However,
deep understanding of who is making less (or even
no) use of information technologies remains weak.
Nevertheless, this is of major importance for both
policy makers, as well as for ICT managers.
So, in a nutshell, our approach contains both a
managerial as a policy relevance. Furthermore, we
also hope that this paper contributes to both
theoretical reconsiderations as well as the
methodological foundation of technology acceptance
research.
ACKNOWLEDGEMENTS
This work was supported by the IBBT projects
ROMAS (Research on Mobile Applications &
Services) and MADUF (Maximizing DVB Usage in
Flanders). Both projects are funded by the
Interdisciplinary Institute for Broadband Technology
(IBBT) and a consortium of companies. The last
case study is funded by the Federal Public Agency
for Information and Communication Technology
(Fedict – Belgian Government).
REFERENCES
Ajzen, I. (1991). The theory of planned behavior.
Organizational Behavior and Human Decision
Processes, 50(2): 179-211.
Atkin, J.A., Neuendorf, K., Jeffers, L.W. & Skalski, P.
(2003). Predictors of audience interest in adopting
digital television. The Journal of Media Economics,
16(3) 159-173.
Bergman, S., Frissen, V. & Slaa, P. (1995). Gebruik en
betekenis van de telefoon in het leven van alledag. In:
Rathenau Instituut, Toeval of noodzaak? Geschiedenis
van de overheidsbemoeienis met de informatie-
ICE-B 2008 - International Conference on e-Business
298

voorziening (277-325). Fatima Reeks, Amsterdam:
Meboprint.
Burgelman, J.C. (2000). Innovation of communication
technologies: some general lessons for the future from
the pas. In: Cammaerts, B. & Burgelman, J.C. (ed.)
Beyond competition: broadening the scope of
telecommunications policy (229-238). Brussel: VUB
Press.
Busselle, R., Reagan, J., Pinkleton, B. & Jackson, K.
(1999). Factors affecting internet use in a saturated-
access population. Telematics & Informatics, 16(1-2),
45-48.
Chaudhuri, A., Flamm, K.S. & Horrigan, J. (2005). An
analysis of the determinants of internet access.
Telecommunications Policy, 29(9-10), 731-755.
Chen, L.D., Gillenson, M.L. & Sherrel, D.L. (2002).
Enticing online consumers: an extended technology
acceptance perspective. Information & Management,
39(8), 705-719.
Choi, H., Choi, M., Kim, J. & Yu, H. (2003). An empirical
study on the adoption of information appliances with a
focus on interactive TV. Telematics and Informatics,
20(2), 161-183.
Davis, F.D. (1986). A technology acceptance model for
empirically testing new end-user information systems:
theory and results. Unpublished PhD dissertation,
Cambridge: MIT Sloan Management School.
Davis, F.D. (1989). Perceived usefulness, perceived ease
of use and user acceptance of information technology.
MIS Quarterly, 13(3), 319-340.
De Marez, L., Verleye, G. (2004a). ICT-innovations
today: making traditional diffusion patterns obsolete,
and preliminary insight of increased importance.
Telematics and Informatics, 21(3): 235-260.
De Marez, L., Verleye, G. (2004b). Innovation diffusion:
the need for more accurate consumer insight.
Illustration of the PSAP-scale as a segmentation
instrument. The Journal of Targeting, Measurement
and Analysis for Marketing, 13(1): 32-49.
De Marez, L. (2006). Diffusie van ICT-innovaties:
accurater gebruikersinzicht voor betere introductie-
strategieën. Unpublished PhD dissertation,
Department of Communication Studies, Ghent
University.
De Marez, L., Vyncke, P., Berte, K., Schuurman, D. & De
Moor, K. (2007). Adopter segments, adoption
determinants and mobile marketing. Journal of
Targeting, Measurement and Analysis for Marketing,
16(1), 78-95.
Fishbein, M.A. (1967). Readings in attitude theory and
measurement. New York: Wiley.
Fishbein, M.A. & Ajzen, I. (1975). Belief, attitude,
intention and behavior: an introduction to theory and
research. Reading MA: Addison-Wesley.
Gunkel, D.J. (2003). Second thoughts: toward a critique of
the digital divide. New Media & Society, 5(4), 499-
522.
Hadjimanolis, A. (2003). The barriers approach to
innovation. In: Shavinina, L.V. (ed.) The handbook of
innovation (559-573). Oxford, UK: Pergamon,
Elsevier.
Holak, S.L. & Lehmann, D.R. (1990). Purchase intentions
and the dimensions of innovation: an exploratory
model. Journal of Product Innovation Management,
7(1), 59-73.
Leung, L. (1998). Lifestyles and the use of new media
technology in urban China. Telecommunications
Policy, 22(9) 781-790.
Lin, C.A. (1998). Exploring personal computer adoption
dynamics, Journal of Broadcasting & Electronic
Media, 42(1), 95-112.
Milner, H. (2006). The digital divide: the role of political
institutions in technology diffusion. Comparative
Political Studies, 39(2), 176-199.
Plouffe, C.R., Vandenbosch, M. & Hulland, J. (2001).
Intermediating technologies and multi-group adoption:
a comparison of consumer and merchant adoption
intentions towards a new electronic payment system.
Journal of Product Innovation Management, 18(2),
65-81.
Randolph, C. (1999). Why do we hate Microsoft and still
use word? Proceedings of the 10
th
Australasian
Conference on Information Systems, 736-748.
Roe, K. & Broos, A. (2005). Marginality in the
information age: the socio-demographics of computers
disquietude. A short research note. Communications:
the European Journal of Communication, 30(1), 91-
96.
Rogers, E.M. (1983). The diffusion of innovations (3
rd
ed.).
New York: The Free Press.
Rogers, E.M. (2003). Diffusion of innovations (5
th
ed.).
New York; The Free Press.
Selwyn, N. (2003). Apart from technology: understanding
people’s non-use of information and communication
technologies in everyday life. Technology in Society,
25, 99-116.
Talukdar, D., Sudhir, L. & Ainslie, A. (2002).
Investigating new product diffusion across products
and countries. Marketing Science, 21(1), 97-114.
Taylor, S. & Todd, P. (1995). Decomposition and
crossover effects in the theory of planned behavior: a
study of consumer adoption intentions. International
Journal of Research in Marketing, 12(2), 137-155.
Trkman, P., Blazic, B.J. & Turk, T. (2008). Factors of
broadband development and the design of a strategic
policy framework. Telecommunications Policy, 32(2)
101-115.
Verdegem, P. & Verhoest, P. (2008). The ‘relative utility’
approach for stimulating ICT acceptance: profiling the
non-user. European Journal of ePractice, Nr. 3, May
2008, http://www.epracticejournal.eu/document/4717.
Venkatesh, V., Morris, M.G., Davis, G.B. & Davis, F.D.
(2003). User acceptance of information technology:
toward a unified view. MIS Quarterly, 27(3), 425-478.
Ziamou, P. (2002). Commercializing new technologies,
consumers’ response to a new interface. Journal of
Product Innovation Management. 19(5), 365-374.
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