Content uploaded by Martina Ziefle
Author content
All content in this area was uploaded by Martina Ziefle
Content may be subject to copyright.
G. Leitner, M. Hitz, and A. Holzinger (Eds.): USAB 2010, LNCS 6389, pp. 513–529, 2010.
© Springer-Verlag Berlin Heidelberg 2010
Technical Expertise and Its Influence on the Acceptance
of Future Medical Technologies: What Is Influencing
What to Which Extent?
Martina Ziefle and Anne Kathrin Schaar
Human Technology Centre (Humtec)
RWTH Aachen University, Aachen
Theaterplatz 14, 52062 Aachen
{Ziefle,Schaar}@humtec.rwth-aachen.de
Abstract. In this research we examine the influence of technical expertise on fu-
ture medical technology. Technical expertise is assumed to positively influence
the acceptance of modern technologies, and there is evidence within the informa-
tion and communication technology (ICT) sector for this. While no one would
seriously dispute this basic impact of technical expertise on technology accep-
tance, it is far from clear what the main drivers of technical expertise are. In or-
der to understand the complex nature of expertise on the one hand and its impact
on the acceptance of other technology domains on the other, an empirical ap-
proach was undertaken. 100 participants (19–75 years) participated in a survey,
in which the acceptance of a medical mobile device was explored. Outcomes
show (1) that technical expertise is a highly complex construct entailing different
facets (knowledge, motivational, emotional and pragmatic components), which
are influenced by age and gender of respondents (2) technical expertise in the
ICT domain decisively modulates acceptance of medical technology. Interest-
ingly, a low technical expertise does not only reduce the acceptance of the
pro-using arguments, but is specifically related to a high confirmation of contra-
using arguments.
Keywords: Technical expertise, technical literacy, perceived technical compe-
tence, distrust in technology, technology acceptance, medical technology, ICT.
1 Introduction
One of the most important social and societal challenges already in these days, but
also in the near future is the contemporaneous occurrence of three major develop-
ments: the aging of societies, the ubiquity of mobile wireless technologies and a con-
siderable broadening of technical using contexts (context diversity) and user groups
(user diversity). Old and increasingly older users will have to use technologies in
many contexts, ranging from information and communication technology (ICT), over
mobility services to medical technologies [1], [2]. The usage of these technologies is
to a considerably lesser extent voluntary than it had been in former times; increasingly
ICT usage represents a necessity for older people in order to have access to social life
514 M. Ziefle and A.K. Schaar
and to maintain an independent living at home. Especially in Western Europe, the
imbalance of generations is noticeable and we have to expect a prominent arising of
this phenomenon, in combination with personal and structural societal consequences.
In this context a lack of caregivers is anticipated and in the opposite a huge number of
old and frail persons that are reliant on help [3], [4]. This future scenario makes it
essential to think about possible solutions, which entail a complex bunch of measure-
ments on different societal levels.
Future medical technologies in combination with mobile ICT usage might be one
promising solution in this supply shortfall [5], [6]. Due to their overall availability,
mobile (medical) technologies are assumed to be especially suited to maintain or even
enhance older and chronically ill people’s mobility, independence and safety. Also,
the increasing shortage in the available caring staff- relatives, nurses, caregivers, or
physicians could be relieved by mobile electronic assistance, as patients could be
remotely cared. Mobile digital medical assistants could also help to minimize hospital
stays, and, in so doing enable not only patients an independent life in a domestic envi-
ronment, but also a relief of the threatening overcharging of sickness funds [5]. Cur-
rently, different kinds of mature and sophisticated medical assistive technologies are
available to enhance older and ill peoples.
However, recent experience shows that it is not predominately the technical barrier,
which hampers a successful rollout and a broad responsiveness of users. Rather, far-
reaching acceptance barriers are prevalent which represent serious obstacles to techni-
cal solutions [7], [8], [9]. One major reason for this reluctant acceptance and a still
negative evaluation might be due to the fact that current developments in this sector
are predominately focusing on technical feasibility, inspired by technical disciplines,
in combination with medical and computer science knowledge, while the “human
factor” and the consideration of users’ needs in these systems are fairly underdevel-
oped [10], [11], [12].
Still, a barrier free and broadly accepted utilization of mobile devices is supposed
to happen “along the way”. This attitude ignores the enormous difficulties of older
users to handle technical devices properly and their hesitant technology acceptance,
but also the considerable knowledge gap about the genesis of technical experience.
1.1 Acceptance of Medical Technology
Talking about the acceptance of technology in general is practiced meanwhile for
about 25 years [14]. Especially the 1980ies and 1990ies, with the diffusion of
computers and the Internet, pushed the subject as a prominent issue in research of
different scientific disciplines (e.g. psychology, sociology, economics, anthropology,
linguistics, and cultural studies). As technology cycles are increasingly faster, tech-
nology acceptance continued to be a key research issue. Technical products are only
successful on the long run if users perceive them as useful and easy to use, e.g. [15],
[16], [17], [18].
Though research has made significant efforts in explaining and predicting technol-
ogy acceptance of ICT, the knowledge about factors, determinants and situational
aspects impacting acceptance is still limited. With the increasing diversity of users
(age, gender, technical generation, culture), the diversity of technical systems (visible
vs. invisible, local vs. distributed) and using contexts (fun and entertainment, medical,
Technical Expertise and Its Influence 515
office, mobility), more aspects are relevant for understanding users' acceptance –
beyond the ease of using a system and the perceived usefulness. In addition, studies
dealing with technology acceptance had been mostly considered ICT within the work-
ing context, and it is highly disputable if outcomes might be transferable to other
technology and using contexts.
Furthermore, most studies are limited to technology acceptance of young, experi-
enced and technology-prone persons - a user group whose technology acceptance
patterns seem not to apply to the broad variety of users nowadays confronted with
technology. Technology is no longer an issue for a small number of interested people.
Nowadays the usage of technology concerns everyone, men as well as women, and
not only young people but also the older ones. Also, the using conditions are different
compared to former times, especially in the medical sector: here, (mobile) technology
covers vital and essential parts of life, and the usage is not voluntary any more, but
highly correlated with involuntariness and dependency.
Recently, research started to include the using context in combination with differ-
ent technology types in their impact on technology acceptance. Increasingly it is un-
derstood that technology acceptance is neither static, nor independently from the
specific using context in which a technology is applied [12], [10]. Also, the impact of
user diversity and the different abilities and restrictions that influence acceptance
receive attention [11]. Among the user characteristics, age and gender effects had
been identified to considerably impact the technology acceptance, and, as a strong
moderator, users’ expertise of technology, which decisively determines the way users
handle and evaluate technical devices [17], [18], [19], [20].
1.2 Technical Expertise
Computer expertise is definitively one of the most important user characteristics that
influence the quality of human-computer interaction. Studies revealed that users with
a high level of technical expertise show considerably higher performance when using
technical devices [21], [22], [23], [24], [25], independently of the type of technology:
performance advantaging expertise effects had been found for the interaction with
personal computers, the Internet, as well as mobile devices (mobile and smart
phones). Also, it had been found that users with higher technical expertise revealed a
higher technology acceptance in terms of perceived ease of using a device and useful-
ness [26], [27], [28], [29], [30].
Focusing on the nature of the expertise effect it is assumed that experts in a spe-
cific domain are able to reach higher performance levels, because they possess highly
organized knowledge structures, more sophisticated procedural knowledge structures,
more elaborated mental models and problem solving strategies, which enable them to
analyze problems thoroughly and to develop flexible solutions and alternatives [21],
[24], [25], [28], [29]. Bransford et al. [31] outlined six main characteristics of exper-
tise: First of all an expert is able to recognize attributes and important patterns of
information, which cannot be seen by a novice (especially meta key patterns). Second
an expert has acquired a vast proportion of content knowledge [28], [29], [32]. And
this knowledge is organized in ways that make a reflected understanding of subject
matter possible. Third the knowledge an expert has cannot be divided into single
isolated facts. Further it is instead possible to reflect contexts of applicability. The
516 M. Ziefle and A.K. Schaar
fourth point of an expert’s characteristic says, that an expert is able to demand its
knowledge with little costs. But although an expert is absolutely in his topic it doesn’t
mean in the other way that he/she is able to teach others his/her knowledge. The last
aspect of an experts characteristics is that an expert has varying levels of flexibility in
his/hers approach to new situations.
Although computer expertise and its effect on performance had been studied thor-
oughly [21], [22], [23], [24], [25], the underlying concept of expertise and its meas-
urement are not exactly defined yet. In most studies computer experience is determined
by subjective reports of length and frequency of computer use [33], [34], [35], [36],
[37]. This kind of assessment is based on the simplified assumption that expertise can
be understood as a function of the time spent operating a device.
Furthermore, this conceptualization of expertise leaves open what knowledge
structures are acquired while interacting with the technology and how these knowl-
edge components constitute the nature of expertise. However, it is highly questionable
that the time spent at a computer automatically leads to an acquisition of computer-
relevant knowledge. Therefore, instead of defining experience by the assessment of
quantitative aspects (duration and frequency) of computer usage, possibly qualitative
aspects like domain-specific knowledge concepts should be assessed [33], [38].
In addition, there are more questions in the context of operationalisation of com-
puter expertise as a main predictor of performance and acceptance of technology.
Studies showed that motivational and emotional factors of human computer interac-
tion are also involved in technical expertise [13], [16], [17]: People with a high self-
confidence when using technical devices show higher levels of technical interest in
general, and a lower computer anxiety. While both factors are not directly related to
computer expertise, there is though an indirect relation: users with high levels of
technical self-confidence and low computer anxiety levels show a considerably better
performance when interacting with technical devices and the better performance is
also correlated to a higher level of computer expertise.
Furthermore, across studies the measuring of “computer”- expertise referred to
very different types of technical devices, different using contexts of technology as
well as different generations of technology (covering a time frame from 1980 until
today). It is more than questionable if technical expertise with former devices may be
transferred to current devices. The expertise, which had been acquired in former
times, may have a considerable short half-life. In this context the factor users’ age
becomes an important part. The relationship between age and technology is always to
be dealt with the view on general changes in (technical-) history. It is not fully known
in how far technical expertise formed by a specific former technology may be trans-
ferred to more recent technical devices. Yet, a basic transferability had been identi-
fied: It was found that computer expertise was also impacting the performance when
handling mobile phones [36], [37], [38]. Beyond a basic positive effect on perform-
ance, computer expertise had also a positive effect on technology acceptance, espe-
cially on the perceived ease of using technical devices [10], [13].
However can we assume that the expertise effect, which refers to information and
communication technologies, is transferable to other domains (e.g. medical technol-
ogy), which might have completely different using characteristics?
Taken together: On a first sight, technical expertise is a concise construct, which is
assumed to positively influence the acceptance of modern technologies. However,
Technical Expertise and Its Influence 517
there is a lot of vagueness with this assumption. While no one would seriously dispute
a basic impact of technical expertise on technology acceptance, it is far from clear
what is the main driver of expertise: is it the increased technical literacy (declarative
knowledge) of persons, which advantages the interaction with technology? It is an
increased proficiency in how to handle technical devices (procedural knowledge,
[28])? Or is it the perceived trust into own abilities, or the self-reported competence or
even the interest in technical developments, which is mainly responsible for the
advantaging effect of expertise? Furthermore, is the expertise effect limited to one
technology context (e.g. ICT) or can we expect that expertise in one domain may be
transferred to other domains (e.g. medical technology)?
1.3 Questions Addressed in the Present Study and Working Model
The current paper aimed for an explorative study of the different facets of technical
expertise, their relation among each other and their impact on medical technology.
The research model is visualized in Figure 1.
Fig. 1. Research model
Relying on the literature and the different aspects of computer expertise, which
were found to be influential across studies, we measured motivational aspects (inter-
est in technology), emotional aspects (enthusiasm when using technology, trust in
technology) as well as cognitive aspects of technical expertise (self-reported technical
literacy and ability when handling technical devices). Additionally, we assessed par-
ticipants’ reported frequency of using technical devices.
In a second step, we looked at the impact of the different expertise facets on tech-
nology acceptance of mobile medical devices. It is quite reasonable to assume that the
518 M. Ziefle and A.K. Schaar
acceptance of medical technology distinctly differs from acceptance-patterns of ICT
for several reasons: First, medical devices are used not just for fun, but also because
of (critical) health states [9], [10], [11]. Also, beyond its importance for patients’
safety and the feeling of being safe, medical technology refers to “taboo-related”
areas, which are associated with disease and illness [9]. Medical monitoring is often
perceived as breaking into persons’ intimacy and privacy spheres and often leads to a
feeling of being permanently controlled [9], [10].
Recent studies [11], [13] showed that medical technology acceptance is a complex
product out of positive and negative attitudes, which persist at the same time. Hence,
both the positively connoted using motives as well as the negatively connoted using
barriers should be assessed to get a complete picture. The following questions were
guiding our research and data analysis:
- Which aspects form expertise in the domain of ICT to which extent?
- Are there inter-correlations between motivational, emotional and cognitive
aspects of ICT-expertise?
- Is ICT expertise (and if so, which facets) predictive to the acceptance of
medical technology?
- Which aspects of the acceptance of medial technology are formed by ICT
expertise: The pros (using motives) or the cons (usage barriers)?
2 Method
In order to examine a large number of participants and to consider the diversity within
the sample we have chosen the questionnaire method in combination with a scenario
technique. Our participants were instructed into a medical scenario:
“Imagine you came down on with diabetes mellitus and from
now on forced to check your blood sugar meter several times a
day. Imagine further that your medical device has the option to
select the collected data via WLAN. This technical solution
offers you the opportunity to collect and analyze your data
easily. In addition to that you could also use the option you
could send your body health parameters to your doctors,
without being forced to bring them personally”.
2.1 Variables
As independent variables different aspects of technical expertise were selected.
- Interest in technology and technology enthusiasm as motivational components
- Technical literacy and perceived ability when handling technical devices as cogni-
tive components and
- Distrust in technology as an emotional component.
Dependent variable was the acceptance for medical technology, operationalized by
five pro and con statements, each. The statements reflected arguments collected in
focus groups (prior to the questionnaire study), and covered different aspects of the
Technical Expertise and Its Influence 519
usage of medical technology, including safety, reliability, ease of use issues as well
social and financial aspects (items are described in detail in section 2.2.).
2.2 Questionnaire-Instrument
The questionnaire was arranged in four main sections: The first part included demo-
graphic data concerning gender, age, and educational level. The second section of the
questionnaire inquires information about the technical expertise of the participants
and attitude towards technology. First we asked for the frequency of using technology
in a private or in a job related context. On a five-point scale the participants had to
evaluate their frequency (1 = very little to 5 = very often). In this context we also
asked for information about the possession of different devices (ICT and medical
technology), the frequency of usage and the ease of use. Additionally participants
rated the general attitudes toward technology (Table 1).
Table 1. Components of technical expertise (1 = very low to 6 =very high)
Interest for Technology...
My technical interest is…
My enthusiasm for technology is…
My technical literacy is…
My ability in dealing with technology is…
My distrust against technology is…
The third section of the questionnaire was concerned with the technology accep-
tance for a medical device, including both, “pro” using motives (Table 2) as well as
“cons”, i.e. usage barriers (Table 3).
Table 2. Pro Items for medical device(1 = complete confirmation to 6 complete rejection)
The following arguments might supporting the usage of the device...
I feel save, when a medical devices controls my body functions constantly.
I am sure that the most innovative devices are solid.
I want an easy instruction manual.
I expect a positive feedback of my social surrounding.
I would use the device, if the sickness funds pay for them.
Table 3. Con items (1 = complete confirmation to 6 = complete rejection)
The following arguments might hinder the usage of the device...
I feel observed, when a medical devices permanently documents my body functions.
I fear that a new medical device is not matured.
I am afraid, that the new devices are not easy to use.
I fear that my surrounding doesn’t support my technical device
I fear that the devices would be expensive.
520 M. Ziefle and A.K. Schaar
2.3 Participants
The intention in the recruitment procedure was to survey users of a wide age range
and health status in order to explore and to compare their motives and barriers about
future healthcare solutions.
A total of 100 participants between 19 and 75 years volunteered to take part in the
study. The purpose in the recruitment procedure was to survey users of a wide age
range in order to compare their motives and barriers about future healthcare solutions.
For younger participants (N = 49), ranging between 19 and 30 years of age, 65.3%
were females, 34.7% male. Regarding the middle-aged group (31 to 55 years of age,
N = 35), 62.9% were female and, 37.1% male. Finally, for the older participants (N =
29), between 56 to 75 years of age, 57.5% was female and 42.5% was male. Partici-
pants were reached through the authors’ social network and corresponded to notices
posted on campus and in public places of the city. The educational level of our par-
ticipants was rather high. 59% (N = 59) declared to have an academic degree. 24% (N
= 24) have at least the A-school level. Regarding the health condition, 45% of our
sample reported to be healthy. 33% had one or two chronic illnesses and 22% were
affected by comorbidity1.
3 Results
Outcomes were analyzed by ANOVA procedures and Spearman rank correlations.
First, we analyzed the independent variables regarding their influence on technical
expertise. Then, we examine whether there is a direct influence of technical expertise
on the evaluation of the smart devices - blood sugar meter.
3.1 Aspects of Technical Expertise
Before we look at the inter-correlations of the different aspects of expertise, we de-
scriptively analyzed the different aspects of expertise, in order to learn the degree of
each of the aspects as well as the distribution within the sample. Outcomes are visual-
ized in Figure 2 and 3.
As can be seen from Figure 2, the motivational aspects of expertise, interest in and
enthusiasm for technology showed similar patterns. Only few persons reported to
have very low and low interest/enthusiasms for technical issues. The majority (about
70%) ranked themselves with 3, 4 or 5 points out of 6 points possible.
Regarding the distrust, we see a “perfect” distribution (normal distribution): more
than 40% reported to have a medium degree of distrust in technology in general.
Regarding the cognitive components of technical expertise (Figure 3) - the reported
literacy and the handling competence – we also found quite similar patterns. While
only few participants stated to have a “very low” declarative (technical literacy) and
procedural knowledge (handling competence), about 80 % of participants ranked their
literacy and handling competence with 3, 4 and 5 (“high”) points out of 6 (“very
high”) points possible.
1 Presence of one or more diseases in addition to a primary disease.
Technical Expertise and Its Influence 521
Fig. 2. Descriptive outcomes in different expertise facets: left: interest in technology; center:
enthusiasm for technology; right: distrust in technology (N = 100)
Fig. 3. Descriptive outcomes in different expertise facets: left: technical literacy; right: reported
competence when handling technical devices; (N = 100)
In a second step, we looked at the relations between and across the single compo-
nents. According to correlation outcomes, all expertise facets revealed high inter-
correlations. Figure 4 illustrates the statistic connections between the relevant items,
revealing that cognitive, emotional and motivation aspects do significantly contribute
to the concept of technical expertise.
In order to reduce the number of factors for further analysing of the relation between
expertise and acceptance of medical technology, we selected one motivational, two
cognitive aspects and one emotional aspect of expertise: Interest in technology, techni-
cal literacy, ability (handling competence) and the perceived distrust in technology.
As in many studies technical expertise had been determined by reports of length
and frequency of computer/device use, we now analyze whether the simplified as-
sumption that expertise can be understood as a function of the time spent operating
the device, can be validated. We asked for the possession and the usage frequency of
522 M. Ziefle and A.K. Schaar
Fig. 4. Inter-correlations between expertise aspects. Device usage refers to a mobile phone.
ICT (PC, mobile phone, fax, digital camera) and medical devices (blood pressure /
sugar meter, hearing aid, pulse watch), as well as the reported ease of use (Table 4).
Table 4. Correlations (Ease of use, frequency of device usage and technical expertise)
Technical Expertise and Its Influence 523
For the ownership, no significant relations to neither of the expertise aspects were
found. Also, the usage frequency of medical devices did not reveal statistically sig-
nificant relations to the extent of technical expertise.
Though, regarding some ICT devices (PC, mobile phone, and digital camera), sig-
nificant correlations between expertise and ease of use ratings and well as frequency
of usage were found. Thus, we can conclude that for common ICT devices, frequency
of usage and ease of use ratings show a basic interrelation to the technical expertise,
operationalized as interest in technology, literacy in technology, handling competence
when using technical devices, and distrust in technology
Another meaningful finding in this context is that expertise components are highly
correlated to gender, and less pronounced to age. Regarding age, we only found one
relation: with increasing age, the distrust in technology raises (r = .23; p < .05). In
contrast, gender showed to be highly connected to expertise components. Women
report to have a lower interest in technology (r = .43; p < .05; a lower self-reported
handling competence (r = .58; p < .05) and also a lower self-reported technical liter-
acy (r = .52; p < .05) in contrast to male respondents. Interestingly, the level of dis-
trust is not different between both gender groups.
In the following, we now focus on the influence the technical expertise aspects on
the acceptance of medical technology, taking a blood sugar meter as an example, thus
an external mobile device, which may have similar attributes than the mobile phone.
3.2 Technical Expertise and Its’ Influence on the Acceptance of Medical
Technology
In order to learn something about the influence of the technological expertise and its
influence on the acceptance of future medical technology we picked the following
items to prove whether they have an influence on the evaluation of the pro or con
arguments: reported interest (motivational component), reported literacy (cognitive
component), reported ability/handling competence (cognitive component), and dis-
trust (emotional component). Each of the expertise components was dichotomized by
the median in a “high” and a “low group” (e.g. high interest, low interest).
In a first step, we looked at the main effects (ANOVA procedures) of expertise as-
pects on the acceptance of medical technology, separated for the pros and the con items
Secondly we analyzed the influence of the expertise components on the single items of
the pro using arguments and the using barriers (using Spearman rank correlations).
Main effects of expertise facets on the medical device blood sugar meter
First of all, the pro-using arguments are focussed at. While the ANVOA revealed no
significant effect for the interest in technology, the technical literacy and the handling
competence, the distrust in technology (F (5,92) = 3; p < .05) revealed to be a main
driver for the pro-using motivation of medical technology. When looking at the usage
barriers, the interest in technology was significantly impacting the using barriers of
medical technology (F (5,88) = 2.4; p = .05), the technical handling competence (F
(5,98) = 4.9; p < .05) as well as the distrust (F (5,92) = 5.1; p < 0.05) significantly
influenced the negative connoted acceptance of medical technology. Comprising
statistical analyses, the more distrust in technology persons report, the lower is their
pro-using motivation and the higher their reluctance if not refusal to use medical
524 M. Ziefle and A.K. Schaar
technology if necessary. Beyond the distrust in technology, which was found to im-
pact both, the pros and the cons, the reported competence in handling technical de-
vices and the interest in technology was influencing exclusively the using barriers,
thus the reluctance of using medical technology. The lower the interest in technology
and the lower the self-reported handling competence with ICT technology, the higher
is the unwillingness to accept medical technology.
Influence of expertise facets on the pro and con items in detail
So far, we considered all pro-using items and all con-using items as a total score. As it
might be insightful to get a deeper insight into the relative extent of (dis-) agreement
for the specific pros and cons, we used correlation analyses to find out the relative
relation between expertise components and the single items with the expertise com-
ponents. As can be seen from Figure 5, for the pro items there are only two statisti-
cally significant relations between the expertise component distrust in technology on
the one hand and the pro-using argument “I feel save if a device checks my vital val-
ues constantly” and the pro-using argument “I would use the device if the sickness
fund pays for it” on the other hand. Apparently, for persons having high distrust lev-
els, there are two arguments which militate in favour of using the medical device: the
public sickness funds pay for it, which could be interpreted by high distrust persons
that the reliability in the devices is high and that they are constantly informed about
their own health status and vital parameters, respectively.
Fig. 5. Intercorrelations between pros (left) and cons (right) and expertise components
When looking at the usage barriers (right side in Figure 5), all facets of expertise
showed significant correlations. Thus it can be concluded that participants with high
Technical Expertise and Its Influence 525
levels of distrust, low levels of technical interest, technical literacy, and a low self-
reported handling competence when using ICT technologies tend to refuse the usage
of medical technology and to overestimate the negative aspects in contrast to the posi-
tive using argumentations.
4 Discussion and Suggestions for Future Research
The aim of the present study was to analyze the impact of expertise on the acceptance
of medical technology, taking a mobile blood sugar device as an example. The fol-
lowing questions were guiding our research: (1) Is ICT expertise (and if so, which
facets) predictive to the acceptance of medical technology? (2) Are there inter-
correlations between motivational, emotional and cognitive aspects of ICT-expertise?
(3) Which aspects of the acceptance of medial technology are formed by ICT exper-
tise: The pros (using motives) or the cons (usage barriers)?
Before findings of the research are discussed within implications for research, ap-
plication and future research demands, it should be noted that the topic “acceptability
of medical technology” is highly sensitive. People asked to participate in this research
showed a high interest for the topic and a high willingness to participate, independ-
ently of generation and gender. Apparently, a high public awareness for the societal
needs of medical technology is present as well as a high motivation to express own
opinions and fears connected to its usage. On the basis of the results we now can
answer the research questions:
(1) On the one hand we now can clearly say that technical expertise, operationalized
by four components - interest in technology, technical literacy, handling compe-
tence and distrust in technology in general- is in fact impacting the acceptance of
medical technology. The higher the technical expertise the higher is the accep-
tance of medical technology and the higher the intention to use the medical device.
Apparently, there is a basic transferability of the positive expertise effect from one
domain (information and communication technology to another quite different
domain: medical technology.
(2) When asking which of the facets of expertise, the cognitive, the emotional and the
motivational part, we now can say that the emotional part (distrust in technology)
is the strongest player for both, the pro-usage and the contra-usage motivation of
medical technology. Beyond the emotional component, also cognitive and motiva-
tional factors) play also a role, especially in the unwillingness to accept medical
technology. This findings lead to the final point, which is the most important one
in our perspective.
(3) Expertise, especially the missing or low technical expertise, is nearly exclusively
related to the high confirmation of using barriers and the not-acceptance of medi-
cal technology. Conversely, the pro-using arguments and the positive expectations
when using medical technology nearly play no role for persons with a low extent
of technical expertise. From this it can be derived that it is not specifically the ac-
ceptance of medical technology, which is problematic. Rather, the low technical
expertise with ICT technology and the low self-confidence of persons when
526 M. Ziefle and A.K. Schaar
interacting with common ICT devices acts as a general brake which is then trans-
ferred to the medical technology application context.
Even though this study revealed detailed insights into differential components and
effects of technical expertise on the acceptance of a medical mobile device, still there
are many duties for future research:
Other technology contexts: This study mainly concentrated on one medical tech-
nology, a mobile external device (blood sugar meter). This was selected because
many people - though not chronically ill - know diabetes as a quite popular disease as
well as the disease’s handling by means of a blood sugar device and therefore should
have sufficient shared knowledge and experience. However, future studies will have
to concentrate on other forms of mobile technology, which are not that prominent yet.
Currently, mature medical technical solutions cover smart artifacts, a promising con-
text for medical technology, as all-day objects equipped with special functions like
sensors, memory and communications skills and are assumed to be appropriate in
disease handling (e.g., Smart Sofa [39], Smart Pillow [40] and Smart Dishes [41]).
Another technological context regards ‘wearable computing’, that makes the com-
puter to an integral part of everyday clothing [42]. Future studies will have to study
technology acceptance for this new forms of medical technology.
User diversity: Another research duty regards user diversity, beyond technical ex-
pertise. In this study we did not differentiate in a detailed way whether other user
characteristics may affect acceptance outcomes. This, however, is of prominent inter-
est considering the complexity of personal and individual factors, which might impact
acceptance of technology. As opposed to the past, when mostly sophisticated and
technology prone professionals were the typical end-users of technical products, now
broader user groups have access to technology. Still, the development of technology
in general seems to be limited to dominantly young, technology experienced, West-
ern, middle- and upper class males [43], [44]. Although the vital importance of ensur-
ing that a technology produced is both usable and acceptable for a diverse user group
is known in the meanwhile, the recognition of the importance of users’ diversity is
only slowly influencing mainstream usability studies and not yet considered within
the development of technical products. Design approaches thus have to undergo a
radical change taking current societal trends into account, which have considerable
impact for the inclusion of a diverse user group.
Thus, it is a central claim that mobile displays are designed to be in line with older
users’ specificity and diversity [45]. Design approaches should therefore take the
user-perspective seriously. The duty of further research efforts is to fill the knowledge
gap, and to systematically integrate user diversity—age, gender, social and cultural
factors—into usability approaches.
Self-reported technical expertise vs. factual expertise: It should be kept in mind
that the expertise-aspects, which were assessed in this research, are exclusively sub-
jective and represented users’ judgments about own abilities and attitudes. Users
taking part in this study evaluated their own technical interest, literacy, their compe-
tencies when handling technical devices as well as their distrust in technology. How-
ever, so far, we do not have an external validation of the respective high or lower
expertise levels. In addition, the real expertise level of participants should be deter-
mined by a psychometric testing procedure [33].
Technical Expertise and Its Influence 527
Acknowledgements
Authors would like to thank all respondents having taken the time to patiently fill in
the questionnaire, to vividly discuss the sensitive topic with us and openly share their
perspectives. In addition we thank Christina Vedar for her research support.
This research had been funded by the Excellence Initiative of the German federal
and state governments.
References
1. Leonhardt, S.: Personal Healthcare Devices. In: Mukherjee, S., et al. (eds.) Malware:
Hardware Technology Drivers of Ambient Intelligence, pp. 349–370. Springer, Dordrecht
(2006)
2. Ziefle, M., Röcker, C.: Acceptance of Pervasive Healthcare Systems: A comparison of dif-
ferent implementation concepts. In: Workshop User-Centred-Design of Pervasive Health
Applications (UCD-PH 2010). 4th ICST Conference on Pervasive Computing Technolo-
gies for Healthcare 2010 (2010)
3. Wittenberg, R., Malley, J., Comas-Herrera, A., Fernandez, J.L., King, D., Snell, T.,
Pickard, L.: Future Demand for Social Care, 2005 to 2041: Projections of Demand for So-
cial Care and Disability Benefits for Younger Adults in England, Report to the Strategy
Unit (Cabinet Office) and the Department of Health, PSSRU Discussion paper 2512, Per-
sonal Social Services Research Unit, London (2008)
4. Wittenberg, R., Comas-Herrera, A., Pickard, L., Hancock, R.: Future Demand for Long-
Term Care in England. PSSRU Research Summary (2006)
5. Weiner, M., Callahan, C.M., Tierney, W.M., Overhage, M., Mamlin, B., Dexter, A.: Using
Information Technology To Improve the Health Care of Older Adults. Ann. Intern.
Med. 139, 430–436 (2003)
6. Schmitt, J.M.: Innovative medical technologies help ensure improved patient care and
cost-effectiveness. International Journal of Medical Marketing 2(2), 174–178 (2002)
7. Bohn, J., Coroama, V., Langheinrich, M., Mattern, F., Rohs, M.: Social, Economic, and
Ethical Implications of Ambient Intelligence and Ubiquitous Computing. In: Weber, W.,
Rabaey, J., Aarts, E. (eds.) Ambient Intelligence, pp. 5–29. Springer, Heidelberg (2005)
8. Ziefle, M., Röcker, C., Wilkowska, W., Kasugai, K., Klack, L., Möllering, C., Beul, S.: A
Multi-Disciplinary Approach to Ambient Assisted Living. In: Röcker, C., Ziefle, M. (eds.)
E-Health, Assistive Technologies and Applications for Assisted Living: Challenges and
Solutions. IGI Global, Hershey (2010)
9. Ziefle, M., Schaar, A.K.: Gender differences in attitudes towards invasive medical tech-
nology. Electronic Journal of Health Informatics (2010, in press)
10. Ziefle, M., Wilkowska, W.: Technology acceptability for medical assistance. Full Paper at
the 4th ICST Conference on Pervasive Computing Technologies for Healthcare 2010
(2010)
11. Gaul, S., Ziefle, M.: Smart Home Technologies: Insights into Generation-Specific Accep-
tance Motives. In: Holzinger, A., Miesenberger, K. (eds.) USAB 2009. LNCS, vol. 5889,
pp. 312–332. Springer, Heidelberg (2009)
12. Arning, K., Ziefle, M.: Comparing apples and oranges? Exploring users’ acceptance of
ICT- and eHealth-applications. In: Proceedings of the International Conference on Health-
care Systems, Ergonomics and Patient Safety, Straßbourg, France [CD-ROM] (2008)
528 M. Ziefle and A.K. Schaar
13. Wilkowska, W., Ziefle, M.: Which factors form older adults’ acceptance of mobile infor-
mation and communication technologies? In: Holzinger, A., Miesenberger, K. (eds.)
USAB 2009. LNCS, vol. 5889, pp. 81–101. Springer, Heidelberg (2009)
14. Davis, F.D.: Perceived usefulness, perceived ease of use, and user acceptance of infor-
marion technology. MIS Quaterly 13, 319–334 (1998)
15. Brosnan, M.J.: The impact of computer anxiety and self-efficacy upon performance. Jour-
nal of Computer Assisted Learning 14, 223–234 (1998)
16. Busch, T.: Gender differences in self-efficacy and attitudes toward computers. Journal of
Educational Computing Research 12, 147–158 (1995)
17. Ziefle, M., Bay, S.: Transgenerational Designs in Mobile Technology. In: Lumsden, J.
(ed.) Handbook of Research on User Interface Design and Evaluation for Mobile Technol-
ogy, pp. 122–140. IGI Global, Hershey (2008)
18. Arning, K., Ziefle, M.: Different Perspectives on Technology Acceptance: The Role of
Technology Type and Age. In: Holzinger, A., Miesenberger, K. (eds.) USAB 2009. LNCS,
vol. 5889, pp. 20–41. Springer, Heidelberg (2009)
19. Vicente, K.J., Hayes, B.C., Williges, R.C.: Assaying and isolating individual difference in
searching a hierarchical file system. Human Factors 29, 349–359 (1987)
20. Westerman, S.J.: Individual differences in the use of command line and menu computer in-
terfaces. International Journal for Human Computer Interaction 9, 183–198 (1995)
21. Chi, R., Glaser, R., Farr, M.J.: The Nature of Expertise. Lawrence Erlbaum, Hillsdale
(1988)
22. Downing, R.W., Moore, J.L., Brown, S.W.: The effects and interaction of spatial visualiza-
tion and domain expertise on information seeking. Computers in Human Behaviour 21,
195–209 (2005)
23. Mayer, R.E.: From novice to expert. In: Helander, M.G., Landauer, T.K., Prabhu, P. (eds.)
Handbook of Human Computer Interaction, pp. 781–795. Elsevier, Amsterdam (1997)
24. Ye, N., Salvendy, G.: Quantitative and qualitative differences between experts and ices in
chunking computer software knowledge. International Journal of Human Computer Inter-
action 6, 105–118 (1994)
25. Chi, M.T.H., Feltovich, P.J., Glaser, R.: Categorization and representation of physics prob-
lems by experts and novices. Cognitive Science 5, 121–152 (1981)
26. Arning, K., Ziefle, M.: Understanding differences in PDA acceptance and performance.
Computers in Human Behaviour 23(6), 2904–2927 (2007)
27. Arning, K., Ziefle, M.: Ask and you will receive: Training older adults to use a PDA in an
active learning environment. International Journal of Mobile Human-Computer Interac-
tion 2(1), 21–47 (2010)
28. Harvey, L., Anderson, J.: Transfer of declarative knowledge in complex information proc-
essing domains. Human Computer Interaction 11(1), 69–96 (1996)
29. Kent, P., Vaubel, R., Gettys, C.F.: Inferring User Expertise for Adaptive Interfaces. Jour-
nal Human-Computer Interaction 5(1), 95–117 (1990)
30. Hanisch, K., Kramer, A., Hulin, C.: Cognitive representations, control, and understanding
of complex systems: a field study focusing on components of users’ mental models and
expert/novice differences. Ergonomics 34(8), 1129–1145 (1991)
31. Bransford, J.D., Brow, A.L., Cocking, R.R.: How People Learn: Brain, Mind Experience
and School. National Academy Press, Washington (1999)
32. Dunphy, B.C., Williamson, S.L.: Pursuit of expertise. Towards an Educational Model for
Expertise Development. Advances in Health Sciences Education 9, 107–127 (2004)
Technical Expertise and Its Influence 529
33. Arning, K., Ziefle, M.: Assessing computer experience in older adults: Development and
validation of a computer expertise questionnaire for older adults. Behaviour and Informa-
tion Technology 27(1), 89–93 (2008)
34. Potosky, D., Bobko, P.: The Computer Understanding and Experience Scale: a self-report
measure of computer experience. Computers in Human Behavior 14(2), 337–348 (1998)
35. Vu, K.-P., Hanley, G., Strybel, T., Proctor, R.W.: Metacognitive Processes in Human-
Computer Interaction: Self-Assessments of Knowledge as Predictors of Computer Exper-
tise. International Journal of Human-Computer Interaction 12(1), 43–71 (2000)
36. Arning, K., Ziefle, M.: Effects of cognitive and personal factors on PDA menu navigation
performance. Behaviour and Information Technology 28(3), 251–268 (2009)
37. Ziefle, M.: The influence of user expertise and phone complexity on performance, ease of
use and learnability of different mobile phones. Behaviour and Information Technol-
ogy 21(5), 303–311 (2002)
38. Ziefle, M., Schroeder, U., Strenk, J., Michel, T.: How young and older users master the use
of hyperlinks in small screen devices. In: Proceedings of the SIGCHI Conference on Hu-
man Factors in Computing Systems 2007, pp. 307–316. ACM, New York (2007)
39. Legon, J.: ’Smart Sofa’ Aimed at Couch Potatoes (2003),
http://www.cnn.com/2003/TECH/ptech/09/22/
smart.Sofa/index.html2003
40. Ferguson, G.T.: Have your objects Call My Objects. Havard Business Review 80(6), 138–
143 (2003)
41. Cook, D.J., Das, S.K.: How Smart are our Environments? An UpdateLook at the State of
the Art. Journal of Pervasive and Mobile Computing 3(2), 53–73 (2007)
42. Horter, H., Linti, C., Loy, S., Planck, H., Kotterba, B., Günther, U.: Health Wear – Senso-
rische Textilien zur Erfassung von Vitalparametern. In: Proceedings of the Second German
Congress on Ambient Assisted Living (AAL 2009), January 27-28. VDE, Berlin (2009)
(CD ROM)
43. Asada, H.H., Shaltis, P., Reisner, A., Rhee, S., Hutchinson, R.C.: Mobile Monitoring with
Wearable Photoplethysmographic Sensors. IEEE Engineering in Medical and Biology
Magazine 22(3), 28–40 (2003)
44. Choong, Y.Y., Salvendy, G.: Implications for Design of Computer Interfaces for Chinese
Users in Mainland China. International Journal of Human-Computer Interaction 11(1), 29–
46 (1999)
45. Ziefle, M., Jakobs, E.-M.: New challenges in Human Computer Interaction: Strategic Di-
rections and Interdisciplinary Trends. In: 4th International Conference on Competitive
Manufacturing Technologies, South Africa, University of Stellenbosch, pp. 389–398
(2010)
