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Predicting Technology Acceptance and Adoption by the
Elderly: A Qualitative study
Karen Renaud
Department of Computing Science, University of
Glasgow, Glasgow, Scotland, karen@dcs.gla.ac.uk
Judy van Biljon
School of Computing, University of South Africa
South Africa, vbiljja@unisa.ac.za
ABSTRACT
Technology adoption has been studied from a variety of
perspectives. Information systems, Sociology and Human-
Computer Interaction researchers have come up with various
models incorporating factors and phases to predict adoption that,
in turn, will lead to persistent use. Technology acceptance by the
elderly mobile phone user has received less attention and no
model currently exists to predict factors influencing their
technology adoption. A literature study yielded a set of
acceptance factors (derived mostly from quantitative studies) and
adoption phases (derived mostly from qualitative studies) that
could influence and predict mobile phone adoption by the elderly
user. We confirmed a subset of these factors by consulting
findings from research into the context of senior mobile phone
users, including the needs and limitations of these users. We then
verified the factors qualitatively by means of structured
interviews with senior mobile phone users. The interviews
included the use of scenarios as well as a mobile phone design
activity. Triangulating the quantitative findings from literature
with the qualitative findings from this study led to a set of
interlinked acceptance factors and adoption phases that we present
as the Senior Technology Acceptance& Adoption model for
Mobile technology (STAM). This paper makes a contribution to
understanding technology acceptance by senior users and should
be of interest to researchers, designers and decision-makers on
technology adoption, especially mobile features and services.
Categories and Subject Descriptors
H.1.2 [User/Machine Systems]: Human factors, Human
information processing, Software psychology; J.4 [Social and
Behavioural Sciences] Economics, Psychology, Sociology.
General Terms
Human Factors, Design, Experimentation.
Keywords
Mobile phone adoption, technology adoption models, elderly
1. INTRODUCTION
People are living longer than ever before in the 21st century,
which means that the ’gray’ market is growing. Due consideration
of their particular needs is essential in the design and marketing of
products [5]. Abascal and Civit [1] propose the possibility of
government subsidised prices for elderly people and the potential
for introducing these products into the mainstream market as
reasons why industry is starting to realise the potential of the gray
market. Despite the moral and commercial incentives and the fact
that they are the only growing group in most developed societies,
elderly mobile phone users are an oft neglected group in product
development and marketing [22].
Technology acceptance, in general, has been widely studied and
several models of technology acceptance have been proposed and
tested [9, 28, 34]. However, the life cycle of mobile phone
technology—from designing and developing the innovation,
communicating or diffusing information about it, deciding to
adopt (selecting, purchasing or committing to use it) and then
achieving persistent use—is poorly understood for elderly users
[8]. One reason could be that few studies differentiate between
pre- and post-adoption.
We follow a three-pronged approach firstly by consulting the
literature on those factors that influence technology acceptance
and summarising these in tabular format. Secondly we considered
the context of the elderly mobile phone user and identified factors
that might influence their acceptance of mobile phones. Thirdly
we conducted interviews with a number of elderly mobile phone
users to confirm the identified factors and uncover new factors
that influenced their mobile phone acceptance and adoption.
Finally we triangulated between the findings obtained from each
approach and we conclude by proposing a model that
encapsulates our findings. The remainder of section 1 expands on
the purpose and motivation of this paper in section 1.1 and the
organisation in section 1.2.
1.1 Purpose and Motivation
It is important to make a distinction between adoption and
acceptance of technology. Technology adoption is a process –
starting with the user becoming aware of the technology, and
ending with the user embracing the technology and making full
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use of it. Someone who has embraced a technology is likely to
replace the item if it breaks, find innovative uses for it, and cannot
contemplate life without it. Many teenaged mobile phone users
have embraced the technology without reservation. Acceptance,
as opposed to adoption, is an attitude towards a technology, and it
is influenced by various factors. A user who has purchased a new
technology item has not yet adopted it – there are other stages
beyond simple purchasing and this is where acceptance plays an
important role. If the user buys an item and then does not accept
it, it is unlikely that full adoption will occur.
The field of Information Systems (IS), proposes a number of
technology acceptance models which focus, at a micro-level, on
factors influencing acceptance (without considering the process
towards full adoption) [33]. Sociological studies prefer a macro-
level approach, contemplating the purchasing decision as part of a
process - incorporating the user’s acceptance or rejection and use
of technology i.e. the adoption process [12]. In this paper we will
be focusing on factors influencing progression through the
different adoption phases. Technology adoption and acceptance
models contribute towards anticipating future needs in a complex
and ever-evolving market scenario. However, existing research on
mobile phone adoption focuses mostly on one specific aspect of
technology adoption e.g. m-commerce [36] which is rather limited
in application and therefore there is a need for research which
integrates the different factors into a single model. The factors
incorporated into current acceptance models have been
quantitatively verified, by questioning students or economically
active adults [20]. The context of the elderly user is very different
from that of younger people, and it is unlikely that the factors
incorporated into the existing fully represent the factors that
influence the elderly mobile phone user. A literature study of the
needs, limitations and expectations of the elderly adult mobile
phone user made it clear that they demand added value in the
form of a more social, active, meaningful and independent life
[22].
Traditionally technology adoption models in MIS were developed
from a positivistic epistemology while the technology adoption
models in sociology have been developed from an interpretivistic
epistemology. This paper integrates findings from the quantitative
as well as the qualitative approaches with the findings from our
survey to propose a technology acceptance model that represents
the acceptance space of the elderly mobile phone user. The
findings of these studies can provide valuable insights into the
lives of the elderly user, as well as those aspects of their lives that
can have an effect on their acceptance and usage of mobile
phones. The scope of this paper is limited to addressing mobile
phone acceptance by the elderly within the wider arena of
technology adoption. The participants in the survey were all
South African residents between the ages of 60 and 92. The
research reported in this paper focuses on two sub-questions:
• What are the factors that influence the acceptance of mobile
technology by the elderly?
• How can these factors be incorporated into a technology
acceptance and adoption model?
1.2 Organisation of this Paper
Section 2 introduces two adoption models and a number of
technology acceptance models. Section 3 discusses the context of
the mobile phone user including their limitations and challenges.
Section 4 describes the interviews. Section 5 presents our
findings. Section 6 proposes the STAM model and reflects on the
contribution of this paper. Section 7 concludes.
2. TECHNOLOGY ADOPTION
This section reviews technology adoption and acceptance models
and extracts a set of factors relevant to mobile phone acceptance
by senior users.
2.1 Technology Diffusion - Processes
Two very different models, which depict the technology adoption
process are presented, one by Rogers [28] and the other by
Silverstone and Haddon [31]. Rogers [28] proposed a five stage
process of product adoption:
• the knowledge phase where the person gets to know about the
product;
• the persuasion phase where he or she becomes persuaded of a
need for the product;
• the decision phase which leads to a purchase;
• the implementation phase where the item is used; and,
• the confirmation phase where the individual seeks to confirm
that he or she made the right decision in purchasing the
product.
Silverstone and Haddon [31] proposed the domestication of
technology as a concept used to describe and analyse the
processes of acceptance, rejection and use as described in Table 1.
Users are seen as social entities and the model aims to provide a
framework for understanding how technology innovations change,
and are changed, by their social contexts. The domestication
theory adoption process is more suitable for our purpose in
charting acceptance since Rogers’ model focuses mostly on the
decision to buy or not to buy, which, as we shall see, is less
applicable in terms of acceptance by elderly users.
Table 1: Domestication adoption process dimensions [20] :66
Dimension
Description Examples of potential themes
relevant in user experience
research
Appropriation Process of
possession or
ownership of
the artifact.
Motivation to buy a product.
Route to acquire information
about a product. Experience
when purchasing a product.
Objectificatio
n Process of
determining
roles product
will play.
Meaning of a technology.
What function will be used in
users’ life? Where is it placed?
How is it carried?
Incorporation Process of
interacting
with a product.
Difficulties in using a product
(usability problems). Learning
process (use of instructional
manual)
Conversion Process of
converting
technology to
intended
feature use or
interaction.
Unintended use of product
features. Unintended way of
user interaction. Wish lists for
future products.
2.2 Technology Acceptance Models
The Technology Acceptance Model (TAM) proposes a number of
factors that are essential in determining user attitude towards
accepting a new technology, as shown in Figure 1 [9, 21], TAM
incorporates six distinct factors [9, 24]:
•External variables (EV), such as demographic variables,
influence perceived usefulness (PU) and perceived ease of use
(PEU).
•Perceived usefulness (PU) is defined as ‘the extent to which a
person believes that using the system will enhance his or her job
performance’ [34].
•Perceived ease of use (PEU) is ‘the extent to which a person
believes that using the system will be free of effort’ [34] .
•Attitudes towards use (A) is defined as ‘the user’s desirability of
his or her using the system’ [21]. Perceived usefulness (PU) and
perceived ease of use (PEU) are the sole determinants of attitude
towards the technology system.
•Behavioural intention (BI) is predicted by attitude towards use
(A) combined with perceived usefulness (PU).
•Actual use (AU) is predicted by behavioural intention (BI).
The attitude towards accepting a technology is believed to be the
result of personal and social influences. The fact that TAM does
not account for social influence is a limitation [10, 21].
Furthermore, TAM is somewhat limited since the only
determining factor leading to actual system use is depicted as
behavioural intention to use, which is unrealistic in our context, as
we will show later.
Venkatesh et al. [34] extended TAM and developed the Unified
Theory of Acceptance and Use of Technology (UTAUT), which
attempts to explain user intentions to use an information system
and subsequent usage behaviour. An important contribution of
UTAUT is to distinguish between factors determining use
behaviour namely the constructs of performance expectancy,
effort expectancy, social influence and facilitating conditions and
then factors mediating the impact of these constructs. The
mediating factors noted are gender, age, experience, and
voluntariness (i.e. the degree to which use of the innovation is
perceived as being of free will). It seems rather restrictive to limit
the mediating factors to this group when other factors might well
be as influential. Both TAM and UTAUT can be applied to any
technology type but there is some value in specialising the models
for particular technologies. It is worth noting that whereas TAM
includes a module depicting attitude, UTAUT omits this,
preferring to expand TAM’s external variable module into a
number of relevant factors. The following section discusses the
application of models which are specific to the mobile technology
area.
2.3 Mobile Technology Acceptance Models
Kwon and Chidambaram [19] propose a model for mobile phone
acceptance and use which includes the following components:
demographic factors, socio-economic factors, ease of use,
apprehensiveness, extrinsic motivation (perceived usefulness),
intrinsic motivation (enjoyment, fun) social pressure and extent of
use. They found that perceived ease of use significantly affected
users' extrinsic and intrinsic motivation, while apprehensiveness
about cellular technology had a negative effect on intrinsic
motivation [19]. The limitation of this model is that it does not
include infrastructural factors, which are essential in mobile
technology [22].
The Mobile Phone Technology Acceptance Model (MOPTAM),
depicted in Figure 2 [32], draws on UTAUT to include the
determining and mediating factors and then adapts the result to
model the personal mobile phone use of university students. TAM
and UTAUT were developed in organisations where the
infrastructure was standard and respondents were not affected by
the cost. UTAUT includes facilitating conditions (Infrastructure)
as a determining factor but restricts the influence to Actual Use
whereas MOPTAM predicts the influence of FC on PEU, PU, and
BI as well. It is interesting to note that perceived ease of use and
actual use are common to all.
Figure 2: Diagrammatic representation of MOPTAM [32]
Based on exploratory research, Sarker and Wells [30] propose a
framework that relates exploration and experimentation to the
assessment of experience that determines acceptance outcome.
The mediating factors are: context, technology characteristics,
modality of mobility, communication/task characteristics and
individual characteristics. Phang et al. [25] proposes a model for
senior citizen acceptance of eGovernment services based on their
findings that:
• Intention to use is influenced by Perceived Ease of Use,
Perceived Usefulness, Internet safety perception, Gender
Education, Age and Internet experience.
• Perceived usefulness is influenced by preference for human
contact, self-actualisation and resource savings.
• Perceived ease of use is influenced by computer anxiety,
computer support and declining physiological condition.
Figure 1 : Technology Acceptance Model (TAM) [21]
Perceived Ease of
Use (PEU)
Perceived
Usefulness (PU)
Social Influence
SI)
Facilitating
Conditions (FC)
DETERMINING FACTORS
Socio-economic
Factors (SF)
MEDIATING FACTORS
Demographic
Factors (DF) Personal
Factors (PF)
Actual System Use
(U)
Behavioural
Intention (BI)
Perceived Ease of
Use (PEU)
Perceived Ease of
Use (PEU)
Perceived
Usefulness (PU)
Perceived
Usefulness (PU)
Social Influence
SI)
Social Influence
SI)
Facilitating
Conditions (FC)
Facilitating
Conditions (FC)
DETERMINING FACTORS
Socio-economic
Factors (SF)
MEDIATING FACTORS
Demographic
Factors (DF) Personal
Factors (PF)
Actual System Use
(U)
Actual System Use
(U)
Behavioural
Intention (BI)
Perceived Ease
of Use (PEU)
Actual System
Use
Attitude toward
Use (A)
Behavioural
Intention to Use
(BI)
Perceived
Usefulness
(PU)
External
Variables (EV)
Perceived Ease
of Use (PEU)
Perceived Ease
of Use (PEU)
Actual System
Use
Actual System
Use
Attitude toward
Use (A)
Attitude toward
Use (A)
Behavioural
Intention to Use
(BI)
Behavioural
Intention to Use
(BI)
Perceived
Usefulness
(PU)
Perceived
Usefulness
(PU)
External
Variables (EV)
External
Variables (EV)
Arning and Ziefle, [2] found evidence to support the moderating
effect of individual variables such as age, gender, subjective
technical confidence and computer expertise on the relationship
between attitude towards a technology and performance. Taking
all these studies into account, Table 2 summarises the most
fundamental factors incorporated into the models listing the
following : Social influence (SI), Perceived Ease of Use(PEU),
Perceived Usefulness (PU), Facilitating Conditions (FC),
Behavioural Intention (BI), Demographic Factors (DF), Socio-
Economic Factors(SE), Personal Factors(PF) and Exploration and
Experimentation (EE).
Note that perceived ease of use is the common factor across all
the models, while some variables are subsumed under other
factors. For example, age and gender are subsumed under
demographic factors while computer support is subsumed under
facilitating conditions.
Table 2. Factors influencing mobile phone acceptance.
Models and theories
Factor TAM UT-
AUT Kwon &
Chidam-
baram
Sarker
& Wells MOP
-
TAM
SI No Yes Yes Yes Yes
PEU Yes Yes Yes Yes Yes
PU Yes No No Yes Yes
FC No Yes No Yes Yes
BI Yes Yes Yes No Yes
DF External
variables No Yes Yes Yes
SE External
variables No Yes Yes Yes
PF No No No Yes Yes
EE No No No Yes No
Accep-
tance No No No Yes No
It is interesting to note that only Sarker and Wells include an
acceptance module – the others all appear to assume that actual
use implies acceptance, an assumption we will challenge in this
paper.
3. Context of the senior adult
The mobile phone needs of the elderly centre around critical
services such as emergency and health support that enhance safety
and those services that make everyday life and tasks easier [20,
22]. The elderly position mobile phone use in terms of value,
which is mostly based on communication and safety aspects [20].
In the mobile context, the user and the equipment can be mobile
and the surroundings may therefore change constantly. This opens
up fundamental differences in the context of use between the
traditional computing environments and information appliances
such as mobile phones [14]. Four different aspects of the mobile
phone context have been noted [17, 18] in past research: physical
context as discussed in section 3.1, social context as discussed in
section 3.2, mental context as discussed in section 3.3 and the
technological context as discussed in section 3.4.
3.1 Physical Context
The physical context denotes the physical constraints of the usage
environment [17, 18]. We need to consider both the physical
limitations of the device as well as the limitations of the
surrounding physical context. Screen size, memory, storage space,
input and output facilities are more limited in mobile devices such
as mobile phones [6, 35], while sound output quality is often poor
with restricted voice recognition on input [11]. The undeniable
potential of the ‘gray’ market is hampered by the physical and
cognitive limitations of aging. Elderly mobile phone users make
use of fewer mobile phone features than younger users [20].
Ziefle and Bay [37] suggest that elderly mobile phone users do
not have a mental model of the ubiquitous hierarchical menu
system used by mobile phones. They struggle to find the features
they want to use and therefore do not use them. This is confirmed
by a study carried out by Osman et al. [23] who interviewed 17
elderly users and asked them to name the most important features
of a mobile phone. ’Easy menus’ was mentioned most often,
followed by large screen. The latter is unsurprising since many
elderly users have impaired vision. Another factor mentioned is
that they require large buttons, due to the inevitable decrease in
manual dexterity experienced by many elderly users [20]. It
follows that the effects of aging, such as impaired hearing, vision
and loss of manual dexterity impact negatively on the ease of use
of mobile phones. However, it would be a mistake to classify
users strictly according to age. Mallenius et al. [22] argue for
using functional capacity (consisting of the physical,
psychological and social aspects), rather than age as a facilitating
condition.
3.2 Social Context
Social context concerns the social interaction involved and
enabled by using the mobile device [17, 18]. Phillips and
Sternthal [26] found that with increasing age comes reduced
involvement with other people, as confirmed by Abascal and Civit
[1]. The reasons are argued by experts, but the net effect is
unarguable: reduced access to information that is readily available
to younger people. Elderly people make extensive use of the
television to give them the information they no longer get from
other people [26]. The social contact they do have is primarily
with their extended family and this group appears to provide them
with the advice and support they need. Friends and relatives,
especially the opinion of children and grand-children impact the
behaviour of the elderly mobile phone user [20, 22], therefore
social influence as proposed in MOPTAM is an important factor
in mobile phone acceptance.
3.3 Mental Context
The mental context relates to aspects of the user’s understanding
of the mobile handset usage model [18]. Mobile phones are
acquired by a widespread population of users who will probably
not have any formal training in operating them and consider them
as devices to be used rather than computers to be maintained [11].
Furthermore, device vendors consolidate multiple functions into a
single device. The mobile user has to handle interleaving of
multiple activities and multiple public faces, previously unknown
when only a landline or a stationary computer was used [27].
Cognitive demands are exacerbated due to physical constraints on
size, bandwidth and processing power, which restricts the
communication bandwidth and places extra demands on the user’s
attention [13]. The mental strain described above is amplified for
the elderly mobile phone user. People perform more slowly and
with less precision as they age, elderly users appear to have
difficulty learning how to use a new mobile phone [4] and use
fewer of the available features [33]. The ability to learn is not
impaired but the rate of learning is reduced [3, 29]. Burke and
Mackay [7] mention that the formation of new memory
connections is impaired with age. Therefore it is beneficial to
allow elderly people to regulate their own rate of information
processing. They struggle to filter out irrelevant stimuli so it takes
them longer to process the relevant information in order to learn
to use the device [26]. This is because they have reduced visual
processing speed [15] and working through mobile phone menus
is likely to be more difficult for them, purely because of this.
3.4 Technological Context
The technological context refers to the mobile infrastructure
including the networks available, services provided and features
of the mobile device [17]. The mobile context poses unique
challenges and opportunities in terms of mobility, portability and
personalisation [36], and yet there is an overlap between the
factors influencing mobile phone adoption and technology
adoption in general [19]. Therefore we will now consider factors
from models for technology adoption as the basis for proposing a
model for mobile phone adoption.
4. INTERVIEWS
Our investigation set out to confirm the factors that influenced
mobile technology acceptance by the elderly user and to
determine whether other new factors were involved. The best
way to understand influencing factors is to allow participants to
talk, so we decided to make use of a semi-structured interview
which would allow participants to contribute to the discussion as
they wanted to. The data was captured by the researchers during
the interviews. The questionnaire provided under Appendix 1
consists of four sections: Section A captures demographic data for
the user profile as discussed in section 4.1. Section B describes
five scenarios seniors typically encountered in their everyday life,
the scenarios were presented to the participants as discussed in
section 4.2. Section C focuses on technology acceptance factors
as described in section 4.3.
4.1 Participants’ profile
Thirty four elderly people participated in our study (10 male and
24 female). The participants per age distribution were: 60-70
years: 13, 70-80 years: 16 and 80-92 years: 5; hence the majority
were in the 60-80 age group. Considering mobile phone use, 19 of
the participants had contracts and 15 used pre-pay. They obtained
the phones by buying them (16), having the phone bought by
someone else (3) or getting the phone as a gift (15). The majority
who bought the phones themselves were in the 60-70 age group
i.e. the younger participants.
4.2 Scenarios
‘A scenario is a description of the world, in a context and for a
purpose, focusing on task interaction. It is intended as a means of
communication among stakeholders, and to constrain
requirements engineering from one or more viewpoints (usually
not complete, consistent, and not formal)’ [16]:3. Carroll explains
that scenarios are valuable because they are both concrete and
flexible [16]. In considering the categorization of scenarios, our
scenarios could be categorized as activity scenarios because they
describe and suggest the use of the mobile phone artifact.
In this study we used activity scenarios to tell a carefully tailored
story that reveals life aspects that influence mobile phone usage
by the elderly. The researcher detailed the scenarios and allowed
the participant to comment. The participants responded actively,
eager to discuss their difficulties and experiences of mobile
technology. Participants often made a distinction between what
they do and what other people do. This supports our decision to
present scenarios for discussion rather than rigid questionnaires
where the social desirability bias may deliver less candid
responses. Scenarios are a traditional and useful design tool and,
in this case, the informal approach was best suited to the
exploratory research activity. By presenting the scenarios before
the design activity, we hoped to suggest additional uses of the
phone that they had not anticipated before.
4.3 Factors from Acceptance models
In line with TAM, we are particularly interested in ease of use,
perceived usefulness, and attitude towards use. Furthermore, we
want to include other applicable external factors that influence
acceptance. We assessed the different TAM factors as follows:
4.3.1 Ease of Use and Actual Use
It is clear that ease of use cannot really be self-reported and actual
use is as hard to determine since users sometimes do not
remember particular features until they need to use them again. It
is far more enlightening to observe users making use of a product.
We therefore asked participants:
• To name the three features they used most often, and
• Then ask them to show us how their phone performed the
features.
4.3.2 Perceived Usefulness
We asked participants to design their own phone on the
assumption that they would include features based on perceived
usefulness. We provided a paper facsimile of a mobile phone,
together with a number of buttons (with function names) that they
could affix to the phone in an empty space provided. A number of
blank buttons were provided should additional features be
required. They were asked to choose the six most important
buttons to place on ‘their’ ideal phone.
4.3.3 Intention to use
We only interviewed users who owned mobile phones, and this
suggests a pre-existing implicit intention to use.
4.3.4 External Factors and Facilitating Conditions
To open the way for discussions that would help us to identify
particular external factors, we sketched scenarios involving
familiar problems experienced by an elderly person, as evidenced
by the literature review, and asked for the participant’s opinion or
advice about the situation.
4.3.5 Acceptance
One cannot directly ask a user whether he or she has accepted a
technology. We also know acceptance cannot be accurately
inferred based merely on usage. However, people’s attitudes tend
to influence what they say about the technology. We therefore
recorded our participants’ comments about their phones, and used
this to gauge the extent to which they had embraced the
technology.
5. RESULTS AND FINDINGS
We sketched a number of scenarios and asked our participants to
comment on them. This was to explore their concept of the
perceived usefulness of the mobile phone as a technology. We
were also hoping that some influential factors would emerge from
the discussion.
5.1 Findings from scenarios
Scenario 1 (obtaining information about mobile phones): Relating
to information gathering, the responses fell into three groups:
nine said that people would ask their children; two said that they
should ask people of their own age (not their children); while 23
reckoned that people would go to mobile phone vendors for
information.
Scenario 2 (accepting a cast-off phone): Relating to the impact of
decreased ability to learn versus other motivations, three main
groups of responses arose: 11 answered yes, citing the
economical ‘You can sell the old one’; the philosophical ‘You
should take the challenge’; and the pragmatic: ‘The old phone
may be getting out of date’ as reasons. Seventeen answered no,
stating memory loss and difficulty in learning as reasons. A third
group of 6 reasoned that it depended on the person and the
circumstances.
Scenario 3 (emergencies such as having a stroke): Relating to
safety and ease of use, 21 participants said that a mobile phone
could be useful in emergencies, 12 felt that the elderly person
would be ‘too scared and confused’, or ‘unable to find spectacles’.
The rest felt that theoretically it was a good idea, but not practical
since elderly people find phones difficult to use, even more so
when stressed.
Scenario 4 (accessory in helping to remember): Relating to the
need for organisation, 28 reckoned that people could set a
reminder, of these 5 had reservations about whether elderly
people would manage to do that, whilst 1 was unsure that a
mobile phone would be of any help.
Scenario 5 (safety aid in travelling): Relating to safety and
usefulness the majority (27) agreed that it could be useful, they
gave different reasons such as the traveller contacting (phone or
SMS) the family or vice versa, but some believed it could be
used by a third party in the event of an emergency.
Participants demonstrated a clear intention to use mobile phones.
Possession of a phone, on its own, does not indicate that an
intention to use exists because many had been given their phone
and therefore did not necessarily consider it useful enough to
purchase a phone themselves. However, what was clear was that
everyone considered the phone useful in all of our scenarios and
that they consequently intended to use it and offered advice
related to possible usages to the persons in the scenarios.
Furthermore, some interesting influential factors emerged from
the discussions.
Scenarios 3 and 5 highlight their strong focus on safety and
security issues and their awareness of the fact that a mobile phone
can help them to feel more secure. Scenarios 2 and 4
demonstrated the participants’ awareness of their functional
limitations such as reduced memory and limited ability to learn
how to use a new phone or new features on their own phone.
Scenario 1 responses demonstrated that our participants would not
investigate the market themselves if they wanted to purchase a
phone – they would all consult either a family member, friend or
mobile phone vendor.
These scenarios point to a number of influential factors: user
context, which includes such aspects as functional capacity, safety
and security, economic limitations and recommendations from
friends and family. Now that we have shown the existence of an
intention to use in a variety of situations (usefulness) it is
necessary to explore what happens once these have been
established.
5.2 Usefulness
In this section we consider the findings from the design activity
(Appendix A) where participants had to select the six most
important features from the given set, (a number of blank buttons
were provided in case participants required an additional feature).
The most popular features are depicted in Table 3 ordered
according to priority. The features focus on communication,
safety and the need to organise, supporting earlier findings on the
importance of communication and safety aspects [20].
Table 3: Features desired
Button Name on button Number of
times selected
1 Nearest&Dearest 27
2 SMS_write 12
3 SMS_read 11
4 Police 11
5 Display_Own Number 9
6 Phone_book 8
7 Ambulance 8
8 CareUnit 6
9 Reminders 5
Our participants were free to choose any functionality they
wanted from the available buttons, yet the buttons chosen were
quite predictable. Numbers 1-4, 7 and 8 were all related to
communicating with others. Numbers 5, 6 and 9 were essentially
using the phone to help them remember numbers or events. Given
the availability of a wide variety of buttons, it is surprising that
the chosen buttons are so limited. The activity reported in this
section was designed to assess perceived usefulness. The
following section reports on actual use.
5.3 Actual use
Sarker and Wells [30] suggested an exploration and
experimentation module, and certainly we had the sense that
many of our participants had experimented with their phones soon
after coming into possession of them. Some communicated a
sense of frustration, verbalised as follows:
I just can’t use that predictive text, even though my daughter has
tried to show me how.
I am sure there is a way to get the phone to remind you about
things, but I can’t find it.
Please could you help me find my phone numbers – I have tried
but I can’t find them.
If the participants did indeed engage in an exploration phase, the
obvious outcome of that phase would be the usage of features
they had discovered and could use. Table 4 lists the features our
participants told us they used regularly (see question 8 in
Appendix A) and therefore depicts the outcome of their
experimentation with, and exploration of, the phone. We
confirmed that a general intention to use the phone plus their
sense that the phone could be useful, when they first got it,
resulted in a period of experimentation. However, their current
use appeared to include only a minimal subset of features –
mostly related to communicating as they would using traditional
phones. We intended to count the button presses in the second
part of this process in order to gauge effort expended and
consequent ease of use. We had to discontinue this since it
became obvious that the participants had difficulty finding their
most-used features. Some participants asked the interviewer for
assistance in finding the feature; others tried various routes down
the menu structure before triumphantly locating the desired
feature. We felt that the button press count was so inaccurate as to
be useless and therefore discontinued counting. Since the type of
mobile possessed by the participants was diverse, the unavoidable
conclusion is that most of our participants had serious ease-of-use
issues with their phones, whatever make they are.
It became clear that participants fell into two distinct groups:
those who had mastered the beast and those who were bemused
by it. The former were more adventurous in their use of the
phone, using more than just the minimal communication facilities.
The latter appeared to own the phone merely because it had been
given to them. They clearly saw that the phone could be useful,
especially in emergencies, but they did not enjoy ownership the
way the former group did. It appeared that for the latter group full
adoption had not occurred – they had not converted to the
technology — and this is likely to be due to a less than
wholehearted acceptance of the technology. Our findings
suggested that all users had explored the mobile phone but that a
number of them found a number of features too difficult to find
and reverted to using it merely as a mechanism for phoning
people when not at home – using a fraction of the functionality of
the phone.
6. Proposing STAM
Based on the integration of the three main activities in our
research approach: a literature study on technology adoption
models, an investigation into the context of the senior user and the
findings from our interviews, we propose the Senior Technology
Acceptance & Adoption Model (STAM) as depicted in Figure 3.
STAM consists of the following components, defined as:
• User Context such as demographic variables, social influence
and personal factors such as age and functional ability, for
example. Social influence is the prevalent external variable
and therefore depicted as a module in the user context. Social
influence aligns with Rogers’ observability innovation
attribute.
• Perceived usefulness is defined as ‘the extent to which a
person believes that using the system will enhance his or her
job performance’ [34]. This aligns with Rogers’ [28]
compatibility and relative advantage innovation attribute.
• Intention to Use is influenced by perceived usefulness and also
by user context.
• Experimentation and Exploration, which is the module where
the user first starts using the technology and forms first
impressions of the ease of use. Note that the experience
obtained here will feed back into confirmed usefulness. The
importance of this module confirms findings by Arning and
Ziefle [2] that performance was the main predictor of ease of
use. It also aligns with Rogers’ [28] trialiability innovation
attribute.
• Ease of learning & use results from the perceived ease of use
ie. ‘the extent to which a person believes that using the system
will be free of effort’ [34], and the final conclusion about ease
of use is directly influenced by the experimentation and
exploration stage. This aligns with Rogers’ [28] complexity
innovation attribute. Finally, whereas other models do not
incorporate the ease of learning aspect, the senior model needs
to, since difficulty in learning to use a device is a determining
factor for the elderly [37] as is the fear of failure [2] .
• Confirmed usefulness is the usefulness of the person’s phone to
him or her – composed of the features he or she is able to learn
to use.
• Actual use is indirectly predicted by the outcome of the
experimentation, which leads to ease of learning & use.
Facilitating conditions and the consequent ease of learning &
use predict actual use.
Finally, acceptance or rejection is predicted by ease of learning &
use and actual use, with the former more strongly influencing
acceptance.
STAM, like UTAUT and MOPTAM does not include attitude as
a determining factor. Van Biljon found no significant correlation
between attitude towards use and any of the other determinants.
This is supported by our observation that most people between the
ages of ten and 70 use mobile phones and indeed all our
participants owned and used phones. Dissatisfaction with the ease
of use of the phone did not deter people from intending to use the
phone – the social influences were far too strong to be offset by a
phone that was difficult to use. What was affected by ease of use
was actual use of the phone, and eventual acceptance.
Table 4: Feature use frequency (N=32)
Feature Sum Associated factor
Phone book 24 user context – memory
limitations
SMS 14 user context – economic
limitations
Phone using
number 11 user context – need for
social contact
Alarm 9 perceived usefulness
Check Missed calls 4 social influence
Camera 4 social influence
Figure 3 depicts the Senior Technology Acceptance & Adoption
Model (STAM) which models both acceptance factors and
adoption phases. We have replaced the multi-faceted attitude
module with modules depicting this progression from first
ownership towards actual acceptance.
Figure 3: Senior Technology Acceptance & Adoption Model
(STAM)
The newly proposed STAM captures the context of the elderly
mobile phone user in an improved way since it relates technology
acceptance factors to the adoption phases in the following way.
For elderly people the appropriation phase (see Table 1) is often
skipped. They seldom make the decision to buy as their first
phone is often given to them or bought for them (note that fewer
than 50% of our participants bought their current phone
themselves). In the objectification phase (see Table 1)
determining the role the technology will play manifests in the
behavioural intention which is influenced by social factors and
perceived usefulness. The incorporation phase describes the
interaction with the technology as represented by the
experimentation and exploration module. It is well known that the
elderly consider spending very carefully and the price of a device
or service is a differentiator for use [22, 33]. This is depicted in
the facilitating conditions module. Facilitating conditions,
perceived usefulness and ease of learning & use all influence
actual use. Acceptance implies that the user has progressed
through all the phases without being derailed by the various
facilitating factors. Rejection would result from a poor
experimentation experience and a resulting perception that the
device is too difficult to learn or to use. Whereas most other
models suggest eventual acceptance by all users, our experience
suggests otherwise, and our model reflects this. As noted in
section 2.3, STAM is not the first attempt at modelling technology
acceptance by the elderly adult user. Arning and Ziefle [2] studied
the influence of TAM factors on performance and found a
significant correlation between performance and ease of use. That
correlation was even stronger for the elderly age group. This
study is therefore consistent with their findings about the
dominant influence of ease of use in full-adoption. Phang et al.
[25] presented a model for representing the factors that influence
intention to use, where perceived ease of use as well as perceived
usefulnes was found to be highly significant in determining
intention to use. However, they found that age was not significant
in determining intention to use. This is consistent with our
findings that the elderly clearly intend to use a mobile phone but
actual use is clearly hampered by ease of use. While confirming
the importance of perceived usefulness and ease of use as
fundamental factors determining technology acceptance for this
age group, there are also significant differences between these
models because they focus on different components of the
adoption process. In contrast STAM depicts the transition from
usage to acceptance and conversion (adoption) – a step that some
users will never take since their progress is inhibited by poor ease
of use and consequent less than optimal confirmed usefulness.
Elderly people have the added complication of often skipping the
appropriation phase and this provides a plausible explanation for
the fact that some elderly mobile phone users never progress to
acceptance and the conversion phase.
Scenarios can be limiting due to their textual, sequential and finite
format, but we found the combination of scenarios and the design
activity useful in capturing attitudes and minimizing social
desirability bias.
7. CONCLUSION
This study investigated mobile phone acceptance by the elderly
adult user. We considered existing technology acceptance models
and extracted a set of factors that could influence mobile phone
acceptance. These factors were filtered by considering the context
of the elderly and then validated by means of semi-structured
interviews that included the presentation scenarios. The main
contribution of this paper is to propose the STAM for modelling
the acceptance process as driven by the factors that influence
mobile phone adoption in the context of the elderly mobile phone
user. By relating acceptance factors to adoption stages STAM
provides an explanation why many elderly adults never reach the
final adoption phase and never fully accept the technology. This
approach may also be useful in modelling technology acceptance
of other demographic groups. This paper also makes a
contribution of integrating research from different fields, i.e. the
qualitative research focusing on the acceptance process (from
Sociology) with quantitative research on the factors that influence
adoption (from Information Systems).
Research results from larger groups are needed to test the validity
and reliability of STAM in explaining the mobile phone adoption
of the elderly adult user. Furthermore the age group identified
(60-92) is quite broad and further work includes testing the model
on smaller age ranges e.g. 60-70 to ensure that cognitive abilities
are comparable across the age range.
8. ACKNOWLEDGMENTS
We acknowledge the NRF for financially supporting this project.
Appendix A: Questionnaire
1. What is your mother-tongue (first language that you learned
to speak)? ………………
2. Are you?
[a] Male [b] Female
3. How old are you?
[a] 60- 69 [b] 70- 79 [c] 80 or older
4. How would you describe your general level of computer
experience?
[a] None - I have never used a computer
[b] Low - I have used a computer belonging to
someone else
[d] Medium - I own a computer
[e] High - I am comfortable using a computer
5. Is your phone?
[a] Contract [b] Pay as you Go
6. Did you?
[a] Buy your phone [b] It was bought for me
[c] It was passed on by someone else
7. Scenarios presented in questionnaire:
1) Jim lives alone. One of his children has emigrated. He is 75
years old and needs to keep in touch. He has decided to get a
mobile phone so he can receive pictures and messages. Who
should he get advice from before he goes to buy a phone?
2) Leslie is a 75 year old with a mobile phone, which was given to
him by his daughter, and he has been using it for 2 years. He now
feels confident using it. She has now renewed her contract and
wants to give him her old Cell Phone. Do you think he will take
it?
3) Pam has had a stroke. She is worried that it will happen again.
Do you think she could use her mobile phone in some way to
make her feel less vulnerable?
4) Peter, aged 85, needs to take his medication every day at 12
noon and he keeps forgetting. Can his mobile phone help him?
5) Tim likes to travel alone now that he has retired. His family is
concerned about him. He says they shouldn’t worry because he
has his mobile phone with him. Is he right? What should he do to
allay their fears?
8. Tick features that the participant uses and record keys pressed
to do so:
Alarm Games
Calculator Torch
Calendar Phone with Phone Book (save numbers)
Camera Phone typing in number
Check missed calls Photo album/gallery
SMS Picture messaging
SMS with predictive
text Personalised ringtones
E-mail Profiles(change volume etc. )
Transfer Money Set reminders on calendar
FM radio Stopwatch
Other? Features you would like to use but don’t know how to: ...
Design activity:
A paper prototype of a phone (depicted in Figure 4) together with
separate paper buttons with the function names on (listed in Table
4) was presented to the participants. The buttons were randomized
for each participant who was then requested to select the six most
important functions (as represented by the buttons) and place
them onto the phone prototype in the designated space above the
menu bar.
Table 4: Functions on phone buttons
Ambulance Games Reminder Alarm Police
Get
Directions Bank Read
SMS Write
SMS Take
Photo
Send
Picture Nearest
&
Dearest
Display
my
number
See my
photos Balance
Directory
Services Call
Register Internet Bluetooth Phone
Book
Senior Mobile Phone
MENU
1 -.@ 2 abc 3 def
4 ghi 5 jkl 6 mno
7 pqrs 8 tuv 9 wxyz
* 0 +^ #
Figure 4: Mobile Phone prototype presented to
participants
9. REFERENCES
[1] Abascal, J.,Civit, A. Universal Access to Mobile Telephony
as a way to enhance the Autonomy of Elderly People, In
WUAUC 2001. Alcacer so Sal. Portugal. (2001).
[2] Arning, K.,Ziefle, M. Understanding age differences in PDA
acceptance and performance. Computers in Human
Behaviour. 23 (2007), 2904-2927.
[3] Baldi, R.A. Training older adults to use the computer. Issues
related to the workplace, attitudes and training. Educational
Gerontology. 23 (1997), 453-465.
[4] Botwinick, J. Cognitive processes in maturity and old age.
Springer, (1967).
[5] Brennan, Z. Oldmobiles roll up for grey drivers, The Sunday
Times. (1997), 13.
[6] Brewster, S. Overcoming the lack of screen space on mobile
computers. Personal and Ubiquitous Computing. 6(3)
(2002), 188-205.
[7] Burke, D.M., Mackay, D.G. Memory, Language and ageing.
Philosophical Transactions of the Royal Society: Biological
Sciences, 352 (1997), 1845–1856.
[8] Carroll, J., Howard, S., Peck, J., Murphy, J. From adoption
to use The process of appropriating a mobile phone. AJIS. 10
(2) (2003), 38-48.
[9] Davis, F.D. Perceived Usefulness, Perceived Ease of Use,
and User Acceptance of Information Technology. MIS
Quarterly. 13(3) (1989), 319-340.
[10] Davis, F.D., Bagozzi, R.P., Warshaw, P.R. User acceptance
of computer technology A comparison of two theoretical
models. Management science. 35(8) (1989), 982-1003.
[11] Dunlop, M., Brewster, S. The Challenge of Mobile Devices
for Human Computer Interaction. Personal and Ubiquitous
Computing. 6(4) (2002), 235–236.
[12] Haddon, L., Domestication and Mobile Telephony. In J.
Katz, (eds.) Machines that Become Us: The Social Context
of Personal Communication Technology. Transaction
Publishers, New Brunswick, New Jersey, 2003, 43-56.
[13] Hyyppä, K., Tamminen, S., Hautala, I., Repokari, L. The
effect of Mental Model Guiding User's Action in Mobile
Phone Answering Situations. In Electronical Proceedings
from The 1st Nordic Conference on Computer Human
Interaction. 2000. Stockholm, Sweden NordiCHI and
STIMDI 2000.
[14] Iacucci, G. Bridging Observation and Design in Concept
Development for Mobile Services. In: (eds.): Third
International Symposium on Human Computer Interaction
with Mobile Devices, IHM-HCI 2001. Lille, France, 2001.
[15] Jackson, G.R., Owsley, C. Visual dysfunction,
neurodegenerative diseases and aging. Neurology and
Clinical Neurophysiology. 21(3) (2003), 709-728.
[16] Jarke, M., Bui, X.T., Carroll, J.M. Scenario Management:
An Interdisciplinary Approach. Requirements Engineering
Journal. 3 (1998), 3-4.
[17] Jones, M., Marsden, G. Mobile Interaction Design. John
Wiley and Sons, Hoboken, NJ, (2005).
[18] Kiljander, H. Evolution and Usability of Mobile Phone
Interaction Styles. Thesis, Department of Computer Science
and Engineering, Helsinki University of Technology,
Helsinki, 2004.
[19] Kwon, H.S., Chidambaram, L. A test of the technology
acceptance model: The case of cellular telephone adoption.
In Proceedings of the 33rd Hawaii International Conference
on System Sciences. IEEE Computer Society, Hawaii (2000),
1-10.
[20] Lee, Y.S. Older adults' user experiences with mobile
phones:Identification of user clusters and user requirements.
Thesis, Industrial and Systems Engineering, Virginia
Polytechnic Institute and State University, Virginia, 2007.
[21] Malhotra, Y., Galletta, D.F. Extending the Technology
Acceptance Model to Account for Social Influence:
Theoretical Bases and Empirical Validation. In Proceedings
of the Thirty-Second Annual Hawaii International
Conference on System Sciences. IEEE, 1999, 1006.
[22] Mallenius, S., Rossi, M., Tuunainen, V.K. Factors affecting
the adoption and use of mobile devices and services by
elderly people – results from a pilot study. (2010).
[23] Osman, Z., Maguir, M., Tarkiainen, M. Older Users’
Requirements for Location Based Services and Mobile
Phones. LNCS. 2795 (2003), 352-357.
[24] Pedersen, E. Adoption of Mobile Internet Services: An
Exploratory Study of Mobile Commerce Early Adopters.
Journal of Organizational Computing and Electronic
Commerce. 15(3) (2005), 203 - 222.
[25] Phang, C.W.J., Sutano, A., Kankanhalli, L., Yan, B.C.Y.,
Teo, H.H., Senior citizens' acceptance of informations
systems: A study in the context of e-Government services.
IEEE Transactions on Engineering Management. 2006
[26] Phillips, L., Sternthal, B. Age differences in information
processing: a perspective on the aged consumer. Journal of
Marketing Research. 14(2) (1977), 444-457.
[27] Preece, J., Sharp, H., Rogers, Y. Interaction Design - beyond
human-computer interaction, ed. K. Santor. John Wiley &
Sons, Inc., New York, (2002).
[28] Rogers, E.M. Diffusion of Innovations. 5 th ed. The Free
Press, New York, (2003).
[29] Salthouse, T.A., Speed of behavior and its implications for
cognition. Handbook of the psychology of aging. Van
Nostrand Reinhold, New York, 1985.
[30] Sarker, S., Wells, J.D. Understanding Mobile Handheld
Device use and adoption. Communications of the ACM,
46(12) (2003),35-40.
[31] Silverstone, R., Haddon, L., Design and the Domestication of
Information and Communication Technologies: Technical
Change and Everyday Life. In Communication by Design:
The Politics of Information and Communication
Technologies. Oxford University, Oxford, 1996, 44-74.
[32] Van Biljon, J. ,Kotzé, P., Modelling the Factors that
Influence Mobile Phone Adoption. ACM International
Conference Proceeding of the 2007 annual research
conference of the South African institute of computer
scientists and information technologists on Riding the wave
of Technology. Port Elizabeth, (2007), 152 - 161.
[33] Van Biljon, J.A. A Model for Representing the Motivational
and Cultural Factors that Influence Mobile Phone Usage
Variety. PhD Thesis, School of Computing, University of
South Africa, Pretoria, 2007.
[34] Venkatesh, V., Morris, M.G., Davis, G.B., Davis, F.D. User
acceptance of information technology: Toward a unified
view. MIS Quarterly. 27(3) (2003), 425-478.
[35] Young, T. Software Interface Design for Small Devices.
(2003) [cited 2006/10/12] Available from:
http://www.cs.ubc.ca/~trevor/writings/SmallScreenDesign.pd
f.
[36] Yuan, Y., Zhang, J.J. Towards and appropriate business
model for m-commerce. Mobile communications. 1(1-2)
(2003), 35-56.
[37] Ziefle, M., Bay, S. Mental Models of a Cellular Phone
Menu. Comparing Older and Younger Novice Users. Lecture
Notes in Computer Science: Mobile Human-Computer
Interaction – MobileHCI 2004. LNCS 3160/2004 (2004), 25-
37.
