On keys` meanings and modes: the impact of different key solutions

Behaviour & Information Technology, Vol. 25, No. 5, September – October 2006, 413 – 431
On keys’ meanings and modes: the impact of different key solutions
on children’s efficiency using a mobile phone
MARTINA ZIEFLE*, SUSANNE BAY and ALEXANDER SCHWADE
Department of Psychology, RWTH Aachen University, Germany
The present study investigates the impact of different key solutions of mobile phones on
users’ effectiveness and efficiency using the devices. In the first experiment, 36 children (9 –
14 years) and in the second experiment 45 young adults (19 – 33 years) completed four
common phone tasks twice consecutively on three simulated phones that had identical
menus, but different key solutions. An approach was undertaken to quantify the
complexity of keys in three models, incorporating different factors contributing to the
keys’ complexity (number of key options, number of modes and number of modes with a
semantically dissimilar meaning), in order to predict users’ performance decrements. As a
further main factor, the degree of the users’ locus of control (LOC) was measured and
interactions with performance outcomes were studied. As dependent measures, the
number of inefficient keystrokes, the number of tasks solved and the processing time were
determined. Results showed a significant effect of control key solutions on users’ efficiency
and effectiveness for both children and young adults. Moreover, children’s LOC values
significantly interacted with performance: children with low LOC values showed the lowest
performance and no learnability, especially when using keys with a high complexity. From
the three factors contributing to the complexity of keys, keys exerting different functions
with semantically inconsistent meanings had the worst effect on performance. It is
concluded that in mobile user interface design keys with semantically inconsistent
meanings should be generally avoided.
Keywords: Mobile phones; Key complexity; Modes; Cognitive compatibility; Usability;
Menu navigation performance; Children
1. Introduction
Many of the technological devices today exhibit a combination of features with crucial implications for their ease of
use. They are small sized, with a diminutive display, but
powerful with respect to the functionalities provided. A
typical example for these devices is the mobile phone:
despite these phones’ increasing number of functions the
current market fulfils users’ demands for tiny devices.
The difficulty of combining this aesthetic standard with the
broad functionality results in complex hierarchical menus
and intricate navigation key solutions differing distinctly in
number of keys, their inherent functionalities, size, shape,
colour and spatial arrangement (e.g. Lee and Hong 2004).
Due to the demand for miniaturization and the increasing
number of functions and applications, new phones need to
be managed with fewer, or smaller buttons (Helle et al.
2003).
From studies dealing with the effects of hierarchically
structured information such as hypertext (e.g. Vicente,
Hayes and Williges 1987, Kim and Hirtle 1995, Lin 2001,
Pak, 2001) it is known that users have difficulties navigating
through the menu, often not knowing where they are and
where the sought-after function is located. However, in
mobile phones this ‘cognitive friction’ (Cooper 1999) is
even more distinct, mainly due to two factors. One is the
small display that allows only little information to be
displayed at a time and second, the ease of roaming within
the menu depends on the control keys whose functioning
must be transparent to the user.
*Corresponding author. Email: Martina.Ziefl[email protected]
Behaviour & Information Technology
ISSN 0144-929X print/ISSN 1362-3001 online ª 2006 Taylor & Francis
http://www.tandf.co.uk/journals
DOI: 10.1080/01449290500197086
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Recent studies examining the usability of different
existing mobile phones with comparable functionality
showed that navigation performance is strongly affected
by the different complexity of their operational logic (menu
depth and width as well as key solution) (Ziefle 2002a, b,
Bay and Ziefle 2005, Ziefle and Bay 2005). Mobiles with a
shallow menu and only few control keys lead to better
performance than complex ones. Methodologically, however, and this weakens the significance of the outcomes,
it is not clear what was responsible for performance
differences – the complexity of the menu, the control keys,
or an interaction of both. The current study therefore
addresses the impact of the complexity of control keys on
the usability of mobile phones independently from the
effects of the menu’s complexity, experimentally creating
mobile phones with the same menu but different key
solutions.
In the literature, we find many guidelines regarding
physical characteristics of keys and controls, such as force,
displacement and sensory feedback (e.g. Sanders and
McCormick 1993, Jordan 1998, Shneiderman 1998, Raskin
2000, Baumann 2001, Weiss 2002). Moreover, there are
many general recommendations and design principles for
the interaction of device and user (e.g. Burmester 1997,
Jordan 1998, Raskin 2000, Martel and Mavrommati 2001).
In contrast to the variety of information on keys and
controls in general, however, to our knowledge there is no
usability study dealing specifically with the differential
effects of keys and their meanings in mobile phones.
Moreover, there is no theoretical approach quantifying the
different factors contributing to the complexity of a key
solution.
The total number of keys and the number of functions
implemented in each key are two factors that contribute to
the cognitive complexity, since users have to understand the
‘production rules’ (IF-THEN connections) of each key (e.g.
Kieras and Polson 1985) and the key’s effects on the system
status, furthermore, users have to learn to distinguish
between the keys and remember the specific functions.
Consequently, the more control keys a mobile phone
provides, the more complex the key solution is because the
user has to identify the target key among the remaining
distracting keys at each point of the solution process.
However, a small number of keys do not necessarily lead to
higher usability (Raskin 2000). Namely, the convergence of
many functions to single keys – in order to keep the number
of keys small – also increases the complexity of keys,
because the number of inherent rules for different functions
within one key is usually augmented. In this context, the
concept of ‘modes’ is of central importance. Modes are
given if the system shows different reactions to one and the
same keystroke depending on the point of the menu
(Raskin 2000). An example for a well-known mode is the
computer keyboard where keys lead to different reactions
when the Caps Lock key is engaged. Keys that respond to
modes only work reliably when the current function of the
key is in the user’s locus of attention and visible or retained
in the short-term memory – which is usually not the case
(Raskin 2000).
One could argue that the negative impact of ‘modes’ on
usability and learnability of a device is nothing new and in
literature concerning usability a rather frequently addressed
research topic. While some researchers (e.g. Raskin 2000)
recommend to generally avoid modes in a technical design
(if possible) and to rely on other technical solutions instead,
it is a basic question if this is also feasible in small-screen
devices as e.g. mobile phones. A careful study of the
different navigation keys in the different current small
screen devices shows that hardly any of the devices seems to
come along without modes. Presumably, the ongoing
miniaturisation of the devices affects not only the screen
size, which is increasingly limited, but also the size of the
chassis and the space for the housing of navigation keys. In
this context, the basic idea of modes seems to be an elegant
escape from the shortage with respect to the amount of
space. If devices are not able to house many keys (due to
their restricted space), the implementation of modes and
the allocation of many functions to single keys could be a
proper solution. However, two points are of high ergonomic value in this context: (1) to learn if there is a sensitive
cut-off between making a device as easy as possible and
implementing the huge amount of functionalities that are
nowadays available (Helle et al. 2003); (2) to find out if
there is any combination of modes that is easier and more
transparent for the user than alternative combinations, and
if so, which the specific characteristics of such a combination are. This question is on the one hand rather difficult to
address as the literature provides no model or approach
which can predict the complexity of different key characteristics in interaction with a given menu. On the other
hand this question is of high practical benefit for designers
and manufacturers if answered how and how much the
different factors contribute to complexity.
What is possibly even more important than considering
merely having modes or not, are the semantics of a key. The
different functions of a key may be more or less
semantically consistent, that is, similar in their meaning,
which is completely disregarded by the concept of modes.
The softkeys in mobile phones for example, change their
function at different points of the menu according to the
changing label displayed above them on the screen, e.g.
‘OK’ and ‘Select’, both meaning a confirmation action,
reducing cognitive load with regard to understanding,
learning and memorising the key’s meaning. If a key is once
used to select an item and another time to delete, then it is
rather probable to confuse the user and provokes mistakes.
Also, if a key exerts its function only at one specific menu
level, not working at any other point in the menu, then this
On keys’ meanings and modes: children’s efficiency using a mobile phone
also represents a mode; however, a semantically dissimilar
mode that is definitively more difficult to comprehend and
memorise than a ‘simple’ one. An experimental observation
from our earlier experiments clearly shows the semantic or
cognitive difficulty of such a mode: participants (young
adults) had to solve a task on a mobile phone and learn to
use a key (e.g. a phone book key) in one context. When
they used the key at another menu level again, they noticed
that the very same key that had worked perfectly one
moment before now did not have any effect (as the user had
moved to a menu level where the key has no function at
all). Interestingly, this was a typical misinterpretation
where participants did not conclude, as would have been
correct, that this key has a mode with one function in a
specific menu level and with no function at all other levels.
They rather misunderstood the key as ‘out of order’. This
differentiation between different kinds of modes leading to
different degrees of cognitive demands has been completely
disregarded up to now by the mode concept, instead of
being treated as similarly difficult. Arguing with another
theoretical concept – Norman’s (1981) activation-triggerschema system – it is assumed that action sequences are
controlled by sensorimotor knowledge structures, so-called
schemas. Schemas are activated and selected by a triggering
mechanism that requires the appropriate condition.
Through stimulus generalisation one and the same schema
can be activated in similar even though not exactly
corresponding conditions. This approach may help to
understand why the semantic meaning of a function is of
importance. If a key exerts completely different functions
on different menu levels, it is not possible to develop welldefined and clear-cut schemas that are valid for single keys.
Hence, performance does not reach a stage of autonomous
action activation. As a consequence, the usage is therefore
not easy to be learned and memorised and the identification
of the appropriate key is in danger to be distracted by
competing keys not at issue.
2. Questions addressed and experimental logic
Since the current study is concerned with effects of
navigation keys on performance, it is of central impact to
examine the effects of the complexity of keys independently
of the complexity of the menu. Methodologically arguing,
the different key characteristics must be implemented on
one and the same menu in order to isolate this factor from
other confounding variables (as e.g. the breadth and width
of the menu structure or shortcomings with respect to
functions naming and allocation of functions to categories).
Moreover, there is a number of different characteristics of
navigation keys conceivable that do possibly contribute to
the complexity of keys given in mobile phones. In the
present study, three main key characteristics were considered as having a crucial impact on keys’ complexity,
415
even though this selection is not necessarily exhaustive, but
of course arbitrarily: (1) the number of key options; (2) the
number of keys having modes; and (3) the number of keys
that have semantically inconsistent functionalities in
different modes, representing operation rules that are more
difficult to comprehend and mentally represent. Meeting
demands of ecological validity, the three key features at
issue were extracted from existing key models in real
brands. Analysing and weighing the features differentially,
a model was developed, which enables the prediction of the
relative augmentation of complexity due to the three
different factors present in key solutions (the term ‘solution’
is used in the ongoing text, as each navigation keys
structure has its own combination of the three characteristics considered). Independent variable was the complexity
of navigation key solutions in three graduations. Its effects
on effectiveness and different measures of efficiency were
taken as dependent variables.
To get a broad insight into the effects of different key
complexities in different user groups, a differential approach was pursued. In the first experiment, children were
chosen to assess the specific impact of the control keys’
complexity on users who grew up with technology and who
represent the users of the future. This may give good
insights into the demands manufacturers and ergonomists
will be facing tomorrow. It is often assumed that some
usability issues will become less important because of
people’s contact with technology from early on; this
supposition will be questioned with this study. For a
validation and a broader generalisability of the results, a
replication of the study with young adults (university
students) is undertaken in Experiment 2. Students can be
regarded as the ‘best case’ user group, technologically
prone and, rather sophisticated with respect to their
cognitive capabilities, without possible limitations of the
interpretation of the results due to developmental processes, neither ascending as given in children, nor
descending, as in older adults. Another factor that was
shown to mediate the efficiency handling technology is the
locus of control regarding the usage of technological
devices (Beier 1999, Bay and Ziefle 2003b, Ziefle et al.
2004). Locus of control is defined as a person’s expectation
regarding the connection between their actions and their
action outcomes. Internal locus of control means that a
person usually attributes his success to his own competency, while people with external locus of control ascribe
their action outcomes not to themselves, but rather to
chance or others. When transferring the concept to the
context of using technology, the degree of a person’s own
confidence handling technological devices is of central
interest. However, and this is not known so far, it is unclear
whether the concept applies also for children’s selfestimation of their performance level. Thus it was examined
whether there is a correlation between children’s and young
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adults’ individual beliefs about their abilities to competently use mobile phones. Furthermore, it is very insightful
to determine possible interactions between key complexity
on the one hand and the degree of feeling competent with
technology on the other hand. Therefore, in this study,
besides processing tasks on mobile phones that differ in
their navigation key solutions, the participants completed a
short version of Beier’s (1999) questionnaire assessing the
locus of control regarding technology.
3. Pre-study
In a pre-study it was assessed which control key solutions
and which menu may be used. In this context it was a general
question if a complex menu necessarily needs to be operated
with a complex key solution, as this is often the case for real
devices. Therefore, an extensive pre-study with computer
simulations was carried out, in which existing menus of real
mobile phone devices with existing key solutions were
emulated. To combine an experimental design (variation of
one factor while holding confounding factors constant) with
demands of ecological validity, three key solutions of
existing models were selected and adapted in order to
operate one and the same menu, also originating from a real
mobile phone. The rather complex (deep) menu structure of
the Siemens C35i was chosen and it was found out that only
a few changes in the software of the phones’ menu were
necessary to allow an operation with the key solution of two
other widespread models for the mass market – the Nokia
3210 and the Siemens S45 – in addition to the original key
solution. As shown in the following sections, the three key
solutions differ considerably, regarding the number of keys,
their inherent rules and their overall complexity. The Nokia
key solution consisted of few and very simple keys, and the
Siemens S45, with the same number, but less complex keys
than the original keys of the Siemens C35i (cf. table 1). The
fact that the complexity of keys is not necessarily connected
to the complexity of the functions implemented is of
importance, last but not least for methodical and operational
issues of the current experiment. In order to refer the
outcomes exclusively to the effects of the different key
complexities, it was a central methodological requirement to
ensure that in each key-menu combination the same number
of steps were necessary to solve the task on the shortest way
possible. This was achieved with our solution and will be
described in the next sections.
4. A quantitative approach to key complexity
In the following, a prediction model is developed on the
basis of three factors contributing to keys’ complexity,
which have not been differentiated up to now: the number
of options to be pressed in a key solution, the number of
different functions a key can exert (modes) and the number
of semantically dissimilar modes. In the three predictions
the three factors are weighed differently, thus resulting in a
different complexity of the three key solutions and as a
direct result in a differing predicted efficiency and effectiveness handling the mobile phones. It should be noted that
any proposed model and its validity naturally depends on
the sensitivity and the extent of differentiation of the factors
that are implemented into the model. It is a major concern
of the study that all three key features proposed have
similar characteristics for the three tested mobile phones.
Hence, any correspondences differ only for their proportions, which were selected as reasonable. However, the
selection is arbitrary, which means that more and different
parameters may equally contribute to cognitive complexity.
Nevertheless, the three parameters seem to reflect very basic
key characteristics of real brands and are therefore
implemented into the model. On the base of the fit of
predictions with the empirical findings, other characteristics, disregarded so far, will be discussed.
Before the model is developed, first the single keys of
each solution will be described in detail.
4.1 Key description
The three solutions differed with regard to the number of
keys (more specifically, the number of different options to
be pressed), the number of keys with modes, the number of
different functions each of these keys can exert, the number
of semantically inconsistent functions as well as the number
of redundant key functionalities. To describe the complexity, the key solutions were analysed according to the
different functions the keys exert in the four tasks that had
to be solved by participants: calling a number, sending a
short message, hiding one’s own number and setting a call
divert. An overview on each of the keys’ modes of
operation within the three solutions is provided in table 1.
According to table 1, the Nokia 3210 navigation keys (in
the following referred to as Phone A or keys of Phone A)
exhibit four key options and two of them are sensitive to
modes. The c-key is used for corrections of letters and digits
as well as for returns to higher menu levels, therefore
having modes, yet these two functions can be regarded as
semantically consistent, as they both mean ‘undo’. The
centrally positioned key is a soft key used to enter the
menu, to select highlighted menu entries, to confirm and to
effect calls (four functions, three of them semantically
similar representing confirmation actions, but entering the
menu is not a confirmation action and can therefore be
regarded as semantically dissimilar). The scrolling key
effects movements up and down within any level of the
menu (two functions but no mode).
The Siemens S45 (in the following referred to as Phone B
or keys of Phone B) has overall, eight key options. There
are two soft keys used to carry out the function that is
On keys’ meanings and modes: children’s efficiency using a mobile phone
417
Table 1. Description of the keys present in three different keys solutions (Nokia 3210 (phone A), Siemens S45 (phone B), Siemens
c35i (phone C)). The keys are structured within the number of key options, the number of keys with modes, the number of
functions per key at a time, the number of modes per key, the number of dissimilar functions and the number of redundantly
allocated functions.
displayed above them in the display. The left soft key has
six functions (enter the phone book, access the mailbox,
change, correct, save and sometimes no function at all),
four of those being semantically dissimilar functions (select
the phone book, save and access the mailbox, as well as ‘no
function’). ‘Change’ and ‘correct’ can be regarded as
semantically similar. The right soft key is used to enter
the menu, to select a highlighted function, to send and to
confirm (four functions, two semantically consistent confirmation actions and two dissimilar actions). Moreover,
there is a big round button with four direction arrows (no
modes). The arrows pointing up and down always have the
function of scrolling up and down within the menu. The
arrows pointing left and right are only used to select menu
entries and step back to higher menu levels (in the tasks
carried out here). These are redundant functions because
the buttons with arrows pointing to the left and right have
the same functionality as the right soft key (when used to
select) and the red receiver button for returns to higher
menu levels. These redundant functionalities may decrease
the complexity of the solution because actions can be
carried out in two ways (Sanders and McCormick 1993).
Furthermore, there is a green receiver key on the left side to
make calls when digits have been entered (one semantically
dissimilar mode, as it works only on very few occasions)
and a red receiver button on the right side to end calls as
well as returning to higher levels in the menu (two modes,
one being semantically dissimilar from the other).
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The C35i solution (in the following referred to as Phone
C or keys of Phone C) consists in total of seven key
options. Each of the two rocker switches contains two
options (marked by a dot on each of side of the rocker
switch) were the key to be pressed, thus resulting in two
‘stroke options’ per key. Sometimes (depending on the
menu level) however, there are not two different options to
be selected, but the same function is exerted, independently
of the side of the rocker switch that was pressed. This is
shown by the label on the display above the specific key or
part of the key, respectively. The left rocker switch has six
different modes. The functions exerted are scrolling (left:
up, right: down), selecting the mailbox, changing, saving
entries, and sometimes it has no function at all, depending
on the point of the menu. Four actions are semantically
very different from the scrolling function. The right rocker
switch serves to enter the menu, to select, to correct (left
part) and confirm (right part), or to correct (left) and save
(right), and to send a message (eight functions/combinations of functions, where six are semantically dissimilar
from selecting/confirming). Additionally, there is an extra
key with an icon (open book) to enter the phone directory,
which is not active most of the time, thus representing two
modes, one being semantically dissimilar from the other.
Furthermore, a big, centrally positioned key with a green
receiver is used to make calls, also only exerting a function
at specific points of the menu, otherwise having no
function (one function being semantically dissimilar from
the second). Finally, Phone C has a smaller key with a red
receiver sign to end calls as well as for hierarchical steps
back in the menu (two modes where one is a semantically
dissimilar function).
4.2 The prediction of cognitive complexity
In order to estimate how complex a certain key solution –
the combination of all control keys – is for the users,
different parameters can be taken into consideration.
4.2.1 Prediction on the basis of the number of key options (all
keys treated as equivalent). As users have to make a decision
at each point of the solution process concerning which of
the different key options is to be pressed, firstly the number
of possible key choices should be regarded. The resulting
complexity of the three key solutions for each of the four
tasks considering only the key options is calculated in the
second line of table 2. Phone B, for example, consists of
five keys but one of them has four options, thus the
complexity would be eight. The table shows that with this
parameter the lowest complexity is found for the keys of
phone A. Compared with this solution phone C is 75%
more complex and the keys of phone B are the most
complex with 100% higher complexity than phone A (cf.
table 2).
4.2.2 Prediction on the basis of the number of key options,
modes and redundant functions (all keys and modes were
treated as equivalent). A second factor contributing to
complexity – as mentioned before – is the number of
different modes each of the keys has, that is, the number of
functions that may be carried out with each of the keys. In
this model, the number of options per key was multiplied
with the number of modes it has and added. As having
redundant functions implemented should ease the use of the
keys, the number of redundant functions was subtracted
from the total. This leads to the predicted complexity found
in line three of table 2. Taking again phone B as an
example, it is shown in table 1 that four of its five keys have
modes, thus exerting more than one function. Adding the
number of functions each option can exert (6 þ 4 þ
(4 6 1) þ 2 þ 2) and subtracting the two redundant functions, a complexity of 16 is calculated for this key solution.
With these parameters again the keys of phone A result in
being the least complex solution and phone B is again
100% more complex than phone A. Phone C, though, is
now the one with the highest complexity, namely 150%
higher than phone A and 25% higher than the key solution
of phone B (cf. table 2).
4.2.3 Prediction on the basis of the number of key options,
modes and meanings (semantically dissimilar modes weighed
by factor 2) as well as redundant functions. The third
relevant factor mentioned in the Introduction section is the
fact that functions exerted by one and the same key can be
semantically similar or dissimilar. As (semantically) dissimilar modes are supposed to increase users’ confusion about
a key, this is introduced in the next model predicting
cognitive complexity by weighing each dissimilar mode
with the factor 2, that is, they contribute with double the
strength of a semantically similar mode to cognitive
complexity. Again, keys and modes are multiplied and
the two redundant functions are subtracted (for phone B).
For phone C keys this means that when adding the total
number of dissimilar functions (in total, eight) to the
complexity calculated in the former model (in total, 16) we
arrive at a complexity of 24. Thus, this last model leads to
the prediction that compared to the key solution of phone
A, phone B is 167% more complex and phone C is even
244% more complex. The increase in complexity from
phone B to phone C is 29%.
The predicted differences in cognitive complexity between
the three key solutions should lead to comparable differences
in users’ performance when interacting with the keys. Figure 1
visualises the cognitive complexity of the key solutions
predicted with the three models (key options only, key
options plus modes or key options, modes plus meanings). If
semantically dissimilar modes do indeed have the dramatic
impact on usability that we assume, performance outcomes
should best match with the predictions of model 3.
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On keys’ meanings and modes: children’s efficiency using a mobile phone
Table 2. Predicted increase in complexity on the basis of different model assumptions (Model 1: key options; Model 2: key
options6the number of modes – redundant options; Model 3: key options6the number of modes6number of dissimilar modes –
redundant options) for three key solutions (Nokia 3210 (phone A), Siemens S45 (phone B), Siemens c35i (phone C)).
Phone A
Phone B
Phone C
Model 1:
Key options
4
8
þ100%
7
þ75%
Model 2:
Key options6number of modes – redundant options
8
16
þ100%
20
þ150%
Model 3:
Key options6number of modes6number of dissimilar function – redundant options
9
24
þ167%
31
þ244%
Figure 1. Predictions with respect to the impact of cognitive
complexity in three models: white bars (model 1): predictions on the basis of key options; grey bars: predictions on
the basis of key options and modes (model 2); black bars:
predictions on the basis of key options, modes and
semantically dissimilar modes.
5. General method
In this section, the methodological details are given that
refer to both experiments. This includes the operationalisation of the independent and dependent variables, the
material used as well as the technical hardware and the
software design. Specific aspects concerning one of the two
experiments will then be described in the respective sections.
5.1 Variables
The first independent variable was the complexity of the
navigation key solution of the mobile phones in three steps
(as defined in table 2, phone A, B and C). The menu of the
phones was identical representing the original Siemens C35i
menu, which in previous studies (Ziefle 2002a, b, Bay and
Ziefle 2003a, 2005, Ziefle and Bay 2005) had shown to be
rather difficult to use. The second independent variable is
the locus of control regarding the use of technology.
Therefore users were divided into two groups by median
split.
As dependent variables of the experiment, three different
measures were focused: First, as the most direct measure,
the number of inefficiently used keys were analysed. These
were all keys pressed while completing the tasks, which did
not lead to any or no task-related reaction on the display.
The following keystrokes were specified: soft keys, arrow
keys, green receiver keys within the menu, red receiver keys,
c-key or the phone book key when not exerting a function,
hash and asterisk at any point in the menu, digits except
when task relevant. The different inefficiently used keys
were summed up to the total number of ineffective
keystrokes. Second, as a measure of effectiveness, the
number of successfully solved tasks was determined. Third,
as efficiency measure, the time on task was assessed.
5.2 Locus of control interacting with technology
The test assessing users’ locus of control regarding the use of
technology (LOC) was developed by Beier (1999). It is
available in a long version consisting of 24 items and a short
version with eight items. According to Beier’s own studies
the reliability of the short version shows a Cronbach’s
Alpha of .89. A second study with 36 participants yielded a
reliability of 0.90 (Beier 1999). For the first study presented
here with children as participants, the original items were
adapted to a language that is appropriate for them. Since
‘technical problems’ are of central interest, it was ensured
that the children knew exactly what was meant. The
experimenter defined ‘Technical problems’ with examples
from the children’s everyday life, such as to tape-record a
movie on the videocassette recorder, to handle video games,
to repair the gear change of a bike or working on the PC.
The children had to answer eight questions measuring their
LOC regarding the usage of technical devices. As examples
for the items used in this questionnaire, children were asked
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whether they think that many technical devices are too
difficult to understand and use or whether they
enjoy cracking a technical problem if present. The participants’ task was to confirm or deny on a six-point-scale
(from ‘not at all true’ to ‘absolutely true’) the respective
questions.
5.3 Tasks
Four very frequent telephone tasks were selected. In order
to see whether complex solutions can be learned easily, a
second trial was run, where the tasks had to be completed
in a slightly modified manner:
1. Calling a number: users had to enter 10 digits (the
phone number) and press one control key.
2. Sending a short text message (in order to control the
differences in the speed of typing, the message was
already provided and only had to be sent when
participants reached the adequate point in the
menu): Users had to perform 11 steps within the
menu and enter 10 digits (the phone number where
the text message had to be sent to).
3. Hiding one’s own number when calling somebody:
19 steps had to be executed.
4. Making a call divert: 14 steps were necessary.
5.4 Material
The three mobile phones were simulated as a software
solution and run on a PC, which was connected to a touch
screen. The original key solutions differed in a number of
factors apart from the focused functions exerted in this
study. To interpret solely the keys’ meanings and modes,
ruling out any other key characteristics or phone features to
be possibly confounding (size, haptics, the interspace
between keys, etc.), the simulated phones were carefully
matched in their appearance (same proportions in physical
dimensions such as keys’ size and inter space as well as
display, font type and letter size). In order to avoid any
biases, a virtual mobile phone was presented on the screen,
not resembling any specific brand, but rather kept ‘neutral’
and unobtrusive without eye-catching colouring or prominent features. It was intended that the emulated mobile
phones were not in the foreground, but rather the
operational logic in the three key solutions. As children
may have problems with the not yet fully developed
psychomotor abilities that are necessary to hit the phone
buttons properly, buttons and text layout were somewhat
increased, ensuring proper handling and good viewing
conditions. Providing optimal information density on the
display (Bay and Ziefle 2004), three menu functions could
be seen on the display at a time. Users’ actions were logged
online in order to reconstruct in detail which key was
pressed and how often and at which point within the menu.
It should be noted that issues of tactile feedback of the keys
were not considered; therefore, the simulations on the
touch-screen should provide valid results.
6. Experiment 1: children interacting with keys differing
in complexity
In the following section, an experiment conducted with
children using mobile phones with three key solutions
differing in complexity while holding the menu constant
will be reported.
6.1 Participants
Thirty-six children between 9 and 14 years (M ¼ 11 years)
volunteered to take part in the study. The children had
answered to announcements published in different schools
by a letter addressed to children and their parents in
which they were invited to join an experimental study
dealing with the usability of different mobile phones. As
the usage of technology and, especially, mobile phones is
highly estimated in child users, children responded highly
motivated to join the study. The 36 children were matched
by age and gender to the three experimental groups in
such a way that homogenous groups resulted. In each of
the groups, five boys and seven girls participated, with a
mean age of 11.3 years in the first group (phone A), a
mean age of 10.8 in the second experimental group (phone
B) and a mean age of 10.8 in the third group (phone C).
Moreover, the questionnaire assessing the LOC when
using technological devices was carried out before the
experiment. The three experimental groups did not differ
significantly with regard to this cognitive style (phone A:
M ¼ 64.7 points; phone B: M ¼ 68.9 points; phone C:
M ¼ 68.5 points, out of a maximum of 100 possible
points).
To ensure that differences in navigation performance are
due to the different complexities of keys and not to different
experience with other technical devices, a very detailed
interview was undertaken before the experiment assessing
the children’s expertise.
. Frequency of usage. Participants were to state if and
how often they use technological products (mobile
and wireless phone, Fax, PC and video cassette
recorder (VCR)), using a 5-point scale (1 ¼ several
times per day, 2 ¼ once per day, 3 ¼ once or twice a
week, 4 ¼ once or twice per month and 5 ¼ less than
once or twice a month).
. Interest in technology. Further, the children rated
their general interest in technology, using a 4-point
scale (1 ¼ very low interest, 2 ¼ low interest, 3 ¼ high
interest, 4 ¼ very high interest).
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On keys’ meanings and modes: children’s efficiency using a mobile phone
. Ease of use. Moreover, the estimated ease of use when
using different technical devices had to be stated
again on a scale with four answering modes (1 ¼ the
usage is easy, 2 ¼ the usage is quite easy, 3 ¼ the usage
is quite difficult and 4 ¼ the usage is difficult).
The outcomes of the self-reports of the children shows a
picture of a user group that is rather experienced with
technology (table 3).
As can be seen in table 3, the children use a wireless
phone daily, but, however, the mobile phone only twice a
week. Twenty-three of the 36 children reported using it
mostly only to make and answer calls; 13 children use the
mobile also to send text messages or for more complex
functions. The children started to use the PC daily, and a
VCR about once per month, while the fax machine is used
less than once per month. The overall interest in technology
was medium (M ¼ 2.1 points). With respect to the selfreported ease of use of technical devices, the handling of a
wireless fixed-line phone is among all other devices rated as
most easy. It was judged that the PC and the mobile phone
as well as the VCR were handled quite easily. Only the
handling of a fax machine is reported to impose a more
difficult usage. With respect to interest in technology in
general, the children reported having a moderate interest in
technology in all three groups (phone A: M ¼ 2.1, phone B:
M ¼ 2.0 and phone C: M ¼ 2.3 points), not differing from
each other, as ensured by non-parametric Kruskal-Wallis
Tests. Except the difference between groups (the frequency
of using a wireless fixed-line phone was slightly more
frequently used in the phone B group), no significant
differences with respect to the previous experience with, and
interest in, technology in general and the judged ease of use
were detected between the three experimental groups. Thus,
differences in the performance can be interpreted as a
function of the navigational keys’ complexity and confounding effects of the children’s differential previous
experience can be ruled out.
6.2 Procedure
At the beginning of the experiment, the users’ previous
experience with technological devices was assessed. In order
to get familiarised with the experimental apparatus,
especially the handling of the touch-screen, this questionnaire had to be completed using the touch-screen. Then, the
experimenter assessed the children’s locus of control
regarding the use of technological devices by reading out
the eight items, which had to be answered by the children
on the 6-point scale. Afterwards, the children completed
four telephone tasks that were applied twice in a slightly
modified manner in order to measure effects of learnability.
A time limit of 5 minutes was set for each task. It was
ensured by usage of a child-friendly language that the
children understood exactly what they had to do in each
task. They were instructed to solve the tasks as fast and
thoroughly as possible. If a task was solved successfully, a
‘Congratulations’-message appeared on the display. User
manuals were not provided. After the experiment, the
children were gratified for their efforts with a small present
they could choose from a variety of toys. Depending on the
individual working speed, the whole experiment for one
participant lasted between 30 and 50 minutes.
6.3 Results
The results were analysed by multivariate analyses of
variance and when assessing task difficulty, with analyses
for repeated measurements. The main factors were the
navigation key solution and the degree of LOC (dichotomized by median split into a group with higher and a
group with lower values). The dependent variables were
the number of inefficient keystrokes in completing the
four tasks, the effectiveness (number of correctly solved
tasks) and the time needed to process the tasks. For
determining the overall complexity of the key solution, the
performance in the eight tasks was summed up. Assessing
learnability effects, the first trial of the four different tasks
was summed up for each measure and contrasted to the
performance in the second trial. Finally, correlations of
different user characteristics (previous experience with
technology, locus of control) and navigation performance
were undertaken.
6.3.1 Effects of key solution. In order to determine the
overall effect of the key complexity, a MANOVA was
Table 3. Mean ratings of children’s self-reports with respect to the experience and judged ease of using technology.
Mobile phone
Wireless phone
Fax
PC
VCR
The frequency of using a device (1 ¼ several times per day,
2 ¼ once per day, 3 ¼ once or twice a week, 4 ¼ once or twice
per month and 5 ¼ less than once/twice a month)
The ease of using a device (1 ¼ the usage is easy,
2 ¼ the usage is quite easy, 3 ¼ the usage is quite
difficult and 4 ¼ the usage is difficult)
3.5
2
4.9
2.4
3.9
1.77
1.1
2.4
1.75
1.8
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carried out with the number of inefficient keystrokes, the
effectiveness (number of tasks solved successfully) and the
time on task. Between subject variables were the complexity
of keys (three groups: phone A key solution, phone B key
solution and phone C key solution) and the degree of locus
of control (two groups, high and low competence,
segregated by median split).
The MANOVA analysis yielded significant omnibus
effects of the cognitive complexity (F (2, 52) ¼ 1.8; p 5 0.1).
The degree of LOC alone did not significantly affect the
performance, but the combination of both factors, the key
complexity and the degree of LOC, were shown to
significantly interact (F (2, 52) ¼ 2.2; p 5 0.05). The outcomes are visualised in figure 2 for the three dependent
measures (left side: number of inefficient keystrokes; centre:
number of tasks solved correctly; right side: time on task).
In all measures, the factor complexity of key solution
affects performance in the same way: it was always the key
solution of phone A which showed the best performance
(number of inefficient keystrokes: M ¼ 9.8; number of tasks
solved: M ¼ 6.2 (out of eight); time on task: M ¼ 1074 s)
and the worst performance for phone C (number of
inefficient keystrokes: M ¼ 42.6; number of tasks solved:
M ¼ 5.6 (out of eight); time on task: M ¼ 1220 s), with the
phone B key solution ranging between these two (number
of inefficient keystrokes: M ¼ 19.9; number of tasks solved:
M ¼ 5.9 (out of eight); time on task: M ¼ 1087 s). With
respect to the single F-tests, the number of inefficient
keystrokes was significantly affected by the different key
solutions (F (2, 52) ¼ 6.3; p 5 0.01), while for the number
of tasks solved and the time on task, the significance level
was not reached.
Now, the interaction of the keys’ complexity and the
degree of LOC is of interest. As the analysis showed, again,
the number of inefficient keystrokes was the most sensitive measure, yielding significant effects (F (2, 52) ¼ 4.6;
p 5 0.05), while the other measures did not reach
significance alone but contributed to the omnibus value.
Figure 3 illustrates that the pattern of results was similar
for all measures, depending on the complexity of the key
solution. Regarding the number of inefficient keystrokes
(figure 3, left side) it can be seen that with the phone A
keys, all children, independent from their degree of LOC,
showed the lowest number of inefficient keystrokes (low:
M ¼ 10.1 inefficient keystrokes; high: M ¼ 9.2 inefficient
keystrokes). For phone B, the ‘gap’ between both LOC
groups is slightly, but non-significantly bigger, with the
group with the low LOC pressing the keys 24 times
inefficiently and the high LOC-group, 16 times. A very
dramatic difference occurred in the phone C key solution:
while children with high LOC values performed rather well
with, on average, 17.5 inefficient keystrokes, the low LOC
group yielded the worst performance (M ¼ 92.8 inefficient
keystrokes).
The other two dependent variables showed a comparable
picture. In the phone A solution, both LOC groups solved
the same number of tasks: on average 6.2 out of eight tasks
were solved successfully. Both more complex key solutions
were much harder to grasp for the low LOC children, with
only 5 tasks (phone B) and 4.7 tasks (phone C) solved
correctly. For the children with high LOC values, the
performance of all key solutions lay close together (phone B:
M ¼ 6.8 tasks, phone A: M ¼ 6.2 tasks and phone C:
M ¼ 6.1 tasks). Finally, looking at time on task, the pattern
showed to be the very same. The phone C keys performed
worst again, with the strongest effect for children with low
LOC values, needing 1442 s to process the tasks with the
phone B. With the phone A keys, the children needed only
Figure 2. Effects of key complexity on the number of inefficiently used keys (left), the number of correctly solved tasks
(centre) and the time on task (right) for the children group (N ¼ 36).
On keys’ meanings and modes: children’s efficiency using a mobile phone
1070 s and the phone B was ranging between both other
keys (M ¼ 1322 s).
6.3.2 Learnability. This analysis focuses on the question
whether the cognitive complexity of phone keys can be
understood in a second trial, that is if the children were able
to grasp the rule of the keys and use them correctly in the
second trial. Again, the most obvious effects were found for
the number of inefficiently used keys. In figure 4, left side,
the first trial is contrasted to the second trial in all three key
solutions. Even if the overall number of inefficiently used
keys dropped significantly (F (1, 30) ¼ 3.9; p 5 0.1), the
differences between the three key solutions remained, even
in a second trial (first trial: F (2, 52) ¼ 3.8; p 5 0.05);
423
second trial: (F (2, 52) ¼ 5.8; p 5 0.05)), corroborating that
the key’s logic was not caught equally well in the three key
solutions. It was phone C that caused the largest number of
inefficient keystrokes in the first (M ¼ 22.8) as well as in the
second trial (M ¼ 19.8). In contrast, the number of
inefficiently used keys was 7.7 in the first trial and only
2.1 when using the phone A keys, and in phone B the
number of inefficiently used keys was 14.8 in the first, and
5.1 in the second run. Thus, it can be shown that a benefit
by training cannot be found in the rather complex phone C
key solution, while the other two key structures were
understood by the children, taken from the distinct
performance increments in the second compared to the
first trial. A closer look into the learnability results in both
Figure 3. Effects of key complexity and degree of LOC on the number of inefficiently used keys (left), the number of correctly
solved tasks (centre), and the time on task for children with low LOC (black lines) values and high LOC values (grey lines).
Figure 4. Effects of key complexity and learnability on the number of inefficiently used keys for the first and the second trial
(left), and for children with low and high LOC values in the first (centre) and second trial (right).
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LOC-groups (figure 4, centre and right side) showed the
combination of having a low LOC and using the phone C
solution with many dissimilar modes to be most fatal, not
only in the first (F (2, 52) ¼ 3.1; p 5 0.1), but still and even
stronger in the second run (F (2, 52) ¼ 4.3; p 5 0.05).
As before, the patterns regarding effectiveness and time
on task were comparable, but did not reach the significance
level. In the first trial, using the phone A keys, the children
solved on average 3.1 tasks in the first and in the second
trial, needing 587.8 s when accomplishing the tasks for the
first time and only 477.1 s in the second, trial. In the phone B
condition, the number of tasks solved was 2.6 in the first
trial and 3.2 in the second accompanied by a processing time
of 636.6 s in the first and only 442 s in the second trial. Once
more, the worst performance was found in the key solution
of phone C: in the first trial, the children solved, on average,
2.9 of the four tasks, thereby needing 668 s. In the second
run, 2.8 out of four tasks were solved correctly in 552 s.
6.3.3 Key level. Up to this point, the inefficiently used keys
in the three key solutions were globally analysed by
comprising all different types of inefficiently used keys. It
might be continuative though to have a look at specific key
types that were used inefficiently. Thus, in this section, a
very basal insight into selected types of inefficiently used
keys is provided. Four types of keys were descriptively
analysed: (1) examples of modes with a semantically similar
meaning; (2) example of a key with one function, which is
only exerted at certain points, therefore regarded as
semantically dissimilar mode; (3) examples of keys with
different modes that are semantically different; and (4)
examples of inefficiently used keys that were at no time
helpful for the processing of the tasks, but might be
misunderstood as target-oriented.
1. Key with two, but semantically similar modes. As an
example the ‘C’-button on the phone A is analysed
(cf. table 1). The key has two functions, making
corrections and going back to higher levels in menu
hierarchy. The data show that the key was used 19
times inefficiently in the first trial. In the second trial,
the key was not used inefficiently anymore, showing
that the mode was fully understood by the children.
2. Key with two, but semantically dissimilar modes. In
this analysis, a key is selected that exerts its function
only at one specific level having, however, no
function at all other levels. It concerns the green
receiver key of both Siemens models (cf. table 1). It is
centrally positioned on phone C; on phone B it is
placed laterally. In the first trial, the green-receiver
key is inefficiently used 34 times with phone B and
still more often, namely 49 times with phone C. Does
the misuse drop in the second trial? With phone B,
this was the case. Compared to the first trial, the
children used this key 50% less inefficiently (17
times). With phone C, no learnability was present;
rather the opposite, the green-receiver key was used
even more often than in the first trial (50 times). How
can this result be explained? Two keys with exactly
the same mode lead to a very different performance
and learnability. The meaningful difference between
them is supposedly their spatial position on the
keypad. In contrast to phone B, the key of phone C
is designed in an eye-catching way, being centrally
positioned (compared to the lateral position of the
key in phone B). Thus it can be assumed that its
position was responsible for the frequent misuse,
presumably misinterpreted by the children as having
the function of ‘enter’ or ‘confirmation’.
3. Keys with many and semantically dissimilar modes.
Next, mode keys exerting many functions and,
especially, having dissimilar modes are of interest.
In phone C, the softkeys (rocker switches) have
definitively more key options in general and more
semantically dissimilar functions compared to phone
B. If having semantically dissimilar modes is
especially cognitively demanding as proposed by
model 3, then the frequency of inefficient use of these
keys in phone C should clearly exceed those with
phone B. The analysis showed that this was indeed
the case. In the first trial, the softkeys were used 25
times more inefficiently with phone B. With phone C
however, the keys were even used 92 times in the first
trial. Looking at the second trial, where children
should have learned the specific meaning of the keys,
it was found that these keys were definitively hard to
understand. Even if there was a general decrease in
the usage of the keys, they were still used 16 times
with phone B and 72 times with phone in the second
trial. Thus, it can be concluded that keys with many
and especially semantically dissimilar modes cause a
high cognitive load that is still present when the
children have had some time to learn the functioning
of the keys.
4. As examples for keys never contributing to the
solution of the task, but possibly indicating the
children to run out of ideas how to proceed
constructively, the frequency of using the keys ‘#’
and ‘*’ was enumerated. Both keys are in the keypad
of the mobile phone, to the left and right side,
flanking the ‘0’ in the lowest row. In figure 5, the
frequency of using these two keys is illustrated for
the first and the second trial in all three key
solutions.
As can be seen, the different complexities of the key
solution can even be seen in the usage of these two
irrelevant keys. In the first run, hash and asterisk were
used eight times with phone A, and 23 times with
On keys’ meanings and modes: children’s efficiency using a mobile phone
Figure 5. Effects of key complexity on the number using #
and * as inefficient keys in the first and the second trial.
phone B. The highest number of inefficiently used keys
was found for key solution of phone C (30 times). In
the second run, the children generally showed a
distinctly lower frequency of using these keys: with
phone A, these keys were not used anymore and with
phone B only three times, hinting at a learning process.
With phone C however, these two keys were still used
20 times. Apparently, even if the usage of these keys
should not be linked to the complexity of the key
structure itself, as these keys are given in any phone,
they rather reflect the difficulty the children experienced using the mobile phones, possibly in the sense of
a more general searching-for-help pattern.
6.3.4 Correlations between the dependent variables. Now, for
the full understanding of the outcomes and the identification
of potential predictor variables, it might be insightful to look
at possible interrelations between the children’s self-reported
experience with technology (assessed in pre-experimental
screenings) and the performance outcomes on the one hand
as well as the degree of LOC on the other hand. Thus,
correlations, for ordinal data Spearman-Rho and for
nominal data Kendall-Tau-values, are reported. The degree
of LOC was significantly correlated with all performance
measures (number of inefficient keystrokes: r ¼ 70.47;
p 5 0.01; number of tasks solved: r ¼ 0.5 (p 5 0.01; time on
task: r ¼ 70.36; p 5 0.05), showing that children with high
LOC values performed distinctly better. Having a mobile
phone (r ¼ 70.31; p 5 0.01), the frequency of using it
(r ¼ 70.32; p 5 0.01), and the reported ease handling a
mobile phone (r ¼ 70.45; p 5 0.01) were further factors that
were revealed to be significantly correlated with the degree of
425
LOC. But not only was mobile phone possession, the
frequency and ease of its usage interrelated with the degree
of LOC, but also the frequency of using a PC (r ¼ 70.39;
p 5 0.01) and a VCR (r ¼ 70.35; p 5 0.01), as well as the
reported ease of using the VCR. The reported general interest
in technology was neither significantly correlated with LOC,
nor with the frequency of using technical devices, as a mobile
phone, a VCR, or a fax copier, even if one would have
expected that. Rather, the LOC degree was correlated with
the estimated ease using a PC (r ¼ 70.33; p 5 0.05). Even if
the LOC values of girls were significantly lower than boys’
LOC values (r ¼ 70.38; p 5 0.01), gender showed no
correlation with the number of inefficiently used keys (n.s.)
and time on task (n.s.), but for the number of correctly solved
tasks (r ¼ 70.28; p 5 0.1). The last finding may be interpreted such that the degree of LOC worked as a motivational
regulator for task success. Thus, concluding the correlation
outcomes, children’s LOC values reflect the general degree of
the reported experience with technological devices and the
reported ease using them.
7. Experiment 2: young adults using different key solutions
in mobile phones
To validate the results found in Experiment 1 with children
as participants, the study was replicated with 45 university
students. They can be considered highly experienced in the
use of technical devices in general and should represent the
optimal user. If there is an impact of the different cognitive
complexity of the key solutions on the performance in this
user group, a high generalisability of the results can be
assumed.
7.1 Participants
The age of the 45 participants ranged between 19 and 33
years (M ¼ 22.6). Thirteen were male and 22 female,
distributed equally to the three experimental conditions.
The LOC did not differ significantly in the three
conditions (F (2.44) ¼ 1.93; n.s.) with values of 67.6 in
phone A group, 76.9 in phone B condition and 76.9 in the
phone C group. While 32 of the participants reported
possessing a mobile phone of their own, 13 actually did
not. The 13 novice users were also distributed equally to
the three experimental groups. In phone A and phone C
conditions there were four novices each and five novices
used phone B to process the tasks. Through nonparametric Kruskal-Wallis tests it was made sure that
the three groups also did not differ with regard to their
experience and their judged ease of using different
technical devices. The only significant difference was
found with regard to the functions of their mobile phone,
which the participants reported to use (w2(2) ¼ 5.3;
p 5 0.1). Participants in phone C group reported on using
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more sophisticated functions than just making calls and
sending text messages on average (M ¼ 5), whereas phone
A group reported on only using these two functions
(M ¼ 3.9) and the phone B group ranged in between
(M ¼ 4.6). Overall, the participants reported using their
mobile phone mostly daily or once or twice a week
(M ¼ 2.5) and rated the use as rather easy (M ¼ 1.7). The
PC was used between several times a day and daily by this
group (M ¼ 1.4) and the use was also estimated as rather
easy (M ¼ 1.8).
7.2 Procedure
As in Experiment 1, participants first answered the
questions regarding their previous experiences with technical devices and completed the questionnaire assessing their
LOC interacting with technology. According to the results
in this questionnaire users were divided into two groups by
median split. Then, they processed the same four tasks with
the children twice consecutively, having a maximum of five
minutes per task.
7.3 Results
In a MANOVA performance, outcomes with regard to the
number of tasks solved, the time needed to process the eight
tasks and the inefficient keystrokes carried out in this
process were calculated. Independent variables were the
three key solutions and users’ LOC interacting with technology. A significant main effect was found for the key
solution (F (6.76) ¼ 2.3; p 5 0.05), to be seen in figure 6.
As visualised in figure 6, participants using phone C
solved only 7.4 of the eight tasks, whereas phone A and
phone B groups solved, on average, 7.9 tasks. Regarding
the time needed, phone A showed the best performance
with 430.3 s and phone C again the worst with 587.8 s. The
performance in phone B group ranged in between with
459.7 s. With respect to the inefficient keystrokes, the
largest performance difference between the groups was
found with an average 6.1 inefficient keystrokes in the
phone C group, 3.1 inefficient keystrokes in phone B and
only 0.2 inefficient keystrokes in phone A. When looking at
the single F-tests, statistical significance was reached for the
number of tasks solved (F (2.39) ¼ 2.81; p 5 0.1) and for
the number of inefficient keystrokes (F (2.39) ¼ 5.59;
p 5 0.01). The main effect of LOC did not have a
significant influence on users’ performance. Low LOC
participants accomplished 7.5 tasks in 534.6 s and made 4.4
inefficient keystrokes. In contrast, the high LOC group
solved 7.9 tasks in 452.5 s and made 1.9 inefficient
keystrokes. The interaction between key complexity and
LOC did also not reach statistical significance.
Even though young adults compared to the children
of Experiment 1 pressed fewer keys inefficiently, it is
worth looking at which specific keys caused most trouble.
Again, three examples of keys differing in the sense that they
have semantically similar or dissimilar modes are looked at.
7.3.1 Key level
1. Key with two but semantically similar functions: The
c-key of phone A one, which is used to return to
higher levels in the menu hierarchy and to correct
was never used inefficiently by the students.
2. Key with one but inconsistent function: The green
receiver key of the two Siemens phones, which is
used to effect calls, but does not have any function at
other points in the menu was used 18 times in total
when using phone C and seven times in total when
using phone B.
Figure 6. Effects of key complexity on the number of inefficiently used keys (left), the number of correctly solved tasks
(centre) and the time on task (right) for the young adults group (N ¼ 45).
On keys’ meanings and modes: children’s efficiency using a mobile phone
3. Keys with many and semantically dissimilar modes:
The softkeys of the two Siemens key solutions exert
many different functions depending on the point of
the menu. The phone C keys, having many
semantically dissimilar functions were hit 68 times
in total and phone B softkeys with fewer incongruent
functions were used inefficiently 22 times.
4. Keys not helpful for tasks solution: The students did
not use the hash and asterisk keys.
7.3.2 Correlations between the dependent variables. In order
to estimate what kind of relationship exists between the
LOC of a user interacting with technology and other
experiences with technical devices, correlations between
LOC and users’ answers to questions concerning their
technical expertise were correlated (Spearman-Rho for
ordinal and Kendall-Tau-b values for nominal data). The
highest correlation of r ¼ 7.57 (p 5 0.01) was found
between LOC and the estimated ease using a PC, indicating
that the higher participants’ LOC, the easier they find using
a PC. The second highest correlation showed that the
higher students’ LOC, the higher the students’ estimated
interest in new technologies was (r ¼ 0.53; p 5 0.01).
Furthermore, the higher participants’ feeling of having
control over technical devices, the higher is their frequency
of using a PC (r ¼ 0.52; p 5 .01) as well as a videocassette
recorder (r ¼ 0.30; p 5 0.05). Between LOC and the
frequency of using a mobile phone (r ¼ 0.12; n.s.) and the
estimated ease of using one (r ¼ 70.13; n.s.), no significant
correlations were found nor with users’ mobile phone
expertise in terms of possession of a phone (r ¼ 0.00; n.s.) or
the number of different functions used (r ¼ 0.01; n.s.).
For further insight into the correlates of LOC interacting
with technology, interrelations of age and gender as well as
427
performance interacting with the mobile phone were
calculated. There was indeed a significant correlation of
r ¼ 70.38 (p 5 0.01) between LOC and gender, indicating
that male participants showed higher values in internal
LOC and participants showed a somewhat higher LOC, the
older they were (r ¼ 0.20; p 5 0.1). With regard to the LOC
and performance interacting with the mobile phone, there
was only a rather small correlation with the number of
tasks solved (r ¼ 0.27; p 5 0.1), but insignificant correlations with time on task (r ¼ 70.20; n.s.) and inefficient
keystrokes (r ¼ 70.07; n.s.). Gender correlated by r ¼ 0.25
(p 5 0.1) with time on task (women needed slightly more
time), but not with any of the other performance measures.
8. Comparison of the predicted complexity and performance
outcomes for children and young adults
According to the three models proposed in section 4,
differences in cognitive complexity of the three key
solutions and relative decreases in users’ performance were
predicted (figure 8, left side). Performance outcomes from
the two experiments are now contrasted for the number of
inefficiently used keys, as this was the most direct (different
key complexities were examined) and accordingly, the most
sensitive measure. In figure 7, the prediction (figure 7, left
side) and the empirical values for children’s (figure 7,
centre) and young adults’ (figure 7, right side) is shown
(both user groups appear in different graphs, as the scaling
of the axes was not compatible). The key solution with the
lowest predicted complexity (phone A) is always set as the
baseline and the higher complexity of the other solutions is
calculated as increase compared to the baseline.
From figure 7 illustrating the increases with respect to
the number of inefficient keystrokes, it can be seen that
Figure 7. Comparison of the predicted and the empirical outcomes: Number of inefficiently used keys in the three key
complexities in children (left side) and young adults (right side).
428
M. Ziefle et al.
performance decreased dramatically in phone B and phone
C. This was predicted in all models (cf. table 2 and figure
1). However, model three, especially stressing the importance of dissimilar modes, proved to be the best predictive
approach, even though the empirical results distinctly
outreached the predictions. It was assumed that phone B
would be about 167% more complex than phone A and
phone C, even 244% more complex. Young adults, though,
made 1450% more keystrokes with phone B and 2950%
with phone C. Children had an increase in the number of
inefficient keystrokes of 103% with phone B and even
335% with phone C. Admittedly, the absolute percentages
are of course ‘virtual’, as the absolute weight of each of
the factors assigned to in the models was hypothetical
and on the other hand, they depend on the initial value
of the phone A condition (since this is the basis the
increases were related to). The model three, however, and
this is the central outcome, had the best fit with the data,
which means that the assumption of model three,
that dissimilar modes add considerably more to complexity
of a key solution than only the number of key options
or modes, was shown to be true, and not only for
children but also for young adults, who are bright and
technology-prone.
9. Discussion
The question of how to design control keys for mobile
phones in order to be easily usable was addressed by two
experiments with children and young adults as user groups.
The 36 children and 45 young adults solved four common
tasks with simulated mobile phones that had an identical
menu, but differed in the complexity of keys to be used
when handling the phones. As effects of keys’ complexity
were isolated from the complexity of a menu, the difficulty
using the mobile phone can be interpreted solely as a
function of the different implementations of the control
keys. The outcomes draw a clear and insightful picture and
are now discussed with respect to their implications for the
design of mobile phones.
9.1 Effects of keys’ complexity
As was shown in the two experiments, the complexity of
keys is indeed a critical factor affecting the efficiency using a
mobile phone for a broad user group. Primarily, key
solutions having modes with semantically incongruent
functions were shown to be responsible for user’s inefficiency using the keys correctly. This is taken from the
highest number of inefficient keystrokes found in the phone
C key solution, which possessed the highest number of
semantically inconsistent functions per key. Even if the
absolute number of inefficiently used keys was different in
the two user groups with children using the keys more often
than younger adults did, child users made 4.4 times and
young adults 30 times as many inefficient keystrokes with
phone C keys than with phone A keys, the ones with the
lowest complexity in both, number of key options and
modes, with none of the modes semantically dissimilar. The
mere number of key options is only of secondary
importance, as phone B (comparable number of keys in
phone but less keys with semantically inconsistent functions) performed distinctly better. Thus, it can be concluded
here, that the keys’ complexity aggravates users’ effectiveness and efficiency, mainly due to the meanings of keys: if a
single key has many meanings, with contradicting semantics, the cognitive demand for users is unnecessarily high,
even in the second trial, where no learnability effects were
found, as was shown in the phone C condition. However,
the negative effects of complex key solutions were not
limited to the number of inefficiently used keys, but also
showed up in the time needed to process the tasks and the
number of successfully solved tasks. Children were 10%
more successful and 14% faster when using the phone A
keys than the phone C keys, with phone B phone ranging in
between. Similarly, the young adults solved 6.8% more
tasks correctly with phone A keys and were 37% faster
compared to the more complex phone C users. It is worth
mentioning that a key’s mode itself, when semantically
similar, did not deteriorate performance as strongly as for
example in the ‘C’ key of phone A exerting two functions
(corrections and returns in the menu), both meaning
‘undo’. Even if the children misused this mode key in the
first trial, they learned to use the key in the second time
where they did not use it incorrectly anymore. One of the
phones (phone C) had redundantly implemented functions.
According to the literature (e.g. Sanders and McCormick
1993), this should increase users’ performance as it provides
a higher chance to match the user’s expectations of how a
function is executed. As the children, though, did not detect
the additional functionalities at all (they never used the
arrow keys left and right, and only three of the young
adults did so in more than two tasks) these keys do not
have any identifiable effect on the usability of the phone.
A final remark concerns the two age groups examined
here. The results showed that the common prejudice of kids
being superior in all technical complexities is not true, at
least not in the most stringent form. If manufacturers (one
of the phone manufacturers expressed that in a personal
note to one of the authors) expect usability problems to
vanish into thin air, assuming that future users groups – as
they grow up with technology – will not have severe
difficulties using technical products, these hopes will not
easily be fulfilled. As demonstrated here, the still increasing
(and not decreasing) importance of usability concerns in
technical products should not be underestimated and
the ‘biggest barriers of mobile computing’ (York and
Pendharkar 2004) are still the ‘ergonomics and usability’ of
On keys’ meanings and modes: children’s efficiency using a mobile phone
429
a technical device. Thus, if interfaces are not well defined
and suboptimally designed, implying high cognitive demands on users, performance is very sure to be worse,
independently of any characteristics of the user group. This
finding confirms earlier results showing that suboptimal
mobile interfaces caused a lower performance than welldesigned ones, independent of users’ age and expertise
(Ziefle 2002b; Bay and Ziefle 2005; Ziefle and Bay 2005).
This is especially crucial as the complexity of the keys
present in some of the real devices was shown to be indeed
not necessary (with respect to demands of software
aspects), thus it is possible to implement a less complex one!
9.2 Effects of locus of control
The felt competence in using different technological devices is
another main factor predicting the ease using a mobile phone,
especially for children that are commonly assumed to handle
technical devices easily. The findings were indeed revealing as
nearly any study has been concerned with the effects of this
cognitive style in children when interacting with technology.
The results confirm earlier results (Bay and Ziefle 2003b;
Ziefle et al. 2004; Bremen and Ziefle submitted), showing that
the reported competence using technological devices interacts
with performance of adults. Briefly, two main points are
important. Firstly, 9 – 14-year olds reporting to have low
locus of control handling technology are sensitive enough to
estimate their own capability to handle mobiles, which means
they show considerably lower performance than the rest.
Secondly, children feeling low confidence using technology
had extreme trouble handling the complex phone C keys.
Children with low LOC values used the keys three times as
often inefficiently, solved on average one task less and were
26% slower when processing the tasks compared to children
with high LOC values. Young adults with low LOC values
were 5% less successful in task solving, needed 18% more
time on task and made twice as many inefficient keystrokes,
however, the LOC effects on performance were not found to
be statistically significant. In order to find out why, a closer
look is concerned with the absolute LOC values in both user
groups. In figure 8, the distributions of children’s LOC (left)
and young adults LOC (right) are visualised. As can be seen,
the distributions of children’s and adults’ LOC values are very
similar and therefore may not serve as an explanation of the
lack of significance in the interaction of the degree of LOC
and performance in the adult group.
Focusing on correlation outcomes, the reported frequency of using technical devices (especially the PC) and the
ease of using them was found to be interrelated with LOC
values, however, for children and for young adults. On the
basis of the present results the difference between the
importance of the LOC concept for both user groups cannot
be finally determined. However, one might speculate that
the young adults examined here, being all university
Figure 8. Distribution of the LOC values [max ¼ 100] in the
children group (left) and the young adults group (right).
students, might have compensated the LOC effects by their
general high-cognitive abilities. Thus, in future studies the
role of this cognitive style is to be studied in a less sapient
user group, better representing the average user confronted
with the complexity of technological devices.
9.3 Reviewing the models predicting keys’ cognitive
complexity
With the goal of defining the factors that contribute to
cognitive complexity of a key solution for a menu-driven
technical device and predicting users’ difficulties interacting
with it, we have defined three models in this paper. The first
model only included one factor, namely the number of
different options where a user can stroke the keys of the
device at each point of the task solution process. The
underlying idea of this very simple model is the fact that a
user has to make decisions concerning which of the keys s/he
wants to press in each step to reach a certain goal. The more
options are available, the more difficult the decision is.
This model leads to the prediction that the phone B keys
(eight options) will prove to be the most complex solution,
phone A (four options) the easiest and the phone C (seven
options) ranging in between these two. The second model
introduced – additionally to the number of key options –
two other factors: modes and redundancy. Modes are
supposed to increase complexity because the different
functions a key exerts need to be kept in mind, increasing
the user’s cognitive load. Considering the total number of
different functions each of the keys can exert and subtracting keys with redundant functions (which decrease cognitive
load), it was predicted that the phone C keys would be about
150% and the phone B 100% more complex than the keys of
phone A. The third model finally did not treat all kinds of
modes as equally difficult for the user, but differentiated
between semantically similar functions (basic modes) and
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M. Ziefle et al.
functions of a key that have completely different meanings
(dissimilar modes). Dissimilar modes are supposed to be
more cognitively demanding because they inhibit the
formation of action schemas and are therefore weighed by
the factor 2. This model predicted a 167% higher complexity of phone B compared to phone A and a 244% higher
complexity of phone C compared to phone B. The
performance data of our two experiments showed, in terms
of inefficient keystrokes, increases in complexity with phone
B of 103 % (children) and 1450% (students) compared to
phone A. In the phone C condition increases were around
335% (children) and 2950% (students) in relation to
baseline (phone A). Thus, the model that predicts the
empirical data best, is model 3, considering key options,
modes and meanings. This model can be regarded as a good
approach for designers of key solutions on small menudriven devices who need to estimate how many difficulties
users will have when interacting with the keys and it will
help avoiding high cognitive complexity. However, the
impact of keys’ meanings and modes on the complexity of a
key solution seems to be underestimated, considering the
fact that in both user groups the decrease in performance
from phone B to phone C in terms of inefficient keystrokes
was much higher than predicted. Especially semantically
dissimilar modes may have contributed to complexity,
because the phone C key solution has 37.5% more dissimilar
modes than phone B and only 11% more modes in general,
which may, in part, explain the big difference in performance between phone B and the phone C.
Which other factors may have added to complexity and
should be incorporated into a model? Concerning the
modes, no differentiation was made between keys that exert
one function at one point in the menu and no function at
other points, and those keys that exert semantically
dissimilar functions at different points of the menu. It can
be supposed that there is a difference in the cognitive
demands of those different situations. With regard to the
fact that users should be enabled to establish action
schemas for each of the keys, the allocation of semantically
dissimilar functions to one key should definitely be avoided.
Yet, whenever a key exerts its function only at very few
points in the menu, having no function at all the rest of the
time, it may be designed accordingly in terms of its
appearance (size, unambiguous icon) and spatial location,
as it was done with the green receiver key in phone B. This
leads to a second important point, the spatial location of
single keys within the other keys. The spatial allocation of
keys has to be chosen according to their importance. It was
shown in this study that two keys exerting exactly the same
function, but one being allocated centrally and one
laterally, lead to different frequency of misuse. As this
green receiver key has to be used only rarely (only when
effecting calls) whilst being centrally positioned, this leads
in combination with the green colour to a misinterpretation
as a confirmation key and therefore to many inappropriate
operations. A third important aspect that may explain the
great difficulties experienced by participants using the
phone C solution are the rocker switches, which at times
have two stroking options and at times exert only one
function. This confuses users in addition to the many
different semantic meanings these keys have, as was shown
by a comparison of the keys with the softkeys of phone B.
9.4 Limitations of the study
The present study represents a first attempt to propose and
evaluate a quantitative model predicting the cognitive
complexity of key solutions on small menu-driven devices,
which consists of several contributing factors. Our model,
comprising the number of key options, the modes and the
meanings of the keys was able to predict performance using
three different key solutions fairy well.
Nevertheless, many more studies are necessary to
evaluate the generalisability and further develop the model.
In the previous section we have already mentioned three
aspects that should be incorporated into the model: the
different characteristics of semantically dissimilar modes,
spatial locations of keys and double assignment of functions
of one key, even though quantifying their contribution to
complexity will not be easy. For reasons of ecological
validity we chose three key solutions of existing (and
widespread) mobile phone models. Unfortunately, the three
factors included in the model could not be experimentally
isolated from each other. In order to prove the generalisability of the model it will therefore have to be applied to a
number of other key solutions, as well as to other menus and
other devices. Furthermore, the fit of the model’s predictions with performance data of older adults will have to be
verified. As this user group is especially sensitive to bad
design, as was shown in a number of studies (e.g. Ziefle and
Bay 2005), this should prove to be very insightful.
9.5 Recommendations for the design of mobile phones’ keys
From the results of the two studies illustrated in this article,
a number of recommendations for the design of control
keys of mobile phones can be proposed:
1. Whenever modes cannot be avoided due to the
limited space available on the device, semantically
similar functions should be allocated to a key. This
should enable the user to build up action schemas,
which can be triggered autonomously, decreasing
cognitive load.
2. Keys that exert their function only at very few points
of the menu should be avoided or placed laterally on
the device, unambiguously indicating its functions. If
the very specific function of a key is not transparent,
On keys’ meanings and modes: children’s efficiency using a mobile phone
users may think it is broken, when it does not lead to
any visible changes in the display.
3. The alternating allocation of one and two functions
at a time to one and the same key should definitively
be avoided. The user does not know where to press
the key and makes many mistakes.
9.6 Research in progress
Current research regarding the usability of navigation key
solutions deals with transferring the effects found here to
older adults as well as to devices with different types of
menus. This will help to generalise the logic applied here to
predict the complexity of keys to other devices, such as
navigation systems, photocopying machines, digital cameras, wrist watches – basically all devices disposing of a
hierarchical menu structure that has to be operated with a
small number of keys.
Acknowledgements
We would like to acknowledge the valuable contribution of
Alexander Schwade, who collected and analysed the
children data. Sadly, he passed away before this paper
was concluded. His inspiration will never be forgotten.
Moreover, we thank Philipp Brauner for programming the
software for displaying the mobile phones and logging user
actions as well as Sarah Hatfield, Preethy Pappachan and
Judith Strenk for their research support. A final thanks is
devoted to two anonymous reviewers for their constructive
comments on an earlier version of this manuscript.
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