Conquering the Mobile Device Jungle:

Towards a Taxonomy for App-enabled Devices

Christoph Rieger

and Tim A. Majchrzak

ERCIS, University of M

unster, M

unster, Germany

ERCIS, University of Agder, Kristiansand, Norway

Keywords:

App, Mobile App, Taxonomy, Categorization, Smart Devices, Wearable, Smartphone, Tablet.

Abstract:

Applications for mobile devices (apps) have created an ecosystem that facilitated a trend towards task-oriented,

interoperable software. Following smartphones and tablets, many further kinds of devices became (and still

become) app-enabled. Examples for this trend are smart TVs and cars. Additionally, new types of devices

have appeared, such as Wearables. App-enabled devices typically share some characteristics, and many ways

exist to develop for them. So far for smartphones and tablets alone, issues such as device fragmentation are

discussed and technology for cross-platform development is scrutinized. Increasingly, app-enabled devices

appear to be a jungle: It becomes harder to keep the overview, to distinguish and categorize devices, and to

investigate similarities and differences. We, thus, set out with this position paper to close this gap. In our view,

a taxonomy for app-enabled devices is required. This paper presents the ﬁrst steps towards this taxonomy and

thereby invites for discussion.

1 INTRODUCTION

The continuous growth of the mobile device market

(Statista Inc., 2016) and the recent emergence of de-

vices such as smartwatches (Chuah et al., 2016) and

connected vehicles (Coppola and Morisio, 2016) has

attracted much attention from academia and industry.

In the past decade, particularly the app ecosystem fa-

cilitated a trend towards task-oriented, interoperable

software, arguably started with the advent of Apple’s

iPhone in 2007 (Apple Inc., 2007) and the App Store

in 2008 (Apple Inc., 2008). For traditional mobile

devices (i.e. smartphones and tablets), the competi-

tion has yielded two major platforms (Android and

iOS). Several approaches for cross-platform devel-

opment have been proposed to avoid the costly re-

development of the same app for different platforms

(cf., e.g., Heitk

otter, Hanschke, & Majchrzak, 2013;

El-Kassas, Abdullah, Yousef, & Wahba, 2015).

Technological development has continued and

many new device types have emerged. Most of them

fall under the umbrella term mobile devices and are

more-or-less app-enabled. The latter denotes that it

typically are apps that make such devices particularly

useful and that extend the possibilities they offer. How-

ever, they differ greatly in intended use, capabilities,

input possibilities, computational power, and versatil-

ity, to name just a few aspects. Smart devices such as

smart watches and smart TVs are most prominent in

the realm of consumer devices but plenty of possibili-

ties exist. Lines towards sensor-driven devices for the

Internet of Things (IoT) are often blurred and it is not

always clear how to properly categorize a device. This

makes it hard to discuss, or, actually, to even correctly

name them. Being usually app-enabled, these devices

provide new opportunities for intelligent and context-

adaptive software but at the same time pose technical

challenges regarding the development for new plat-

forms and regarding heterogeneous hardware features.

Moreover, app-enablement does not necessarily bring

compatibility and portability whereas running the same

app on a variety of devices is normally desirable.

While a plethora of case studies and contributions

for individual device types such as smartphones and

tablets can be found in the scientiﬁc literature (e.g.,

(Heitk

otter et al., 2013; Jones and Jia, 2014; Chauhan

et al., 2014; Busold et al., 2015; Dagef

orde et al.,

2016)), a comprehensive study of the general ﬁeld of

app-enabled devices is missing. This paper sets out

to close this gap by contributing a taxonomy for app-

enabled consumer devices. While we have put much

effort into literature work (cf. the next section), the

useful literature base is small. Therefore, we propose

research-in-progress, arguing for our ideas to stimulate

332

Rieger, C. and Majchrzak, T.

Conquering the Mobile Device Jungle: Towards a Taxonomy for App-enabled Devices.

DOI: 10.5220/0006353003320339

In Proceedings of the 13th International Conference on Web Information Systems and Technologies (WEBIST 2017), pages 332-339

ISBN: 978-989-758-246-2

work on this topic. The eventual aim is the discussion

on a conference scale for extension into a state-of-the

art paper, possibly further leading to a kind of standard.

The structure of this paper is as follows: After

discussing related work in Section 2, our proposal

for a taxonomy is presented in Section 3. Section 4

discusses the taxonomy with regard to its current and

future applicability before we conclude in Section 5.

2 RELATED WORK

For the topic of our paper on the one hand a plethora

of related work exists, on the other hand hardly any

closely-related approaches can be cited. Particularly

since Apple’s iPhone initiated the growth of the smart-

phone device class, many papers have been published

on the modern notion of mobile computing, centring

around devices that are propelled by apps. However,

even overview papers typically focus on one category

of devices. For example, Jesdabodi and Maalej (2015)

classify apps by usage states but limit themselves to

smartphones. Moreover, the scientiﬁc literature so

far has only rudimentarily captured the latest devel-

opments in device development. Chauhan, Senevi-

ratne, Kaafar, Mahanti, and Seneviratne (2016), for

instance, provide an overview of smartwatch app mar-

kets with focus on the type of apps as well as privacy

risks through third party trackers.

To make sure that we do not miss an existing tax-

onomy (or similar work), we did an extensive liter-

ature search. We focus on work from 2012 or later,

where the ﬁrst broader range of smart watches such

as the Pebble had already been presented. Together

with the increasing variety in devices, new operating

systems have appeared since then. Examples are An-

droid Wear and watchOS, which focus on wearable

devices (Google Inc., 2016; Apple Inc., 2016) as well

as webOS and Tizen, which address a wider range

of smart devices (LG Electronics, 2016; The Linux

Foundation, 2016).

We deliberately excluded the keywords application

and system. The ﬁrst yielded many results that were

not applicable since the term was mostly used to mean

utilization of something. The latter had originally been

used to describe e.g. cyber-physical systems but now

proved to be too generic. Also, the medical area was

excluded as these papers focus on apps for therapeutic

purposes and do not contribute to the question of app-

enabled devices. We thus used the following search

string in the Scopus database:

TITLE-ABS-KEY(

(app-enabled OR app OR app-based)

AND

(mobile OR smart OR intelligent OR portable)

AND

(device OR vehicle OR ”cyber-physical system”

OR CPS OR gadget)

AND

(classiﬁcation OR categorization OR overview

OR comparison OR review OR survey OR frame-

work OR model OR landscape OR ”status quo”

OR taxonomy))

AND PUBYEAR AFT 2011

AND ( EXCLUDE ( SUBJAREA , ”MEDI” ) )

A search on 07-12-2016 yielded 998 results. Of

these, not a single paper provided an approach for clas-

siﬁcation, let alone a complete taxonomy. Only one

paper (Koren and Klamma, 2016) went beyond a per-

spective on “classical” mobile devices and considered

heterogeneous device types. To complicate matters,

papers mention that there are other smart devices than

smartphones but do not go into detail.

In summary, the result set reveals no closely related

work to which we can limit ourselves to. However, we

can draw from a myriad of sources that tackle some

aspects that are relevant for a taxonomy of app-enabled

devices. This ﬁnding aligns with the motivation for our

paper. Obviously, other authors struggled with putting

different device categories into context because no

proper framing exists.

Although not necessarily focused on multiple de-

vice categories, work on cross-platform app develop-

ment is conceptually related. Usually, cross-platform

development exclusively targets traditional mobile de-

vices such as smartphones and tablets, e.g. “the diver-

sity in smart-devices (i.e. smartphones and tablets) and

in their hardware features; such as screen-resolution,

processing power, etc.” (Humayoun et al., 2013). How-

ever, considering the differences in platforms, versions,

and also at least partly in the hardware is similar to

considering a different type of device. In fact, the dif-

ference in screen size between some Wearables and

smartphones with small screens is less profound than

between the same smartphones and tablets. Therefore,

comparisons that target cross-platform app develop-

ment have paved the way towards this paper. This

particularly applies to such works that include an in-

depth discussion of criteria, such as by Heitk

otter et

al. (2013), Sommer and Krusche (2013), Dalmasso,

Datta, Bonnet, and Nikaein (2013), and Rieger and

Majchrzak (2016).

A part of the difﬁculty with related work is the

term app-enabled by itself. While it is often said that

devices are enabled by apps, or that apps facilitate

Conquering the Mobile Device Jungle: Towards a Taxonomy for App-enabled Devices

333

their functionality, it is usually not explained what this

exactly means. The typical usage that we also follow

is to denote an app-enabled device as one that by its

hardware and basic software (such as the operation

system or platform) alone provides far less versatility

than it is able to offer in combination with additional

applications. Such apps are not (all) pre-installed and

predominantly provided by third party developers unre-

lated to the hardware vendor or platform manufacturer;

moreover, the possibilities provided by apps typically

increase over time after a device has been introduced.

While this still is no profound deﬁnition, it provides a

demarcation for the time being. In particular, it rules

out pure Internet-of-Things devices as well as compu-

tational equipment that only is occasionally ﬁrmware-

updated or that is not built for regular interaction with

human users.

3 TAXONOMY OF

APP-ENABLED DEVICES

Categorizing app-enabled devices is difﬁcult due to the

variety of possible hardware features across all types

of devices and the heterogeneity of device capabilities

within each class. Any simple solution is prone to

not sufﬁciently discriminate. For example, processing

power does not differ a lot between smartphones and

tablets anymore, and microphones are no distinguish-

ing feature for voice-controlled devices. In addition,

the fast-paced technological progress manifests as a

constant stream of new devices, partly rendering previ-

ous devices obsolete. Moreover, device types converge,

illustrated e.g. by the phablet phenomenon (devices

that fall in between smartphones and tablets). Mo-

bility in the strict sense even is no exclusive feature;

smart TVs for example are not really mobile. Cars

with smart entertainment systems or even self-driving

features might be app-enabled, but it can be disputed

whether the whole car is the device and thereby the

device is actually mobile by itself. As a result, a tax-

onomy of app-enabled devices mandates a more open

categorization along several dimensions, allowing for

partial overlaps and future additions. In the follow-

ing, we present steps towards such a taxonomy. First,

the three chosen dimensions are discussed as static

structure for classiﬁcation. Subsequently, current and

foreseeable future device classes are positioned accord-

ing to this matrix.

3.1 Dimensions of the Taxonomy

This work positions app-enabled devices with regard

to the three dimensions media richness of inputs, me-

dia richness of outputs, and the degree of mobility.

Instead of enumerating concrete technologies that are

available today or in future, each dimension should

rather be regarded as continuously increasing intensity

and ﬂexibility of the particular capability, with sev-

eral exemplary cornerstones depicted in the following.

This approach not only provides the highest degree

of objectivity but also should keep the taxonomy ﬂex-

ible enough to capture future developments without

actually changing the dimensions.

Media richness of inputs describes the characteris-

tic user input interface for the respective device class.

•

None refers to fully automated data input through

sensors.

•

Buttons, including switches and dials, are (physi-

cally) located at the device itself and provide lim-

ited input capabilities.

•

Remote controls, including also joysticks and

gamepads, refer to dedicated devices that are teth-

ered or wirelessly connected to the app-enabled

device.

•

Keyboards are also dedicated devices to control

the target devices, but with more ﬂexible input

capabilities due to a variety of keys. Input still is

discrete.

•

Pointing devices refer to all dedicated devices to

freely navigate and manipulate the (mostly graphi-

cal) user interface, for example mouse, stylus, and

graphic tablet. While these devices technically still

provide discrete input, the perception of input is

continuous.

•

Touch adds advanced input capabilities on the de-

vice itself, allowing for more complex interactions

such as swipe and multi-touch gestures.

•

Voice-based devices are not bound to tangible input

units but can be controlled without haptic contact.

•

Gestures allow for a hands-free visual user interac-

tion with the device, for example using gloves or

motion sensing.

•

Neural interfaces are the richest form of user in-

puts in the future by directly tapping into the brain

or nervous system of the human operator.

As second dimension, media richness of outputs de-

scribes the main output mechanisms for the respective

device class.

Strictly, most if not all input is done via sensors, but

none at this point denotes no manual activity by a user.

Since the possibilities of neural interfaces are yet very

limited, future developments might mandate splitting up this

category into different kinds of neural interfaces.

WEBIST 2017 - 13th International Conference on Web Information Systems and Technologies

334

•

None refers to no user-oriented communication by

the device itself. This applies to cyber-physical

actuators with direct manipulation of real-world

objects (e.g. switching on light). This also in-

cludes pass-through mechanisms that in general or

in some situations do not produce a tangible output

of their own but pass it through to a connected man-

aging device (e.g., a smartphone) which retrieves

information and handles user output.

•

Screen output is a major form of user communi-

cation found in app-enabled devices. Although a

clear subdivision is not possible, several classes are

typical, ranging from tiny screen displays (

3”)

to small screens such as for smartphones (

6”),

medium screens for handheld devices (

11”),

large screens (

≤

20”), and usually permanently

installed huge screens >20”.

•

Voice-based output refers to the ﬁrst type of disem-

bodied device output that communicates with the

user without physical contact.

•

Projection extends the disembodiment with visual

output to a device-external location, including aug-

mented reality applications and hologram repre-

sentations.

•

Neural interfaces connect directly to the user in or-

der to a achieve a tightly coupled human-computer

interaction.

Finally, the combination of input and output character-

istics ignores different application areas of the respec-

tive device class. For example, intelligent switches and

drones for aerial photography can both be remotely

controlled and have no direct output, but can hardly be

grouped as being in the same device class. Therefore,

the degree of mobility describes the usage characteris-

tics as third dimension.

•

Stationary devices are permanently installed and

have no mobile characteristics during use.

• Mobile devices can be carried to the place of use.

•

Wearable devices are designed for a more exten-

sive usage and availability through the physical

contact with the user. In contrast to “mobile”, trans-

porting the device is implicit and usually hands-

free.

•

Self-moving devices provide the capability to move

themselves (controlled by the user). Ultimately,

autonomous devices represent the richest form of

mobility for app-enabled devices.

3.2 Categorizing the Device Landscape

The proposed dimensions allow for an initial cate-

gorization of the device landscape. Figures 1 to 3

(page 5) visualize the three-dimensional categoriza-

tion of different device classes using the respective

two-dimensional projections for better readability.

As depicted in Figure 1, many devices classes

can be assigned to distinct positions in the two-

dimensional space of input/ output media richness.

However, it should be noted that the ellipses represent

(current) major interaction mechanisms within the de-

vice class. For example, smartphones also have a few

physical buttons but are usually operated by touch

input. Individual devices may also deviate from the

presented position, for instance specialized or experi-

mental devices that do not (yet?) constitute a distinct

class of devices. Additionally, not all devices falling

into a device class must necessarily implement all pos-

sibilities of that class. Therefore, ellipses are a well-

suited representation as opposed to, e.g., the maximum

value for the respective devices.

The chosen level of abstraction implies that the

taxonomy dimensions are intended to be rather static.

Instead of chasing the actual technological develop-

ment to reﬂect the latest emergence of devices, only

seldom and slow changes are necessary to keep them

up to date. Nevertheless, the categorization of classes

is more dynamic and will need to be regularly checked

for continued relevance. Moreover, classes might need

to be split or at least be adapted regarding their place-

ment on the dimensions’ continuum when new possi-

bilities arise. For this reason, and also for the brevity

of a position paper such as this, we do not provide

detailed deﬁnitions for each class. Rather, we explain

them exemplarily and rely on the general understand-

ing of the well-known classes (such as smartphones).

Figure 1 reveals differences in the speciﬁcity (i.e.,

represented size) of the device classes. Some of them

ﬁll speciﬁc spots in the diagram, either due to techni-

cal restrictions (smart TVs evolve traditional remote-

controlled TVs with large screens) or special purposes

(smart glasses enable hands-free interaction and visu-

alization). Less speciﬁc device classes exist for two

reasons. On the one hand, terms such as smart home

comprise every technology that relates to a speciﬁc

domain, subsuming very heterogeneous devices. On

the other hand, underspeciﬁed device classes such as

implants and smart personal agents are presented as

they are due to their novelty; there are few devices

on the market and a high level of uncertainty must be

ascertained regarding future hardware characteristics

and interaction patterns. We expect to be able to draw

a more concrete picture with a future version of the

taxonomy, both based on the discussion of this paper

and the meanwhile ongoing technological progress.

Differences in the device classes can also be ex-

plained with regard to media richness theory (MRT).

Conquering the Mobile Device Jungle: Towards a Taxonomy for App-enabled Devices

335

Figure 1: Matrix of Input and Output Dimensions.

Figure 2: Matrix of Output and Mobility Dimensions.

MRT describes a corridor of effective communication

with matching levels of message ambiguity and media

richness (Daft et al., 1987). When applying this idea

to the input and output characteristics of app-enabled

devices, similar observations can be made. For ex-

ample, IoT devices have only rudimentary direct user

input possibilities but also give not much feedback in

return. Notebooks allow for medium levels of input

richness through keyboard and mouse input, with large

Figure 3: Matrix of Input and Mobility Dimensions.

screens as more ﬂexible output capabilities. Further-

more, smart glasses directly embed their output into

the real world by projection. Consequently, their voice-

based input is equally rich in order to handle complex

interactions with the user.

This correlation also partly explains why there are

areas in Figure 1 with no assigned device class. Rich

forms of user input such as gestures overcomplicate in-

teractions for devices that have just small screens. On

WEBIST 2017 - 13th International Conference on Web Information Systems and Technologies

336

the other extreme, devices with barely a few buttons

do not provide sufﬁciently ﬂexible input capabilities

to manipulate large screens. Of course, empty spaces

in the taxonomy might also be caused by a lack of

use cases so far. Thus, they might actually be ﬁlled

by future devices, or existing classes might “stretch”

into these areas. In general, with the evolution and

differentiation of input media, existing device classes

might extend towards further areas or even converge.

E.g., consider convertibles as hybrid devices between

keyboard-based netbooks and touch-optimized tablets.

Figure 2 depicts the combination of output me-

dia richness and mobility. Unsurprisingly, a general

tendency towards large screen output for stationary

devices can be observed. With increasing mobility,

screen sizes tend to diminish, from small screens on

smartphones to very limited ﬁtness tracker screens and

screen-less drones. Beyond mobile devices, output ca-

pabilities become more ﬂexible, potentially caused

by speciﬁc domains of application, or their novelty

with insufﬁcient time to establish wide-spread interac-

tion patterns. Some recently emerged device classes

explore intangible output capabilities, for instance aug-

mented / virtual reality (AR/VR) headsets. Others, es-

pecially autonomously moving devices such as smart

cars and personal robots, are driven by the increased

availability of sensor technology and not restricted to

particular output capabilities.

Finally, Figure 3 visualizes the relationship be-

tween input media richness and mobility. Usually,

an increasing degree of mobility entails less physical

input mechanisms with dedicated buttons and keys.

This might be attributed to practicability reasons, for

example using voice commands is easier for wear-

able smart glasses than requiring dedicated input de-

vices. In addition, smarter devices are usually more

complex with regard to their output, and equally so-

phisticated input capabilities are necessary to match

this level as explained by media richness theory. Con-

soles, for instance, provide basic navigation function-

alities. Desktop personal computers and notebooks

can be equipped with intelligent software such that

keyboard and mouse are helpful means for interac-

tion, and smart personal agents integrate advanced

interpretation mechanisms that allow for voice-based

communication in everyday situations.

4 DISCUSSION

Due to the rapid proliferation of the ﬁeld and the initial

character of this work, it is likely that amendments

will need to be made. Moreover, we will need to keep

updating the taxonomy once it has been acknowledged

by the scientiﬁc community. In addition, a taxonomy

should also be appealing for the use by practitioners,

particularly in a ﬁeld like Mobile Computing where

scientiﬁc research and technological progress go hand-

in-hand. Therefore, this section presents ideas for

discussion that go beyond Section 3.

4.1 Alternative Categorization Schemes

Devices can be categorized according to other device

features. Not all are compatible with our taxonomy,

nevertheless we deem several of them noteworthy.

Simple schemes such as a categorization by hard-

ware feature (e.g., camera, computing power, touch

screen) or usage (e.g., business, entertainment, sports,

or communication use) fail to provide clear criteria for

a taxonomy. In particular, a fast adaptation and con-

vergence of available technologies could be observed

in the past years. For example, so-called phablets blur

the lines between smartphones and tablets, and gyro-

scope sensors have found wide-spread adoption in a

variety of devices.

Matrix-based categorizations allow for a better jux-

taposition on two dimensions, for instance regarding

the input and output characteristics of app-enabled de-

vices. However, the heterogeneity of devices within a

device class provides insufﬁcient discriminating power.

For example, medium-sized and touch-based screens

are usual interfaces both for tablets and smart cars.

Similarly, distinguishing between apps for embedded

or stand-alone devices is not always possible due to

different types of device integrations within a device

category (cf. e.g. Coppola and Morisio (2016) for

smart cars).

Therefore, the third dimension chosen for our tax-

onomy adds the degree of mobility to distinguish be-

tween similar device hardware in different usage con-

texts. Other potential approaches for categorizing de-

vices include the degree of integration, automation,

or intelligence attainable or provided by the device.

This reaches from simple input / output devices with

limited app interaction (such as ﬁtness trackers), to in-

teroperable software (such as smartphones), highly

cross-linked and automated devices (in the IoT or

smart home ﬁeld), and ﬁnally to intelligent machines.

While we deem it reasonable to discuss such an op-

tional fourth dimension, we do not think the taxonomy

would gain more discriminatory power.

4.2 Further Development

Firstly, future discussion needs to include the demar-

cation of devices to be included. As argued earlier,

mobility is not necessarily the proper boundary. App-

Conquering the Mobile Device Jungle: Towards a Taxonomy for App-enabled Devices

337

enablement has proven to be feasible, yet we will need

to ﬁnd (or provide) a profound deﬁnition for it.

Secondly, it needs to be determined how the tax-

onomy can be kept up to date. In many other cases,

taxonomies have proven to be either too detailed and

thus requiring constant adjustments, or too little de-

tailed and thus lacking discriminatory power. Due

to a restriction to three orthogonal dimensions and

clearly distinguishable values in each of it, we are op-

timistic that the taxonomy will be future-proof. Nev-

ertheless, proper ways of deciding when adaptations

are needed and what developments can be reﬂected

without changes need to be deﬁned. As part of this, we

will need to scrutinize how to handle the differences in

precision regarding categories. For example, it is very

well understood what a smartphone is; smart homes,

and to an even higher degree neural devices are (yet)

diffuse with a lack of devices and/or applications to

characterize them.

Thirdly, we so far have limited ourselves to con-

sumer devices. This includes many devices that are

also used for professional purposes, but arguably not

all. Beyond that, some specialised devices are (so far)

solely used for professional means. Examples can be

found in industry, particularly in logistics. However,

some of these might simply be subsumed by consumer

devices. It could be said that e.g. the devices used by

parcel couriers are very similar to smartphones, despite

the difference in form and the absence of a general pur-

pose utilization. The same applies to special devices

from areas such as healthcare or crisis prevention and

response. While such devices typically have speciﬁc

capabilities (such as error-tolerance), on an abstract

level they again are very similar to general purpose

hardware. Thus, an updated taxonomy could try to

include non-consumer devices. However, due to the

complexity that arises particularly with devices that

are so specialised that information on them is scarce,

we deem the current limitation justiﬁed.

Fourthly, it should be scrutinized how the taxon-

omy can be provided in a form that is useful both for

researchers and for practitioners. Most scientists know

taxonomies for research topics enforced by publica-

tion outlets. Quite often these feel more like a “try to

ﬁt somewhere” game, particularly if a paper tackles a

contemporary topic and the taxonomy provides little

ﬂexibility. If we want our taxonomy to be helpful for

researchers, and – probably even hard to achieve – em-

ployed by practitioners, it needs to be easy to use yet

powerful. Achieving this will be very valuable, as can

e.g. be seen for cross-platform development, where

new approaches can be clearly categorized by their

characteristics.

The above discussion points have also shown the

limitations of our work. Besides the issues that need to

be worked on, an eventual veriﬁcation of the taxonomy

is mandated. For now, many tasks remain but a ﬁrst

– we deem noteworthy – step has been done. Further

steps are sketched in the next Section.

5 CONCLUSION AND OUTLOOK

In this paper we have presented a taxonomy for app-

enabled devices – it is the ﬁrst such work. Based

on three dimensions that take into account the input

and output characteristics, we have built the taxonomy.

Categorizing the device landscape and plotting the

results into ﬁgures illustrates the discriminatory power

of our taxonomy. However, as the ﬁrst comprehensive

work on the topic, we seek to amend and reﬁne it. For

now, it is put up for discussion. Nonetheless, due to the

soundness of the dimensions and their alignment with

availably theory, we are optimistic that this position

paper provides a profound step towards a systematic

overview of app-enabled devices.

Our future work instantaneously follows this pre-

condition. We seek to discuss the taxonomy as part

of a conference presentation and, subsequently, with

interested colleagues. Moreover, we will also seek

to have practitioners scrutinize it. The next step will

be to provide an amended version of the taxonomy,

along with an extended discussion of device classes.

Eventually, empirical veriﬁcation will be necessary.

The outlook is determined by an aspiration for our

work. It should not become “yet another computer

science taxonomy”. Rather, it should prove useful in

allowing

•

authors to more clearly express what kind of de-

vice(s) they are referring to,

•

researchers and practitioners to gain more discrimi-

natory power when speaking about modern mobile

computing devices, and

•

the general public a more straightforward way of

understanding similarities and differences between

devices, both technically and tangibly.

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