A CLASSIFICATION AND ASSESSMENT SCHEME FOR THE PROLIFERATION OF USER DATAWORKING PAPER

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A CLASSIFICATION AND ASSESSMENT SCHEME FOR
THE
PROLIFERATION OF USER DATA


WORKING PAPER




Karen Carey

School of Computing, Dublin City University, Glasnevin, Dublin 9, Ireland

careyk6@dcu.mail.ie


Markus Helfert

School of Computing, Dublin City
University, Glasnevin, Dublin 9, Ireland

markus.helfert@computing.dcu.ie


Abstract

Research shows a positive relationship between Information systems and business value is
inconclusive.
However executives are struggling to manage and measure the business value
from mobile devices.
Usage profiles facilitate the re
-
use of user experience intelligence by
providing a reference point for designing and evaluating mobile products and services.

I
n this
research

a Classification and Assessment Scheme

was

developed, and measured on selected
influencing factors

with the aim of examining mobile
device
usage
.
This Scheme classifies
human and physical factors.
The assessment of these factors

provide
s

user experience
intelligence, to build structured usage profiles. In this paper we justify the selected
independent variables and discuss how they can be assessed to validate the classification
scheme for future research.


Keywords:
Mobile
Devices,
B
usine
ss
V
alue, Usage
P
rofiles, Decision
M
aking, Information
Q
uality


1.

INTRODUCTION

Information Systems

(IS)

deployment has
undergone radical change. Traditional
deployment where the architecture, user
and access device are known at the time of
development has been replaced by more
diverse
situations (Foley 2011). According
to Avanade (2012), the majority

of
com
panies, (60%) say they are now
adapting their
Information Technology
(
IT
)

infrastructure to accommodate rather
than restrict
employees

in using personal
devices for work purposes
.

Although the
benefits of using personal mobile devices
are evident

the chang
e in deployment has
resulted in an on
-
going
struggle

for
executives

to optimise the way they
manage and measure the business value
from its usage.

Business value of mobile device usage can
be measured by usage profiles. Usage
profiles
can be
generated

from employees’
experience

and
produc
e

user experience
intelligence
.

T
his
can in turn form
a
reference point for designing and
evaluating mobile products and services.

The literature indicates

many external
factors that may influence mobile usage.
Therefor
e, to aid the proliferation of user
data, in this paper we define

a
C
lassification and
A
ssessment
S
cheme
(CAS), A
ppendix A.
The CAS
comprises

of both human and physical factors, which
may influence
Information Quality (IQ)
,
decision time and user satisfact
ion.

In
S
ection two
of this paper
a
brief
description of related work

is given
,
and
the
CAS
is
further explained.
S
ection three
describes the independent variables
(IV)
while the dependent variables (DV) are
described in
S
ection four.
We discuss the
limitations of the paper in section five and
s
e
ction
six
concludes the paper.

2.

BACKGROUND AND RELATED
WORK

The major emphases

in the
IS
communities

in the last decades centred

on
IT’s enhancement of organisational
performance (Banker & Kauffman 2004).

However, despite numerous studies
conducted
,

there is still a lack of good
quantitative measures for output and value
created by IT,
which makes justifying
investments in IT difficult (
Brynjolfsson,
1993)
. Kelley (1994)

purposes that
technology and produc
tivity connection is
elusive, largely because the aggregated
unit analysis at organizational level makes
it difficult to isolate the impact of
individual technology. Merely examining
the dollars invested in IT may not be an
accurate reflection of its effec
tiveness.

Few studies have captured the actual usage
of IT. Moreover, the extent of IT usage
may vary across industries, firms, or
processes.
Sarv Devaraj
&
Rajiv Kohli
(2003) co
nclude that the driver of IT
impact is not the investment in technology,
but
the actual usage of that technology.

Accordingly, when examining IT payoff it
is necessary to scrutinise

the impact of
individual technology usage on
organizational performance.
The
Technology
A
cceptance
M
odel (TAM)
was developed to predict individual
adop
tion and use of
I
T
. There has been
significant support in favour of TAM
(Adams et al., 1992; Agarwal &
Karahanna, 2000; Karahanna, Agarwal &
Angst 2006; Venkatesh et al 2003, 2007
).

Synthesizing prio
r research on TAM,
Venkatesh &

Bala

(
2008
),

developed a
theoretical framework
.

The
ir

framework
categorises “influencing factors” as
follows; individual differences, system
characteristics, social influence and
facilitating conditions.


Individual difference variables include
persona
lity and/or demo
graphics such as
traits

of individuals,
gender, and age

that
can influence individuals’ perceptions of
perceived usefulness and perceived ease of
use.

System c
haracteristics are the

features of a
system that can help individuals develop
favourable (or unf
avourable) perceptions
regarding the usefulness or ease of use of a
system.

Social influence
includes

various social
processes and mechanisms that guide
individuals to formulate perceptions of
various aspects of IT.

F
acilitating conditions represent
orga
nizational support that facilitates the
use of an IT. Based on this our
CAS
selects
the IV and DV as discussed
in the
sections

3 and 4
,

which aggregates user
data
and
develop
s

usage profiles.

3.

INDEPENDENT VARIABLES

The
aim
of this study
is
to develop usage
profiles of mobile devices
therefore,
our
main interest is “system characteristics”
,

and however
,

the scheme
must

also
distinguish the context. T
o structure the
concept of
context Schmidt et
.
al (199
8
)
,

proposed a model for context aware
mobile computing.

T
he top level of the
ir

context
model
distinguish
es

human factors and the
bottom level
,

the physical environment
.

The human factors include user, social
environment and task. The physical factors
include conditions, infrastructure and
loca
tion

(Schmidt et.
al 199
8
).

To structure
the context for examination
in this study
we group our
IV
into human factors
(
user
and the use case/task, application and
interaction methods, task complexity
)

and
the
physical factors
:

(information
structure, small

form factors).

We
acknowledge that these factors are
distinguished in the widest
sense;

however
,

they provide structure to the
context for
assessment.
The CAS

utilisation
is illustrated in Appendix A,
where IV and DV are
group
ed

for analysis.


3.1

Human
Factors

3.1.1
User and Use case

By understanding the user’s needs and
environment we enable developers to
provide a better user experience. Users’
work role create different ‘use
-
cases’.
However, from interviews with our
Enterprise partner, six employee us
er
segments with eleven sub
-
segments, and
190 mobile use cases were identified. To
examine and categorize

the ‘use
-
cases’ this
section is included in the
CAS
.

3.1.2


Software Application and
Interactions
M
ethods

User’s interaction with the device varies
according to task and therefore
,

can be
broken down for analysis.
Nikerson et.al.
(2009) developed
taxonomy

of mobile

applications consisting of seven
dimensions. These dimensions describe the
user’s interaction with applications for
tasks. An explanation
of the different
interaction methods are outlined in
Appendix B.

3.2

Physical Factors

3.2.1 Small

Form Factors

Small form factor is the har
dware
configurations of mobile devices (Hanson,
2011).
Examples

of form factors are

shape,

screen size, power, weight, connectivity,
and input methods.
Interviews with our
enterprise partner revealed that the number
of devices enabled had increased
dramatically from 176,937 in 2010 to
301,301 in 2011 and is expected to
increase further in 2012.

The iPhone, iPad
and Blackberry were the most
common
devices
enabled.
As a result the
measurements in our
CAS

(Appendix A)
are derived from these specific devices
it
can

be adapted to include other devices.

3.2.1.
(A)

Screen size and
R
esolution

Considerable research has been done on
the usability of small screens
(Dillon et.al
1990; Chae & Kim 2004; Acton et.al
2004
;

Chen & Chien, 2007; So & Chan,
2008; Findlater, 2009).

Oksman et.al
(2007),

found te
xt information
the

most
commonly used
informati
on
by mobile
users
when
compared to audio and visual
information
.

Consequently there are many
studies in relation to reading lengthy text
and web searching

on small screen devices
(Dillon et.al 1990;

Jones et.al 2003),


Most
studies tested reading
speed depending on
the methods of text presentation (Hedin
et.al

2007; Laarni, 2002; Chan, 2008
).
Others studies tested the reading speed
depending on the

font size and age of the
user (Darroch et.al 2005).


Overall

tasks
such as text reading and web brows
ing
were viewed as challenging on smaller
screen devices.

Georgiev & Georgieva
(2010),

determined that the size of screen
and screen resolution are two of many

parameters affecting the text readin
g speed
of mobile devices. These parameters

a
re
illustrated
in Appendix C.

Consequently
the size of the screen and screen resolution
are important parameters included in the
scheme.

3.2.1.
(B)

Portability/weight

The weight of the device may impact the
user perception of the portability of the
device. For example it
may be
more
convenient to carry a mobile device than
an iPad. In
our CAS

device
weight is
examined as a factor which influences the
choice of device fo
r a particular task
which
in turn may
impact performance.

3
.2.1.(C)
Input methods


Foley, Wallace and Chan (1984) structured

a taxonomy of input devices around
graphic subtasks
that
they are capable of
performing,
these

include position,
orientation, path
, and quantify and text
entry. Based on their study
Ballagas, et.al
(2006)
outline existing input techniques
and their ergonomic measures, which
comprise

indirect relative cameras,
joystick, track pad, directional step keys
and direct absolute camera. Following
these, measures in the
CAS
for include
keyboard, touchscreen, and image or
visual input methods.

3.2.1
(D)

Power

The battery capacity of mobile dev
ices is
severely restricted due to constraints on
size and weight. Therefore, energy
efficiency is critical for usability of these
devices.

Carroll & Heiser (2010)

illustrate
d
the
energy consumption for
different usage
scenarios; t
he usage scenarios inclu
ded
audio and video playback, text messaging,
phone call, email and web browsing.

W
e
include
talk time, audio and video
playback and internet browsing time into
our
CAS as

it will be necessary to identify
which tasks require the longest battery life
and if the device is suited to it.

3.2.1.(E)
Connectivity

The concept of always best connected
(ABC) allows a person to connect to
applications using the devices and access
technologies that best suits their needs.
Kellokoski et.al (2012), describes

real life
long delay problems that occur when
performing network

changes in
heterogeneous networks with difficulty in
real time communication. Therefore, this
was
i
ncluded in the
CAS
when measuring
time taken for users to complete tasks.

3.2.3

Task Complexity

Frese (1987) proposed that task
complexity is determined by

the number of
decisions that have to be made and by the
relations among those decisions. Based on
this Jacko and Salvendy (1996)
demonstrate that the perceived complexity
of a computerised task increased as the
depth of the hierarchical menu increased.
Th
ey found that perceived complexity
increased as menu depth increased and
also that perceived complexity lengthened
response time and reduced response
accuracy. Thus minimizing perceived
complexity by creating a shallow menu
structure might improve user res
ponse
time. This is quite important to include in
the scheme as it may influence the
assessment of decision time.


3.2.4 Information Type and Structure

For the purpose of profiling we need to
define the ‘information type’, is it text,
audio or visual. We shall identify the
information structure. Horizontal depth
divides a unit of content into multiple
sequential links rather than leaving a single
larger u
nit
on one page (Chae & Kim
2004). Chae and Kim (2004) determined

that increasing horizontal depth may also
increase perceived complexity for users.

They acknowledge how the problem is
especially pertinent to the mobile internet,
where relatively deep men
u structures are
to some degree unavoidable, given the
large amount of information and the
limited screen space. This may have a
significant impact on the time taken for
users to make decisions when using the
device for complex tasks.



Independent Variables

Description

References in Literature

Environment

Employee Segment & Use Cases, Office or Mobile

Schmidt et.al 1999

Software Application

Interaction Methods

Temporal,
Communication, Transaction, Public,
Multiplicity, Location, Identity

Nickerson,

et.al. 2009

Task Complexity

Multiple Paths

Jacko

and Salvendy (1996), Chae
&Kim

(200
4
),




Small Form Factors


System Characteristics
” Screen size, Screen
resolution,
Portability/ Weight, Input Methods, Power,
Connectivity

Venkatesh &Bala (2008), Georgiev,
(2010)

Ballagas, et.al (2006)
, Carroll and
Heiser (2010), Kellokoski et.al
(2012)




Type of Information
& Information
Structure

Text, Audio, Visual

Horizontal and

Vertical depth (BPN and WPN)

Oksman, V. et.al 2007

Chae and Kim 2004




Table 1. Independent Variables

Table 2
.

Dependent Variables

4.

DEPLENDENT VARIABLES


4.1

Information Quality


Mobile Devices
functionality is to select,
acquire, describe, organise, store, process,
integrate, search, retrieve and manage
information (Fattahi & Afshar 2006). As

mobile devices influence information, the
quality of the information expelled from
these devices is a dependent variable.

Hence t
he quality of information resources
is defined in the context of its use. If
information quality
(IQ)
is poor, the IT
utilizat
ion cannot meet its purpose and
could
have a negative influence on
decision making and on organisational
performance
. Remus,
(
1984
)

states
that
the
way in which the information is presented
is one of the most important factors
affecting decision
-
making (Re
mus, 1984).
Therefore it is important to include

measurements for IQ in the
scheme for
assessment.

We

measure IQ using Wang
and
Strong

(1996)
dimensions of benefit

(Wang and Strong 1996)
. Based on
interviews with our enterprise pa
rtner it
became
evident th
at the most important
dimension
for them
includes;

accuracy
,

c
ompleteness

and appropriate amount of
data.

4.2
Decision Time

Research has shown that information
quality has a considerable effect on
decision quality and quantity of
information on decision
effectiveness of
consumers (
Keller & Staelin 1987). Hence

the use of mobile devices to consume and
exhibit information can
affect
decision
Dependent Variables

Description

References in Literature

Information Quality

IQ Dimensions



Wang and S
trong

(1996)

Decision Time

Time constraint, Time
pressure



Fisher et al. (2003)

User Satisfaction

Task Technology Fit

Perceived use and perceived
usefulness

Goodhue and Thompson (1995)

Delone and McLean (1992)


quality and effectiveness
. Svenson et al.

(1990) pointed out that decision quality
depends heavily on time, and that
v
ariable
time should be accounted for

in any
decision
-
making experim
ent. Fisher et al.
(2003)

categorised decision time into time
constraint and time pressure. Time
constraint is an allocated period of time for
decision
-
making. It could be controlled by
al
locating a certain amount of time for the
making of a decision. Time pressure is a
subjective reaction to the allocated time
and it occurs when available time is less
than required
time (Svenson et. al 1990).



4.3 User Satisfaction

User experience assessment captures end
user interphase needs as they relate to the
effective use of current and future IT
applications
Donnellan & Helfert (2010).

Goodhue and Thompson (1995) propose a
mod
el that focuses on association between
information
systems and individual
performance i.e. task technology fitness
(TTF). Their work emphasizes the fit
between IT and users’ tasks and provides
an evaluation approach to test whether IT
meets the user needs. In addition, they also
point out that data quality

is one of the key
constructs to identify the research gaps
between systems capabilities and user
needs.
Goodhue et. al (1995) proposes the
user evaluation of TTF as a measure of IT
success. Connecting

TTF to utilization,
Goodhue (1995
),
considers utilizat
ion as a
performance indicator besides TTF.
Additionally
Delone and McLean (1992)

propose a comprehensive IT success
model that stresses the effects of use and
user satisfaction on individual and
organisational impact. System quality and
Information qualit
y are considered as the
influencing factors to use and user
satisfaction.

5.

LIMITATIONS

It is evident from the research
that there
are many external variables which may
influence usage of mobile devices;
other
influencing factors include individual
differences and

social influences.
Venkatesh and Bala (2008),
describe
individual differences as variables which
include personality and/or demographics
(e.g., traits or states of individuals, gender,
and age).While testing
Anssi et. al (2005)
discusses

ho
w females are more likely to
experience techno stress in usin
g
computers compared with males.

They
also report that males are more likely than
females to

perceive computer usage as fun.

Age was chosen as a covariate. Age has
been found to be associated wit
h
unfavourable perceived usefulness and a
decreased attitude towards using
computers as well as adoption

(
Anssi et. al
(2005)
.
Social Influences

include various
social processes and mechanisms that
guide individuals to formulate perceptions
of various aspe
cts of an IT.
Venkatesh
(2000) suggested that whi
le anchors drive
initial judgments of perceived ease of use,
individuals will adjust these judgments
after they gain direct hands
-
on experience
with the new system
.
Mac Callum et.al
(2012) car
ried out a study testing skill of
students when using IT, They determined
that specific mobile skill was shown to
mediate the role of perceived ease of use
and usefulness on the intention of students
to adopt mobile learning
.

Findlater (2009),
measure awar
eness using the recognition
test and found that high accuracy adaptive
menus negatively impacted the user’s
overall awareness of features in the
interface
.

Thus many influencing factors
exist, however for the purpose of our
research we focus on the variabl
es outlined
in Table 1 and Table2
however future
research would include additionally
analysing other influencing factors such as
those mentioned above.


6.

CONCLUSION


The
literature has exposed many
influe
ncing factors of mobile devices.

We
have constructed a classification and
assessment scheme for the profiling
of user

data. Literature has allowed us to select
independent variables which may i
mpact
IQ
, decision time and user satisfaction.
This paper has
discussed some

variables
and high
lighted
some
of the
literature
s,

which
justifies their inclusion in
our
CAS
.
As this is work in progress we will
implement the
CAS
to create
usage
profiles and

analyse

user data

once

data
has been collected
.


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ACKNOWLEDGEMENT

This Research is funded by IRCSET and
Intel Corporation.



Appendix A



Mobile usage classification and assessment scheme”

(Carey & Helfert, 2012)





Independent Variables
The Lead
The Analyst
The Seller
The Curator
Seekers
User Role
Business Use Case
Temporal
Communication
Transaction
Public
Multiplicity
Location
Identity
S AS
INF RP INT
T NT
PU PR
I G
LB NLB
I NI
Simple (single search)
Complex (comparison search)
Application
How often the application is used per day
Text
Audio
Visual
Deep (6 levels)
Medium (4levels)
Shallow (1 level)
Presentation
Small Form Factors
2.8
3.2
3.5
9.5
360x480 px
960-by-640
87.9-133g
133-140g
601-650g
650-680g
680g-730g
online
offline
Keyboard
Touchscreen
Image
Voice
Talktime
Internet Use
Video Playback
Audio Playback
6-8hrs
6-10hrs
7-10hrs
up to 8hrs
Dependent Variables
Information Quality
Objective and Subjective Assements
Accuracy
Completeness
Appropriate Amount of data
Decision time
Time Taken to complete task
Time back by employees
User Satisfaction
User Perceptions
Ease of use
Usefulenss
User
Task Complexity
Type of information
Information Structure - Horizontal Depth
Interaction Methods
Screen Size
Screen Resolution
Portability
Conectivity
Input Method
Power
Appendix B


“Developing
taxonomy

of mobile applications”


Nickerson et. al 2009


Interaction Method

Explanation

Temporal

The Temporal dimension identifies when the user and the application interact:
Synchronous


user and application interact in real time

Asynchronous



user and application interact in non
-

real time

Communication

The communication dimension relates to which way which way information
flows as the user interacts with the application:

Informational:
information flows only from the mobile application to the user
uni directional information flow to the us
er; information push from the
application to the user.

Reporting:
information flows only from the user to the mobile application;
uni
-
directional flow from user; information pull by the application from the
user.

Interactional:
information flows in both

directions between the user and the
mobile application; bi
-
directional flow between the user and application;
information push and pull

Transaction

The transaction dimension captures this characteristic of the user interaction.
This dimension has the
following characteristics;

Transactional

: user can purchase goods or services through the application

Non transactional
: user cannot purchase goods and services through the
application.

Public

The public dimension relates to whether the application is
generally available.

Its characteristics are the following;

Public;

application can be used by any user; may be limited to a group but any
user may self
-
select to be part of the group that uses the application.

Private;
application can only be used by a

pre
-
selected (by third party) group
of users

Multiplicity

The multiplicity dimension captures this concept of individual or multiple user
interaction with the following characteristics;

Individual;

one user, user experiences the application as if he/she were the
sole user

Group;
multiple users; users view use of the application as part of a group

Location

The location dimension deals with whether the location of the user is used to
modify the
interaction of the application with the user.

It has the following characteristics;

Location
-
based:
mobile application uses the users location

Non
-
location
-
based:

mobile location does not use the user’s location; the
mobile application may know the user’s

location but it does not use this
knowledge to modify the user interaction.

Identity

The identity dimension relates to whether the identity of the user is used to
modify the way the application interacts with the user based on the users
identity

This d
imension has the following characteristics;

Identity based;
mobile application uses the users identity


Non
-
identity based
; mobile application does not use the user’s identity; the
mobile application may know the users identity but it does not use this
kn
owledge to modify the user interaction.

Appendix C.
Parameters affecting text reading speed on mobile devices, (
Georgiev
,

2010)