Digital addiction REFSQ 14

www.bournemouth.ac.uk
Amen Alrobai - Keith Phalp - Raian Ali
Faculty of Science and Technology
Bournemouth University - UK
REFSQ14, Essen, Germany, 7th April 2014

www.bournemouth.ac.uk 2
Outline
 Introduction
 Motivations
 Initial results
 Challenges
 Conclusions

Introduction

What is Digital Addiction?
Mood
modification
Relapse
Salience
Withdrawal
symptoms
Tolerance
ConflictCompulsive Impulsive
Behaviours
Symptoms
Generalised
Pathological
Internet Use
Specific
Pathological
Internet Use
Interaction Themes
“ .. the inability of individuals to control their Internet use, resulting in
marked distress and/or functional impairment in daily life”
(Ha, J.H. et al., 2006)
Context-
ordinated
Content-
ordinated

What is the problem?
 Chinese teenage addicts to the Internet applications increased from
13.2% in 2005 to 14.1% in 2009
(China Youth Association for Network Development, 2010)
 In 2013, 3.7% of British students were considered to be addicted to
gaming and social applications
(Kuss et al., 2013)
 About 140 Internet addiction treatment recovery centers in South
Korea
(Younget al., 2011)
 DA is still not seen as a problem of the software (e.g., game fatigue
system)
 Who to blame?

Motivations

Motivations
 Users requirements and software features almost ignored.
 Current solutions:
• Education
• Restriction
• Cognitive behavioral therapy (CBT)

Initial results

(1) Problem Exploration

(2) DA definition for RE
Compulsive/impulsive use of software-mediated operations to
reach certain requirements. This includes inability to switch to
other available alternatives to reach the same requirements without
a good reason.
DA
symptoms
software-mediated
operations
of
is
realised through
certain
requirements
to reachCompulsive/impul
sive use
Fig. RE logical model

Terminologies Analysis
• Addiction (used by Influential communities)
• Dependence (physical dependence Vs. mental disorders)
• Compulsive (completely unable!!)
• Pathological (adopted from pathological gambling)
• Impulsive (failure to resist)
• Problematic (consequences)
• Excessive (intensity & frequency)
BehaviourObject
Analysing
terminologies
through LR
Developing
logical models of
the definitions
Generating a
relationship
table
Triangulation
• Internet (generic and includes all internet-enabled activities)
• Technological (Internet is subclass)
• Online (performed over the Internet)
• Digital media (digital devices)

Logical Models
Problematic
Internet Use
managing one’s
offline life
multidimensional
syndrome
symptoms
consist of
is
with
difficulties
result in
Internet
addiction
daily life
inability
individual
of
is marked distress
and/or functional
impairment
resulting in
Technological
Addiction
Human-
machine
interaction
[involves]
is Non-chemical
(behavioral)
addictions
cognitive
emotional
behavioral
includes
to control
internet
use
in
(Generalized)
Pathological
Internet Use
multidimension
al overuse
The
Internet
of
is negative personal
& professional
consequences
results in
Problematic Internet Use: a multidimensional syndrome that consists
of cognitive, emotional, and behavioural symptoms that result in
difficulties with managing one’s offline life. (Caplan, 2005)
Technological Addiction: operationally defined as non-chemical
(behavioral) addictions that involve human–machine interaction.
(Griffiths, 1996a)
Internet Addiction: the inability of individuals to control their
Internet use, resulting in marked distress and/or functional
impairment in daily life. (Ha, J.H. et al., 2006)
(Generalized) Pathological Internet Use: conceptualized as a
multidimensional overuse of the Internet itself that results in
negative personal and professional consequences. (Davis, 2001)

Logical Models
Summary of the logical model of definitions
symptomsinternet use
involves
realised through
certain
requirements
leads to
behaviour

(3) Initial Ontology
 Purpose: Facilitating subject-matter discussions and initiating Ontology
development
 Method: Conventional & directed content analysis approach
 Questions: What are the factors, SW features, dynamics and users
characteristics?

Enhance Ontology Development

(4) DA Ontology V.1
Digital addiction
Cultural context User context
Habits
Checking
habit
Mental
disorders
(ADHD)
Obsessive-
compulsive
disorder
Depression
Social
anxiety
Communication
Addiction
Disorder
Behaviours
Escapism
Disinhibition
Timelessness
Impersonation
Multitasking
Escalation of
commitment
Hyperpersonal
aspect
Solipsistic
introjection
Dissociative
imagination
Traits
Low
self-esteem
Narcissistic
Depressed
mood
Impulsivity
Reduced
attentiveness
Shyness
Sensation
seeking
Lack of
willpower
Anhedonia
Emotions
Stress
Social
solation
Social
alienation
Anxiety
Anticipation
Requirements
Goals
Increasing the
number of
followers
Seeking
companionship
Raising
reputation
Values
Self-
Actualization
Respect
Acceptance
Motivations
Attention-
seeking
Sociability
Peer-esteem
Software context
comprisecomprisecomprise
comprisecomprisecomprise
comprisecomprise
includes
influncedBy
includes

DA Ontology V.1
Digital addiction
Cultural context User context Software context
Interaction
medium
Properties
Invisibility
Minimisation of authority
Timelessness
Affordability
More manageable
environment
Power of crowd
Loss of boundaries
Sense of freedom
Interactivity
Affordability
Availability of variety
Habit-forming
Availability
Out of control
Social software
Content
Real time
Less
informative
Features
Personalisation
Customisation
Rewarding mechanisms
Social plugins
Excessive notifications
Privacy
Anonymity
Usability
attributes
Learnability
Efficiency
Effectiveness
Memorability
Security
Satisfaction
Types of
communication
Asynchronicity
Synchronicity
comprisecomprisecomprisecomprise
comprise

Ontology Development Protégé

(5) Requirements-centered
Addiction Factors (Meta-model)
VALUES
ADDICTIVE
ASPECTS
Mayhave
Translated torequests
Has
Has “Private” Affect
Satisfy
DrivenbyRepresent
USER
- Like
- Retweet
- Follow
- Subscribe
- Profile completeness
- Visibility status
I would need virtual space to
connect with specific sets of people
to build social relations, share
updates, photos or documents and
message other group members
Raising social
reputation
GOALS
-Respect
- Acceptance
- Conformity
REQUIREMENTS FEATURES
-Rewarding mechanisms
-Excessive notifications

Challenges

Challenges
 Is DA symptom or an issue?
 DA measurement
 Diversity of both Software and Users
 Developing the social platform
 Tacit and fuzzy nature of DA concepts
 User’s satisfaction should not be compromised

Conclusions

Conclusions
 Can DA treated as a business goal (Resist Vs. Inability)?
 Is it HCI issue, RE or both?
 Is it NFR?
 If software adaptively would be one part of the solution, more
questions still need to be answered, e.g.:
 What to monitor in terms of users interactions, internal or external
triggers?
 How, or even whether, users would like to be aware of DA when they
have it?
 What decisions would be taken by software and what other decisions
are to be taken by users when the software is running?

References
1. Ha, J.H. et al., 2006. Psychiatric comorbidity assessed in Korean children and adolescents who screen positive for Internet addiction. Journal of
Clinical Psychiatry.
2. China Youth Association for Network Development. (2010). The National Report on Internet Addiction of Chinese Youths (2009). Beijing
3. Kuss, D. J., Griffiths, M. D., & Binder, J. F. (2013). Internet addiction in students: Prevalence and risk factors. Computers in Human Behavior, 29(3),
959-966.
4. Young, K.S. & de Abreu, C.N., 2011. Internet addiction: A handbook and guide to evaluation and treatment.
5. Kietzmann, J.H. et al., 2011. Social media? Get serious! Understanding the functional building blocks of social media. Business Horizons, 54(3),
pp.241–251.
6. Caplan, Scott E. "A social skill account of problematic Internet use." Journal of communication 55.4 (2005): 721-736.
7. Davis, R.A., 2001. A cognitive-behavioral model of pathological Internet use. Computers in Human Behavior, 17(2), pp.187–195.
8. Griffiths, M., 1996. Gambling on the internet: A brief note. Journal of Gambling Studies, 12(4), pp.471–473.

Thank you!
Acknowledgements:
• Dr. Huseyin Dogan - Bournemouth University – UK
• Assoc. Prof. Jacqui Taylor - Bournemouth University – UK
• Bournemouth Uni Graduate School for their PGR Development Grant
• FP7 Marie Curie Programme (the SOCIAD project)

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