Arab Academy Graduate School of Business
Social Media Marketing Modeling & Theory
Research Paper
Under the Supervision of : Prof. Dr. Eng. Hesham
Dinana
Prepared by: Amani Gera Hala Riad
Mohamed Abdel Wahab
Reham El Kaliouby
2
Table of Contents & Figures & Tables
Abstract ............................................................................................................................................ 2
Summary & Introduction ................................................................................................................. 4
Social Cognitive Theory .................................................................................................................... 6
Literature Review ............................................................................................................................. 9
Research Design ............................................................................................................................. 21
Data Analysis & Findings ................................................................................................................ 24
Discussion......................................................................................................................................40
Limitations, Conclusion & Recommendations...............................................................................42
Questionnaire................................................................................................................................44
References.....................................................................................................................................50
Figure (1) - Internet Usage Statistics……………………………………………..……………………………….……........5
Figure (2) - SCT..………………………………………………………………………..……………………………………..……....9
Figure (3) -Change in Internet use by age……………………………………………………..………………….……...10
Figure (4) - Fig2 Information internet use…………………………………………………………..…………………....17
Figure (5) - Fig3 Entertainment inter use……………………………………………………………..…………….….….18
Figure (6) - Fig4 Social internet use…………………………………………………………………………………....…….19
Figure (7) - Who is online……………………………………………………….…………………………………………...……20
Figure (8) – BBC Teen internet Usage………………………………………………………….……………….....……...29
Figure (9) - Gender differences and Internet usage……………………………………………………..…………..30
Figure (10) - Information seeking Internet Use Model……………………………………………………….......32
Figure (11) - Gender-info seeking bar chart………………………………………………………………………….....34
Figure (12) - Entertainment Internet Use Model………………………………………………………………….....35
Figure (13) - Entertainment –Gender Bar chart…………………………………………..……………………….....37
Figure (14) - Social Networking Internet Use Model………………………………………………..………….....38
Figure (15) – Gender-Social bar chart…………………………………………………………………..…………….......39
Table (1) - Operational definitions of variables………………………………………………………………………...15
Table (2) - Correlation Table Results............................................................................................25
Table (3) – Internet Use Table......................................................................................................26
Table (4) – Gender Table..............................................................................................................27
Table (5) – Gender-Internet use crosstabulation.........................................................................30
Table (6) – Mean and SD Table Results........................................................................................31
Table (7) - Hypothesized paths of Information Seeking...............................................................32
Table (8) - Gender * info seeking internet use Crosstabulation...................................................34
Table (9) - Hypothesized paths of Information Seeking...............................................................35
Table (10) - Hypothesized paths of Information Seeking.............................................................37
Table (11) - Gender * social internet use Crosstabulation...........................................................39
3
Abstract
Purpose
Understand the internet behaviour of teenagers through
examining the relationships among social influence and self
regulatory mechanisms. For this purpose three types of
internet behaviours (information seeking, entertainment&
social activities) have been addressed. In this research we
have chosen the path analysis model of internet behaviour
which
is
mainly
built
upon
Bandura’s
1997
Social
Cognitive Theory.
Design methodology
The Framework for conducting the research is a Hypothesis
Testing and the Type of Investigation is a Corrlelational Type.
In researching we used one method for data collection which
is a questionnaire survey. It was composed of 20 questions
attached with this report.
Findings Research limitations/implications It is important to note
that the findings of the current study relate to teens aged 14
to 17 who live in Cairo which is the most developed city in
Egypt. These factors might limit the generalizability of the
findings to a wider population, such as today’s Internet users.
Also (N=50) limit it as well.
4
Future research on age differences in individuals’ preferences
for
using
the
Internet
technology
might
prove
fruitful.
Consequently, because of the ever-changing nature of the
Internet, what we learn today may not be valid a few years
from now. Thus, ongoing research is necessary to keep abreast
of it especially that marketing activities proves to be cost
effective
Keywords
Teens’ internet use, Social media, Social networking, Social
Marketing
Paper type Research paper
Summary
This research addresses three types of internet behaviours of
teenagers:
information
seeking,
entertainment&
social
activities. In order to study these online behaviours, we
examined the relationships among social influence & self
regulatory models as mechanisms to the teenage behaviour of
internet use. We have gathered information from 50 students
from BBC INTERNATIONAL School. Using the path analysis
model to test different types of internet use, we found that
social influence greatly affects self regulation which in turn
affects internet -use.
5
Introduction
Since the development of the internet in mid 90’s, there has
been a rapid increase of internet-usage all over the world.
The internet became very popular in society due to its use for
commercial as well as for scholarly purposes. In 2009
internet usage throughout the whole world was estimated at
1,596 million with 393 million users (48.9% penetration
rate) in Europe alone. Statistics pose that there has been a
growth in users between 2000 and 2009 of about 342%
worldwide (Miniwatts Marketing Group, 2009)
The following table represents internet usage around the
world in proportion to world population.
6
Figure (1) Internet Usage Statistics
Internet grew rapidly to become a very important aspect in
the lives of many people. It is estimated that as of 2009, a
quarter of Earth's population uses the services of the Internet.
Usage started with academics, businesses then moved to
hobbyists and then the general public and now teens.
Today's teens are growing up in a world where social media
is everywhere. Regardless of whether or not they have access
7
to these technologies or how they engage with them, there is
little doubt that social media is playing a significant role in
the changing landscape of youth.
The Internet is as much a part of teenagers’ lives as TV,
school
and
books.
It
provides
entertainment,
social
interaction and educational opportunities. We can expect the
time teenagers spend online to increase along with expanded
offerings on the Web, and the growing network of their
friends and family who use the Web frequently. Young
people
are
using
websites
like
MySpace
and
Facebook,
sharing photos, videos, music, ideas, and opinions online
and connecting with a large group of peers in new and
sometimes unexpected ways.
In this study we are interested in understanding why
teenagers use the internet & what are the different influences
that affect their internet behaviour. The purpose of this
paper is to understand the internet behaviour of teenagers
through examining the relationships among social influence
and self regulatory mechanisms. For this purpose three types
of internet behaviours (information seeking, entertainment&
social activities) have been addressed. In this research we
have chosen the path analysis model of internet behaviour
which
is
mainly
built
upon
Bandura’s
1997
Social
Cognitive Theory.
First we are going to talk about the Social Cognitive Theory
with its different concepts. Next we are going to mention a
detailed literature review about the path analysis model that
8
is used for explaining internet behaviour. This review will
also define the different constructs & variables of the model
as well as the developed hypotheses. Third, we will mention
the research design together with the data collection. Fourth,
the analysis of our different findings will be made. Finally,
we will present our conclusion & recommendations.
Social Cognitive Theory
In 1941 Miller and Dollard proposed the theory of social
learning. In 1963 Bandura and Walters broadened the social
learning theory with the principles of observational learning
and vicarious reinforcement. Bandura provided his concept
of self-efficacy in 1977, while he refuted the traditional
learning theory for understanding learning. Social Cognitive
Theory (SCT) deals with cognitive, emotional aspects and
aspects of behavior for understanding behavioral change.
The concepts of the SCT also provide ways for new behavioral
research in many educational areas (Pajares 2002).
The social cognitive theory explains how people acquire and
maintain certain behavioral patterns, while also providing
the
basis
for
intervention
framework
for
designing,
strategies.
implementing
SCT
provides
and
a
evaluating
programs. Evaluating behavioral change depends on the
factors
environment,
mentioned,
the
people
three
and
factors
behavior.
environment,
As
Bandura
people
and
9
behavior are constantly influencing each other (Pajares
2002).
Social cognitive theory provides a comprehensive theoretical
framework
for
understanding
human
behavior,
social
interaction and psychological well-being (Bandura, 1986;
1989; 1997) with which we propose to reformulate the
relationship between Internet use, self regulation & social
influence. The theory recognizes a variety of mechanisms
that govern human behavior, including enactive learning
(learning through one’s own experience), vicarious learning
(learning by observing others), self-regulation (the practice of
self control) and self-efficacy (or the belief in one's ability to
perform a task successfully). The self-efficacy mechanism
(Bandura, 1977; 1982; 1997) pertains since it describes the
cognitive
processes
that
relate
the
acquisition
to
the
performance of new behaviors. This concept may explain the
implications of the transition from novice to veteran Internet
user for psychological well-being.
Concepts of the Social Cognitive Theory
According to Glanz et al (2002), SCT has several concepts as
follows:
Environment: Factors physically external to the person;
Provides
opportunities
and
social
support.
Environment
refers to the factors that can affect a person’s behavior. There
are social and physical environments. Social environment
include family members, friends and colleagues. Physical
10
environment is the size of a room, the ambient temperature
or the availability of certain foods.
Situation:
Perception
of
the
environment;
correct
misperceptions and promote healthful forms. The situation
refers to the cognitive or mental representations of the
environment
that
may
affect
a
person’s
behavior.
The
situation is a person’s perception of the lace, time, physical
features and activity.
Environment
and
situation
provide
the
framework
for
understanding behavior (Parraga, 1990). Behavior is not
simply the result of the environment and the person, just as
the environment is not simply the result of the person and
behavior. The environment provides models for behavior.
The concept of behavior can be viewed in many ways.
Behavioral capability: Knowledge and skill to perform a
given behavior; promote mastery learning through skills
training. Behavioral capability means that if a person is to
perform a behavior he must know what the behavior is and
have the skills to perform it.
Expectations: Anticipatory outcomes of a behavior to model
positive outcomes of healthy behaviour.
Expectancies: The values that the person places on a given
outcome or incentives that present outcomes of change that
have functional meaning.
11
Self-control: Personal regulation of goal-directed behavior or
performance by providing opportunities for self-monitoring,
goal setting, problem solving, and self-reward.
Observational learning: Behavioral acquisition that occurs
by watching the actions and outcomes of others’ behavior. It
includes credible role models of the targeted behavior. This
occurs when a person watches the actions of another person
and the reinforcements that the person receives (Bandura,
1997).
Reinforcements:
Responses
to
a
person’s
behavior
that
increase or decrease the likelihood of reoccurrence through
promoting self-initiated rewards and incentives.
Self-efficacy:
The person’s confidence in performing a
particular behavior which should be approached in small
steps to ensure success.
Emotional coping responses: Strategies or tactics that are used
by a person to deal with emotional stimuli to provide
training in problem solving and stress management.
Reciprocal determinism: The dynamic interaction of the
person, the behavior, and the environment in which the
behavior is performed. It considers multiple avenues to
behavioral change, including environmental, skill, and
personal change.
12
SCT Conceptual Factors
Figure (2)- SCT
Literature Review
In general, the internet has become an important and
permanent media outlet. In a study conducted by the
National
Telecommunications
and
Information
Administration (2002), it was found that the chief uses of
the internet are usually related to e-mail, instant messaging,
information seeking (news, weather, sports, etc…), playing
games,
listening
products&
to
services,
radio&
health&
music,
online
government
purchase
services
of
search,
school assignments, online banking, etc… Children and
young adults on the other hand, have embraced the internet
13
in conducting their daily activities, and therefore, they use
the internet in ways that differ from older adults.
While
older adults tend to use the internet to check for news, sports,
weather, or research products and services, children and
young adults are more likely to use the internet to complete
school assignments or play games.
Also, while very high
percentages of all age groups – adults and children alike –
use e-mail; older children and young adults are doing so at
much higher levels. Children and young adults also use the
Internet for communication and entertainment such as going
to chat rooms, listening to the radio, and watching TV or
movies. As for playing games, it peaks among teenagers 14-17
year olds (NTIA, 2000).
The following charts show percentage of internet users
by
age
groups & percentage of
their change
in
usage
throughout the period from 2000 to 2009 (Lenhart, Purcell,
Smith & Zickuhr, 2010).
14
Figure (3) Change in Internet use by age
From our model it is suggested that information seeking,
entertainment and social online activities are the primary
internet uses. These uses or motivations for use are considered
to
have
a
great
impact
upon
psychological
wellbeing,
addiction tendencies and cause for psychological stress.
Therefore, the information seeking, entertainment & social
online activities will be our dependent variable. Our model
has also focused on teenagers as this age group is believed to
have a significant percentage of internet usage.
In our opinion, the gender is one of the important variables
that should be considered. This is because we believe that
males & females differ in their types of internet usage.
According to the study conducted by NTIA (2000), more
15
males than females used the internet to check news, weather,
and sports, but more females went online to find information
on health services or practices.
A larger percentage of male
internet users reported using the internet for entertainmentoriented activities. Also a higher proportion of males versus
females played games online and viewed television or movies
or listened to the radio. As for social activities, such as chat
rooms, males and females responded similarly for these
categories. In general, although the aggregate rates of use and
growth by gender have equalized, there are still genderrelated differences in internet use within various age groups.
The gender variable is introduced in our model to discuss
how types of internet usage differ between different genders
(Eastin, 2005). This variable was not introduced in previous
researches such as LaRose & Eastin (2004).
As previously mentioned, the model studied in this article
examines the relationships among social influence & self
regulatory constructs as mechanisms to the behaviour of
internet use of teenagers either to collect information, for
entertainment, or for social activities.
The social influence studied in this article presents three
variables (social group success, prior experience & parental
success). This social influence is important because when one
observes the success of his/her social group (peers, friends,
etc…), his/her own experience and that of parents in using
the internet; all of this will increase one’s confidence &
expectations & subsequently increase usage. This concept is
16
built upon Bandura’s research in 1997 which stated that
efficacy beliefs are raised when an individual observes &
regards others’ experiences as positive. At the same time,
when individuals observe the failure of others; judgments of
their own ability& expectations are lowered (Eastin, 2005).
In another previous research done by LaRose & Eastin
(2004), the experience variable was considered as a whole
(social influence) & was proved to have a positive effect on
self-efficacy which is in turn positively related to internet
usage. This model discusses 3 types of experience in using the
internet
(information
seeking,
entertainment&
social
activities) (Eastin, 2005).
Previous research on the social influence of parents & peers,
has been conducted by Hanway (2003) who reported that
although blacks and Hispanics were among the most rapidly
growing
groups
of
internet
users,
they
still
lagged
significantly behind non-Hispanic white Americans in their
internet use. The same is also cited between black teens and
Hispanic teens, and white teens. The reasons for this gap,
includes "network effects" that is, people begin to use new
technologies when they see their family, friends, and their
broader community adopting them.
Regarding the self regulatory construct, it addresses two
variables:
2005).
In
self-efficacy
a
previous
&
outcome
research
by
expectations
Bandura
(Eastin,
1986,
he
separated the affective and behavioral outcomes into two
distinct types: self-efficacy and outcome expectancy (Baker&
Stone, 2008). Outcome expectations were also divided into
17
positive & negative outcome expectations. Positive outcome
expectations include positive reaction of others, approval &
social recognition while positive personal ones include self
satisfaction,
pride&
self
worth.
Negative
outcome
expectations, on the other hand, reflect negative online
experiences such as receiving unwanted mail or fraudulent
information (Eastin, 2005). According to Bandura 1986,
outcome
expectancy
is
an
individual’s
belief
that
by
accomplishing a task, a desired outcome is attained (Baker&
Stone, 2008).
In a Pew Research Center poll (Pew Research Center, 2000)
most Internet users said that e-mail had improved their
connections to family and friends, and those perceptions
(positive expectations) increased the longer users had been on
the Internet and the more they used it. At the same time,
other scholars (such as Heim 1993& Stoll 1995) have
warned
about
the
potential
harmful
effects
of
online
interpersonal communication, blaming online technology for
disrupting real world networks. Nie and Erbring (2000)
found that as Internet use increased, users were more likely
to report a decrease in time spent talking to family and
friends and attending social events (LaRose, Eastin & Gregg,
2001). This could, in our opinion, be considered a negative
expectation
that
could
decrease internet usage. This is
because if users believed that increased usage would lead to
fewer friends, for example, they would therefore decrease
there usage. It could also be seen as a positive expectancy,
18
meaning that if users believed that going online would
increase their friends they would certainly increase usage.
Previous research by LaRose et al (2003) found that positive
expected
outcomes
were
significantly
related
to
general
internet use. As for negative outcome expectations, they are
interpreted by LaRose et al (2001) in the framework of
frustrations& stressors (e.g. bad information, spam, long
download times, etc…). This model discusses how positive&
negative outcome expectations (when broken into distinct
behavioural
models)
produce
significant
relationships
within the context of their predictors (Eastin, 2005). That is
why
this
research
addresses
three
types
of
outcome
expectations (information, entertainment & social activities)
while LaRose & Eastin (2004) discuss outcome expectations
as one variable.
Bandura 1986 defined self efficacy as an individual’s belief
that he or she possesses the skills and abilities to successfully
accomplish a specific task represents self-efficacy (Baker&
Stone, 2008). In 1997 Bandura redefined self efficacy as the
belief in one’s capabilities to organize & execute a required
course of action to produce a certain outcome.
It is
considered as a form of self evaluation that influences
decisions
concerning
different
behaviours. Ajzen
(1991)
places self-efficacy as an important variable in dealing with
the internet. This is because the internet represents a
complex
technology
that
requires
skill&
training
to
successfully operate (Eastin, 2005).
19
Standing at the very core of social cognitive theory, are self-
efficacy beliefs. Self-efficacy beliefs provide the foundation
for
human
motivation,
well-being,
and
personal
accomplishment. This is because unless people believe that
their actions can produce the outcomes they desire, they
have little incentive to act or to persevere in the face of
difficulties.
Bandura's
Much
empirical
contention
that
evidence
self-efficacy
now
supports
beliefs
touch
virtually every aspect of people's lives—whether they think
productively,
self-debilitatingly,
optimistically;
how
well
they
pessimistically
motivate
themselves
or
and
persevere in the face of adversities; their vulnerability to
stress and depression, and the life choices they make. Selfefficacy is also a critical determinant of self-regulation
(Pajares, 2002).
In a research done by (LaRose, Eastin & Gregg, 2001), they
state that within social cognitive theory self-efficacy is an
important mediating factor between social behavior and
depression. In their model, they addressed depression, stress &
experience & how self-efficacy may mediate the effect of
both stress and social support on depression. They added that
the stress resulting from problems in using internet (slow
downloads and unwanted e-mail) could be a significant
source of depression. This could mean that these users never
achieved
the
levels
of
self-efficacy
required
to
control
Internet-related stress (LaRose, Eastin & Gregg, 2001). This,
in our opinion, could in turn reduce their internet usage. In
their opinion, usage as well as prior Internet experience
20
increased
self-efficacy,
which
in
turn
decreased
stress
encountered online, a contributor to general life hassles
related to depression (LaRose, Eastin & Gregg, 2001).
Self-efficacy and outcome expectancy have separate impacts
on behavior and effect. However, self-efficacy typically has a
larger effect than outcome expectancy (Bandura, 1986).
Generally, self-efficacy has a direct impact on outcome
expectancy (Stone & Henry, 2003). In self-efficacy theory,
four groups of constructs are proposed to directly impact selfefficacy and outcome expectancy. These constructs are past
experience or mastery with the task, vicarious experience
performing the task, emotional or physiological arousal
regarding the task, and social persuasion to perform the task.
These constructs impact attitudes toward the task, behavioral
intentions
to
perform
the
task
and
ultimately
task
performance through self-efficacy and outcome expectancy
(Baker& Stone, 2008).
This research shows that besides its effect on internet use, self
efficacy is considered a causal antecedent to both positive&
negative outcome expectations. For example, positive self
efficacy beliefs are thought to increase positive expectations
while negative expectations are thought to decrease as users
become more efficacious and confident in their internet
usage (Eastin, 2005).
21
The model presented in this article seeks to prove several
relationships within two contexts of internet use. The first
context is the information seeking internet use model, the
second construct is entertainment internet use& the third is
the social internet use. Previous research has proved the
positive relationship between prior experience & internet self
efficacy (Staples et al, 1998).
Building upon these previous researches, it is expected that
information,
influence
entertainment
corresponding
self
&
social
efficacy
experience
models.
It
is
will
also
believed that the social influence variables (social group
seeking
success,
prior
experience
&
parental
success)
positively affect the self regulation variables (information
efficacy & positive& negative outcome expectations) which in
turn affect type of internet use. T
his is because according to Fulk (1993), technology use in
general is positively influenced by peers& co-workers. This
study seeks to explore the social influence of peers & parents
on information seeking, entertainment & social activities
internet use (Eastin, 2005).
Below is a table of operational definitions for all the model
variables.
22
Variable
Positive and negative outcome
expectations
Operational defintion
Positive outcome
expectations include
positive reaction of others,
approval & social
recognition while positive
personal ones include self
satisfaction, pride& self
worth. Negative outcome
expectations, on the other
hand, reflect negative
online experiences.
Self efficacy
An individual’s belief that
he or she possesses the
skills and abilities to
successfully accomplish a
specific task represents
23
self-efficacy (Baker&
Stone, 2008). In 1997
Bandura redefined self
efficacy as the belief in
one’s capabilities to
organize & execute a
required course of action
to produce a certain
outcome.
Self-Regulation
Experience:
The practice of self control.
includes actual &
vicarious experience.
Social group & parental
Success
Social influence& success
of peers & parents on
internet use of teenagers.
Table (1) Operational definitions of variables
Hypotheses
From the above discussions we have come up with several
hypotheses for each type of internet use (information seeking,
entertainment& social activities). For the information seeking
internet use we have developed the following hypotheses:
H1: Social group information seeking success is positively
related to information efficacy.
H2: Social group information seeking success is positively
related to positive information expectations.
24
H3: Social group information seeking success is negatively
related to negative information expectations.
H4:
Information
experience
is
positively
related
to
information efficacy.
H5: Parental information seeking success is positively related
to information
efficacy.
H6: Parental information seeking success is positively related
to positive & negative information expectations.
H7: Information Efficacy is positively related to information
internet use
H8: Positive information expectations are positively related
to information internet use.
H9: Negative information expectations are negatively related
to information internet use.
H10: Information efficacy is positively related to positive
information expectations.
H11: Information efficacy is negatively related to negative
information expectations.
H12: Gender is related to type of internet use.
These
hypotheses
could
be
better
understood
from
the
following model of information seeking internet use:
25
For the entertainment internet use we have developed the
following hypotheses:
H1: Entertainment social group success is positively related to
entertainment efficacy.
H2: Entertainment social group success is positively related to
positive entertainment expectations.
H3: Entertainment social group success is negatively related
to negative entertainment expectations.
H4:
Entertainment
experience
is
positively
related
to
entertainment efficacy.
26
H5: Parental entertainment success is positively related to
entertainment
efficacy.
H6: Parental entertainment success is positively related to
positive entertainment expectations.
H7: Parental entertainment success is negatively related to
negative
H8:
entertainment expectations.
Entertainment
efficacy
is
positively
related
to
entertainment internet use
H9: Positive entertainment expectations are positively related
to entertainment internet use.
H10:
Negative
entertainment
expectations
are
negatively
related to entertainment internet use.
H11: Entertainment efficacy is positively related to positive
entertainment expectations.
H12: Entertainment efficacy is negatively related to negative
entertainment expectations.
H13: Gender is related to type of internet use.
These
hypotheses
could
be
better
understood
from
the
following model of entertainment internet use:
27
For the social activities internet use we have developed the
following hypotheses:
H1: Social group success in social activities is positively
related to social efficacy
H2: Social group success in social activities is positively
related to positive social expectations.
H3: Social group success in social activities is negatively
related to negative social expectations.
H4: Social activities experience is positively related to social
efficacy.
H5: Parental social success is positively related to social
efficacy.
28
H6: Parental social success is positively related to positive
social expectations.
H7: Parental social success is negatively related to negative
social expectations.
H8: Social efficacy is positively related to social internet use.
H9: Positive social expectations are positively related to
social internet use.
H10: Negative social expectations are positively related to
social internet use.
H11: Social efficacy is positively related to positive social
expectations.
H12: Social efficacy is negatively related to negative social
expectations.
H13: Gender is related to type of internet use.
These
hypotheses
could
be
better
understood
from
the
following model of social activities internet use:
29
30
BBC INTERNATIONAL SCHOOL as a case study for teens’ (14-17) use of
internet for the three major purposes (information seekingentertainment and social networking)
Figure (7): Who is online?
Our purpose in exploring Teen’s relationship to using the
internet
is
through
the
study
of
students
of
BBC
INTERNATIONAL SCHOOL (INTERNATIONAL DIPLOMA) on
31
how they explore and use the internet and taking into
consideration the differences in Gender.
To examine how these teens are broadly relating to computer
based web applications. In researching Teens and the Internet
in this way, we fully recognize that our population sample
is an ‘already connected’ one, and therefore one which is not
representative of all BBC INTERNATIONAL SCHOOL teens.
However, as mentioned above, the intention is that this case
study
will
comprise
one
part
of
a
wider
research
investigation into Teens and Internet use. At this stage
therefore, we are not seeking to draw any conclusions about
Adults and their relationships to Internet use in general.
Rather, the Teens and the internet use are of specific and
particular interest to us (1) because of the success of the
internet in attracting teens; and (2) because students are at
least
minimally
computer
literate
and
provide
the
opportunity to explore how such teens relate to computer
based web applications communication. Generally we regard
the internet and its applications as facilitating a unique
opportunity to explore teens’ use of online communication,
and specifically the benefits that teens are receiving from the
use
of
this
particular
internet
and
social
networking
websites.
Research Design
In light of the issues surrounding teen’s relationship to the
internet use, these are some of the questions that were
32
formulated to be addressed by the research into teen’s
engagement with the internet and their use of different web
applications:
1. Is there a difference between Gender and each of the three
forms of internet use?
2. Which form of internet use specifically attracts them
more?
3. Whether their experience plays an important role in their
self efficacy for using the internet or they are subjected to the
peers or parents’ influence as well?
The Framework for conducting the research is a Hypothesis
Testing and the Type of Investigation is a Corrlelational Type
and the Researcher Interference is Minimal.
The study setting is a NON-Contrived environment because
the questionnaire was conducted by the students themselves
who represent the right sample age for the research.
The Unit of Analysis was for individuals and the Time
Horizon was a Cross-Sectional one as the questionnaire was
conducted in one day and that is different from the
longitudinal time horizon.
In researching we used one method for data collection which
is a questionnaire survey. It was composed of 20 questions
attached with this report.
33
The Questionnaire
The questionnaire is decomposed of twenty questions that
mostly were extracted mainly from an online survey of the
following web address:
http://pro20.sgizmo.com/survey.php?SURVEY=5UGXZ8529Y8
W4U4W3I8K8BVR5IV5ZK-14897767956039&pswsgt=1245670677&sg_r=http%3A%2F%2Fwww.g
oogle.com.eg%2Fsearch%3Fhl%3Den%26source%3Dhp%26q%3D
social%2Bmedia%2Bsurvey%2Bquestionnaire%26meta%3D%26
rlz%3D1R2ADFA_enEG369%26aq%3D2%26oq%3Dsocial%2Bm
edia%2Bsurve&sg_g=ea4a89fb1dfd1134ff52323fab1ef505&_c
sg=34nttuUpWBm4c&notice=DO-NOT-DISTRIBUTE-THISLINK
Other sources of the constructed questionnaire:
http://www.haverford.edu/psych/ddavis/webforms/p314.cyb
er.02.q1.html
http://www.idemployee.id.tue.nl/g.w.m.rauterberg/ibq/ibq_en
gl.html
The
questionnaire
was
conducted
at
the
BBC
INTERNATIONAL SCHOOL before checking the amendments
34
of it by Prof. Dr. Eng. Hesham Dinana. The comments were
received later and it included changing the peers and
parental questions into a likert 5 scale questionnaire instead
of the nominal scale.
The Sample
The amended questionnaire is attached with the report in
the appendix. In order to have a complete fifty samples
answering
the
questionnaire,
seventy
two
copies
were
35
distributed
among
the
International
Diploma
at
BBC
INTERNATIONAL SCHOOL of Grade 9, 10 & 11 and their
ages is ranging between 14 and 17. This sample age
represents the targeted segment intended to be tested for the
three forms of internet use model.
After collecting back the questionnaire, we found that fifty
respondents were best collected as we left the ones that had
some questions missed.
Measures
The scale, which consists of 21 items which are our
variables we are measuring, was designed in order to
measure the Internet attitudes of the BBC INETERNATIONAL
SCHOOL
students
by
Tavsancıl
and
Keser
(2002).
The
participants mostly responded to the statements using a fivepoint Likert scale ranging from 1 (Strongly Disagree) to 5
(Strongly
Agree)
as
it
is
shown
in
the
Questionnaire
appendix. The validity and reliability studies were carried
out by Tavsancıl and Keser (2002), and the Cronbach Alpha
internal consistency coefficient was found to be 0.89. The
reliability study of this survey of internet attitudes was
found to be 0.91.
Data Analysis & Findings
36
In this quantitative study, surveys were used to determine the
role of Internet usage and Internet attitudes in teens (N=50).
Firstly, Pearson’s correlation coefficient was calculated to
determine the correlation relationships between the three
forms of internet usage. Secondly, Descriptive Analysis was
conducted to compare the mean and the standard deviation
between variables in each of the three forms of the model in
terms of gender. Lastly, the multiple Regression Analysis of
variance and Beta were performed to compare the Internet
usage frequency of the three specific activities of using the
internet
(seeking
information,
entertainment
and
social
networking) in terms of gender variable. For statistical
analysis SPSS 18.0 package program was used.
37
Table (2) - Correlation Table Results
Correlation of information
seeking
Variables
1. Gender
2. info seeking internet
use
3. info seeking self
efficacy
4. +ve expectations on
1
2
3
.041
1.000
-.231
.339*
6
7
8
1.000
.500**
.162
.288*
-.153
-.169
-.091
-.034
7. parents on seeking info -.021
8. peers on seeking info
seeking info
.305*
1.000
-.307*
1.000
-.162
.074
-.034
1.000
.118
.102
.334*
-.030
-.235
1.000
-.043
-.100
-.171
.103
.000
-.437**
4
5
6
seeking info
6. experience
5
1.000
-.083
5. -ve expectations on
4
1.000
Correlation of entertainment
Variables
1. Gender
2. entertainment
internet use
3. entertainment self
1
2
3
7
8
1.000
-.135
1.000
-.102
.054
4. +ve expectations on
entertainment
-.122
.155
.100
5. -ve expectations on
entertainment
.179
-.165
-.088
6. experience
-.091
-.065
.257
.309*
.113
1.000
7. parents on
entertainment
.085
.109
-.347*
.110
.249
-.205
8. peers on
entertainment
.194
-.123
-.228
.235
.133
-.073
efficacy
1.000
1.000
.076
1.000
1.000
.169
1.000
38
Correlation of Social
Networking
Variables
1
2
3
4
5
6
1. Gender
1.000
2. social internet use
-.291*
1.000
3. social self efficacy
-.235
.433**
1.000
4. +ve expectations on
social
-.205
.311*
.540**
5. -ve expectations on
social
.042
.276
.155
6. experience
-.091
.100
.181
-.080
-.001
1.000
7. parents on social
.067
.069
.115
-.230
.086
-.174
8. peers on social
.184
-.107
-.209
.380**
-.098
-.047
7
8
1.000
1.000
.215
1.000
-.163
1.000
Results
Table (2) contains correlation matrices for the three sets of
variables.
All
models
are
based
on
separate
list
wise
deletions, consequently, the sample size did not vary between
sets of variables(N=500). Overall, 100% of the participants
reported having a profile on Facebook, and 100% had
everyday Internet access. Sixty percent reported their Internet
use occurred for more than four hours a day.
Table (3) – Internet Use Table
internet use
Cumulative
Frequency
Valid
Percent
Valid Percent
Percent
3
11
19.3
22.0
22.0
4
9
15.8
18.0
40.0
39
5
30
52.6
60.0
Total
50
87.7
100.0
50
100.0
Total
100.0
Table (4) - Gender Table
Gender
Cumulative
Frequency
Valid
Percent
Valid Percent
Percent
Male
22
38.6
44.0
44.0
Female
28
49.1
56.0
100.0
Total
50
87.7
100.0
50
100.0
Total
The males were 44% and the females were 56% from the
sample as shown in Table 2.
A total of six outcome expectancies were measured. These
included three positive and three negative expectancy values
for information, entertainment, and social uses. Items created
for each of the outcome expectancies were taken from the
research of LaRose et al. Each construct was measured using
five-point Likert-type items, which ranged from very much
(score = 5) to not at all (score = 1).
Positive outcome expectancies: The information outcome
expectancy construct included three items which assessed the
likelihood of obtaining information on the Internet (α= 0.81,
M
=
16.36,
SD
=
5.11).
The
entertainment
outcome
40
expectancy construct was measured with six-items which
assessed the likelihood of being entertained while on the
Internet (α = 0.76, M = 29.12, SD = 7.72). Social expectancies
were constructed using also a five Likert-type items. This
construct measured the perceived likelihood of joining new
networks and making new friends over the Internet (α = 0.73,
M = 19.34, SD = 7.22).
Negative outcome expectancies:
Adopting items from the
Charney and Greenberg (2002) Internet frustration scales
and LaRose et al., the information (α = 0.71, M = 12.10, SD
= 4.67) and entertainment (α = 0.71, M = 12.66, SD = 4.94)
constructs were created with three items, while the social
expectancies construct was composed of two items (α = 0.70, M
= 5.80, SD = 3.81).
Internet self-efficacy judgments: Using the concept driving
the general measure of Internet self-efficacy created by
Eastin and LaRose, three Internet self-efficacy constructs
were measured with Likert- type items ranging from Strongly
Disagree (score 1) to Strongly Agree (score 5).
Information seeking efficacy consisted of five items measuring
belief in ability to seek and obtain information available on
the Internet (α = 0.84, M = 24.60, SD = 7.23).
Entertainment efficacy was constructed using a six item
measure to assess a person’s belief in his/her ability to be
41
entertained while on the Internet (α = 0.92, M = 28.88, SD =
10.68).
The Social efficacy construct contained five items measuring
a person’s belief in his/her ability to obtain and secure
social contact via the Internet (α = 0.82, M = 28.69, SD =
9.53).
Information seeking Internet use was measured with two
items assessing how often a person used the Internet to gather
information whether everyday or less than once a month
and how many hours if used per day (M = 0.72, SD = 0.36).
Entertainment use was measured with six items assessing
how much time was spent using the Internet to play video
games, listen to music, and watch movies on an average (M =
0.78, SD = 0.46).
Finally, Social Internet use was measured with four items
assessing how often a person used the Internet to talk with
other people through Facebook or other social media profiles
and how many friends with a five-likert scale (more than
200+ indicates score 5) (M = 0.96, SD = 0.50).
All usage items were measured with open-ended questions.
After summing and summarizing each measure, 21 variables
were produced summarizing all the variables that need to be
analyzed.
42
Then the .xls file was imported to SPSS 18.0 and the scale
was assigned nominal to the gender variable only.
Figure (8) – BBC Teen internet Usage
Teen Internet Usage
4.8
4.6
4.4
Males
4.2
Females
4
3.8
3.6
Info seeking
Entertainment
Social
43
Figure (9) Gender differences and Internet usage
Gender * internet use Crosstabulation
Count
internet use
3
Gender
Total
4
5
Total
Male
5
6
11
22
Female
6
3
19
28
11
9
30
50
Table (5) – Gender- Internet use CrossTabulation
44
Table (6) - Mean and Standard deviation Tables Results
Descriptive Statistics
N
Minimum
Maximum
Mean
Std. Deviation
Gender
50
1
2
1.56
.501
internet use
50
3
5
4.38
.830
+ve expectations on
entertainment
50
1
5
4.28
.948
+ve expectations on
seeking info
50
1
5
4.18
1.004
+ve expectations on social
50
1
5
3.70
1.111
-ve expectations on seeking
info
50
1
5
2.28
1.294
-ve expectations on social
50
1
5
3.28
1.126
entertainment self efficacy
50
3
5
4.66
.593
social self efficacy
50
2
5
4.18
.873
social internet use
50
2
5
4.36
.851
info seeking internet use
50
1
5
4.04
.880
entertainment internet use
50
2
5
4.48
.735
experience
50
1
5
4.72
.970
parents on seeking info
50
1
5
1.88
1.223
parents on entertainment
50
1
3
1.90
.863
parents on social
50
1
5
2.04
1.142
peers on seeking info
50
1
5
4.00
1.069
peers on entertainment
50
1
5
4.34
.939
peers on social
50
1
5
4.26
1.046
Valid N (listwise)
50
45
Figure (10)-Information seeking Internet Use Model
Peers
on
information
seeking
-.031
-.089
+ve
outcome
expectations
on
information
seeking
Gender
.467
.041
.162
Information
Seeking
Internet
use
.270
Information
Seeking
Efficacy
-.155
Experience
Self
.111
-.169
.320
Parents on
information
seeking
.026
.036
-ve
outcome
expectations
on
information
seeking
.019
Table (7) - Hypothesized paths of Information Seeking
Hypothesized paths of Information Seeking
Expected
sign
H1a
Gender → information seeking internet use
+
H1b
+ve outcome expectations → information internet use
Information seeking self efficacy → information internet
use
-ve outcome expectations → information internet use
+
H1c
H1d
+
+
H1e
H1f
H1g
H1h
H1i
Peers success → +ve outcome expectations
Parents success → +ve outcome expectations
Peers success → information self efficacy
Experience → information self efficacy
Parents success → information self efficacy
+
+
+
+
+
H1j
Peers success → -ve outcome expectations
+
H1k
H1l
H1m
Parents success → -ve outcome expectations
information self efficacy → +ve outcome expectations
information self efficacy → -ve outcome expectations
+
+
-
Beta
Significance
Level
.041
.779
.467
.001
.270
.035
.036
.784
.839
-.031
.320
-.089
-.155
.026
.111
.042
.586
.305
.875
.493
.019
.908
.162
.262
-.169
.240
46
The data fit the information use model:
From
(β=
0.467)
relationship
it
between
shows
that
positive
there
outcome
is
a
significant
expectations
on
information seeking and the dependent variable we are
investigating which is the information seeking internet
usage. Also, the more the information self efficacy the less is
the negative outcome expectations for information seeking
and this is justified through (β=-.169).
However, neither peers’ success nor experience influenced the
information seeking self efficacy. Parents’ success was the one
who influenced the BBC students to gain more self efficacy
in information seeking from the internet and this was
opposing the model of 2005 in USA and we can rely the
differences between the models due to different criteria. One
of which is that the Egyptian culture plays an important
role in shaping students and how they behave. Parents may
not be so influential in other cultures, but in Egypt; parents
can have significant influence especially when they guide
their kids on how to use the internet and how to search for
the information in particular.
47
Figure (11)-Gender-info seeking bar chart
Gender * info seeking internet use Crosstabulation
Count
info seeking internet use
1
Gender
Total
2
3
4
5
Total
Male
0
0
7
8
7
22
Female
1
1
2
15
9
28
1
1
9
23
16
50
48
Table (8) - Gender * info seeking internet use Crosstabulation
Figure (12) - Entertainment Internet Use Model
Peers on
Entertainment
.222
+ve
outcome
expectations
on
Entertainment
Gender
.166
-.167
.135
-.135
.100
Entertainment
Internet use
.022
Entertainment
Self Efficacy
.187
Experience
.072
Parents on
Entertainment
.094
.026
-.135
-.088
-ve
outcome
expectations
on
Entertainment
-.135
-.175
-.135
.233
49
Table (9) - Hypothesized paths of Information Seeking
Hypothesized paths of Entertainment
Expected
sign
H2a
Gender → Entertainment internet use
+
H2b
+
H2c
H2d
+ve outcome expectations → Entertainment internet use
Entertainment self efficacy → Entertainment internet
use
-ve outcome expectations → Entertainment internet use
H2e
H2f
H2g
H2h
H2i
Peers success on → +ve outcome expectations
Parents success → +ve outcome expectations
Peers success → Entertainment self efficacy
Experience → Entertainment self efficacy
Parents success → Entertainment self efficacy
+
+
+
+
+
H2j
Peers success → -ve outcome expectations
+
H2k
H2l
H2m
Parents success → -ve outcome expectations
+
+
Entertainment self efficacy → -ve outcome expectations
Significance
Level
-.135
.350
.166
.258
.022
.879
-.175
.222
.231
.128
.072
-.167
.187
-.280
.094
.617
.223
.175
.048
.513
.233
+
Entertainment self efficacy → +ve outcome expectations
Beta
.109
.162
+
.489
-.088
-
.541
The data fit the entertainment use model:
From
(β=
0.222)
relationship
it
between
shows
that
positive
there
outcome
is
a
significant
expectations
on
entertainment and the peers’ success influence independent
variable.
(β= -0.88) proves that it is logical that when Entertainment
self efficacy increases, negative outcome expectations for
entertainment decreases. The entertainment self efficacy was
not
that
significant
on
the
dependent
variable
of
entertainment internet use (β= 0.022). Also the negative
outcome expectations for the entertainment has a negative
50
Beta
coefficient
with
the
independent
variable
of
entertainment use of the internet (β= -.175) in which it
proves the hypothesis.
However, the mostly significant independent
variable on the entertainment self efficacy is the peers’ success influence like we
mentioned before and this is more explained in the discussion below as it is
influenced by different factors. The experience had little significance on the
entertainment self efficacy (β= .187) which means that teens rely
mostly on their peers to download images, games and videos
from the internet.
The gender differences in the entertainment model hadn’t
any significance on the entertainment independent variable
of using the internet (β= .135) which means that both
genders are engaged in using the internet for entertainment
the same attitude and direction.
Figure (13) - Entertainment –Gender Bar chart
51
Figure (14) - Social Networking Internet Use Model
.351
Peers on
Social
+ve
outcome
expectations
on
Social
.120
Gender
.071
-.291
-.135
.540
Social
use
.363
Social
Efficacy
.193
Experience
-.173
Parents on
Social
-.086
-.180
Self
Internet
-.135
.155
-.135
.024
-ve
outcome
expectations
on
Social
-.135
.072
Table (10) - Hypothesized paths of Information Seeking
Hypothesized paths of Social
Expected
sign
H3a
Gender → Social internet use
+
H3b
+ve outcome expectations → Social internet use
+
H3c
H3d
Social seeking self efficacy → Social internet use
-ve outcome expectations → Social internet use
+
+
H3e
H3f
Peers success on → +ve outcome expectations
Parents success → +ve outcome expectations
+
+
Beta
Significance
Level
-.291
.041
.071
.651
.363
.022
.204
.351
.129
.205
-.173
.012
.120
H3g
H3h
H3i
Peers success → Social self efficacy
Experience → Social self efficacy
Parents success → Social self efficacy
+
+
+
H3j
Peers success → -ve outcome expectations
+
H3k
H3l
H3m
Parents success → -ve outcome expectations
Social self efficacy → +ve outcome expectations
Social self efficacy → -ve outcome expectations
+
+
-
.193
-.180
-.086
.215
.184
.414
.561
.072
.624
.540
.000
.155
.282
52
The data fit the entertainment use model:
From (β= 0.363) it shows that there is a significant
relationship between the social self efficacy and the
independent variable of the Social internet use and this
proves our hypothesis more than the previous study in 2005.
However, the positive outcome expectations were of lower
significance in using the internet for social networking (β=
0.071). The influence of the peers’ success is well noted as
the majority has indicated that their peers influence them
for engaging in the social networking websites like Facebook,
twitter and youtube…etc.
Surprisingly, it is also noted that the increase in the social
self efficacy doesn’t decrease the negative outcome
expectations from using the social media websites as they
indicated in their questionnaire that they dislike that they
are deprived from their privacy and they also dislike the
requests from unknown people who would like to add them
in their profile (β= 0.155).
The experience also is of lower significance as (β= 0.193) on
the social self efficacy which indicates that prior experience
to using the social media websites doesn’t resemble any
significance and it is absolutely logical and this coincides
with the SCT of Bandura in which individuals observe their
53
colleagues to gain vicarious experience and that strengthen
the self efficacy.
There is a significant influence on the positive social
outcome expectation from the social self efficacy (β= 0.540).
In our study, the gender (β=- 0.291) was noticed to have an
interesting difference than the previous study of 2005. Males
were the ones more engaged in social media websites
significantly more than the females. This was the opposite in
the previous study of 2005.
54
Figure (15) – Gender-Social bar chart
Gender * social internet use Crosstabulation
Count
social internet use
2
Gender
3
4
5
Total
Male
0
1
6
15
22
Female
2
5
8
13
28
2
6
14
28
50
Total
Table (11) - Gender * social internet use Crosstabulation
Discussion
From these data, unlike the previous model that was tested
in 2005; support was found for the information seeking,
entertainment and social networking models of Internet use
for teens. This is not surprising as each of these uses is
considered a prominent and potentially difficult type of
Internet use that would require an individual to constantly
reevaluate his or her perceived ability from direct and
indirect (vicarious) experiences.
Just as expected, while social influence models such as peer
and parental success and experience influenced the initial
cognitive
mechanisms
of
self-efficacy
and
outcome
55
expectations, not all behaviors are influenced the same. For
example, peer success had a relatively substantial influence
on the entertainment expectancy measures but almost no
relationship in the information seeking and the social
model. However, Parental influences demonstrate a similar
picture, appearing only to impact negative expectancies in
the entertainment alone which concludes that peers are a
powerful influence on using the internet for entertainment,
positive expectancies in the information and social models,
and efficacy perceptions only in the information seeking
model. Although self-efficacy increases positive expectations
and decreases the likelihood of perceiving negative outcomes
from use in all three models, standardized coefficients
ranged from a +.540 to -.169.
These findings support the notion that the general construct
of Internet use as well as social and cognitive influences miss
important and substantial differences by behavior, and thus,
could provide misleading or incomplete results.
Furthermore, the bivariate and modeled relationships among
the
information
seeking
self-regulation
variables
and
information seeking behavior support the idea that rather
than
looking
directly
at
web
experience
and
recall
researchers should consider the mediating effects of selfefficacy when exploring variables such as cognitive load and
56
disorientation within the context of online learning (further
discussed below).
Although
parental
influence
is
present,
the
dominant
influence on self-regulation appears to come from peers
especially in the entertainment and social models; thus, we
return to potential Internet effects implications. In agreement
with Nathanson who found that peer mediation facilitates
antisocial
exposure
and
potential
negative
outcomes
of
media, it is possible that negative outcomes of certain
Internet behaviors such as self-removal from the information
society
due
to
perceived
inabilities
to
gather
relevant
information is in part attributed to peer influence as it
relates to self-efficacy development. If this is the case, this
project makes it possible to look at potential knowledge gaps
being created between groups of youths. What is commonly
understood as the digital divide could in fact be an efficacy
issue driven by social influence. Perhaps as youths observe
peers struggling to orient themselves to information online,
efficacy levels and subsequent use decrease.
Experimentally, this would position self-efficacy and its
development as
an
important
moderating component to
research investigating psychological divides as well as more
traditional models of communication such as information
processing of online information. For example, it has been
argued that disorientation while information seeking online
increases cognitive load, subsequently decreasing recall and
57
potential future use of the Internet as an information
resource.
Further Eveland and Dunwoody reasoned that performance
increases because level of expertise dulls “the impact of
complex Web designs” allowing users to focus on their task.
However, according to the current definition of self-efficacy
and previous findings that suggest increases in self-efficacy
levels increase use and performance in online environments,
the casual link being inferred between experience and
performance could have less to do with cognitive load and
more to do with online information seeking self-efficacy.
Driving this study was the idea that testing Internet use
within task specific models such as information seeking,
entertainment,
or
social
use
would
provide
greater
explanatory power than previous general use models. This is
not the case for any of the three models tested here. However,
considering that both direct and indirect relationships were
evaluated
in
each
model,
this
conclusion
needs
an
explanation. While LaRose et al. explained a relatively large
amount of variance in overall Internet use through multiple
regression analysis (R2 =0.60), the current study accounts for
significant
portions
of
variance
within
each
modeled
dependent measure.
Understanding that this comparison is not equivalent, it does
demonstrate a different picture of the three models and their
58
predictive
power.
Moreover,
the
three
models
produced
stronger bivariate relationships than the previously obtained
in past researches.
Our study however, proved the social model which was a
failure in the previous research of 2005 and that could be
attributed to different reasons. One of which is that at the
time of the previous research in 2005, the number of social
networking websites were not as huge as they are in 2010.
Facebook that started in 2006 is the most popular of use
among teens; and as we mentioned above, 100% of the (N=50)
use Facebook. This indicates how rapid the social media
growth among teens.
Finally, this study set out with the idea that people use the
Internet differently, for different reasons, and with different
influences. This is supported by the current data. As pointed
out, obvious and subtle differences can be observed among
the direct and indirect relationships within the three
models. Additionally, while the three uses were found to be
significantly
correlated
(r
information,
entertainment
=
0.14; r information, social = 0.21; r entertainment, social =
0.14), none of them reached what is considered substantial.
This suggests that individuals are customizing their time
online, which indicates general measures of use will fail to
capture accurately what influences online use and outcomes
from that use. Moreover, this would explain why researchers
measuring Internet use with a mixed bag of application
59
variables (i.e., time spend chatting or time spent playing
games) have had a hard time achieving high reliability
coefficients when creating a general use variable.
Limitations, Conclusion & Recommendations
Variables that should be considered when addressing
internet usage in Egypt:
1- Education
Educational attainment also factors into computer and
Internet use. The higher a person’s level of education, the
more likely he or she will be a computer or Internet user.
2- Income of household
Income of household could affect the availability & therefore
easy access of computers at home which could in turn reduce
usage.
3- Urban or Rural Location of the Household could affect
teen internet usage in Egypt (percentage, type, gender related
issues) either positively or negatively.
60
4- Language
One could point to metrics that suggest a predominance of
English language sites on the Internet. The Organization for
Economic Cooperation and Development, for example, reports
that more than 94 percent of links to pages on secure servers
were in English in July 2000 especially in commerce, not to
mention other Internet traffic (e-mail and other online
communications) which is also mainly in English. We
believe that this could also affect internet usage in Egypt.
It is widely known that the average amount of time spent on
the Internet is rapidly increasing, and that the starting age of
Internet users is steadily decreasing (Kraut et al, 1998, Nie &
Erbring, 2000). As time moves forward, the Internet is
becoming a larger factor in the lives of people at progressively
younger
ages.
Thus,
parents,
psychologists,
educators,
technology creators and lawmakers must become aware of the
potential risks and rewards of this phenomenon (Cooper,
2003, 127).
It is important to note that the findings of the current study
relate to teens aged 14 to 17 who live in Cairo which is the
most developed city in Egypt. These factors might limit the
generalizability of the findings to a wider population, such
as today’s Internet users. Future research on age differences in
individuals’ preferences for using the Internet technology
might prove fruitful. Consequently, because of the everchanging nature of the Internet, what we learn today may
61
not be valid a few years from now. Thus, ongoing research is
necessary to keep abreast of it.
Teen Internet Use Survey
This questionnaire is a part of the research for social networking in the Arab Academy Graduate
School of Business. The results of this survey will be used for academic purposes only. The
survey is anonymous and does not require any personal details to be submitted.
Estimated time for this questionnaire completion is 10-15 minutes. Aggregated research data
will be available for you upon survey completion.
The research team greatly appreciates your help and support with this research and thanks you
for your valuable contribution!
Get Started!
1. Your gender:
Male
Female
2. How often do you use the internet?
62
Every day
More than
Once a
Once a
Less than
once a week
week
month
once a month
3. If you use it every day how many hours do you use it for?
Less than
1–2
2–3
3–4
More than
1 hour a day
hours
hours
hours
4 hours a day
4. How much you expect good things from using the internet? Tick ( ) for each
Somewhat
Very Much
5. How much you expect bad things from using the internet? Tick ( ) for each
Somewhat
Not at all Not Really
Undecided
Very Much
Not at all
Not Really
Undecided
Having fun
Increase my knowledge
Get updated info about celebrities
(singers, actors, football players)
Find old friends
Join new networks
It takes too long to download
games& music
Difficulty in finding useful
information
Too much information on the
internet
Privacy issue
Friendship requests from unknown
63
people online
6. State how much you agree or disagree with following statements
Strongly Disagree
Disagree Neutral
Agree
Strongly Agree
I can easily use computers
I can easily locate information on the
net
I can download favourite music,
videos & games
I can easily learn advanced skills
Make new friends on the net
Chat& communicate with old
friends
7. Do you have your profile on any the following social networking (SN) services (you can
choose several choices)
Facebook
LinkedIn
Twitter
Bebo
Youtube
Blogger.com
Myspace
Flikr
Other
64
8. How many connections (“Friends”) you have for your Social Networking profile (on
average):
Less than 10
10-49
50 –99
100+
200+
9. Please rate from 1 to 5 what are the main reasons for using the internet?
1 – do not
use
5 – this is my
2 – use very 3 – use quite
primary way
4 – use often
rarely
often
to use these
services
Find some
information
Get opinions
Entertain yourself
Socialize
Stay up-to-date
with friend’s life
65
10. Methods of how you learn to use the internet are by: (Please Tick ( ) in front of whichever
is applicable)
Sr. No.
Method
1.
Trial and error method
2.
Guidance from colleagues and friends (peers)
3.
Guidance from your parents
4.
Self instruction
5.
External courses
11. Do you trust information you obtain via social networking websites?
Yes
Yes if it comes from my friends/connections
Yes if comes from professional communities
Yes if it comes from company official profiles/pages
No, I’m always critical to such kind of information and check other sources
12. What troubles you the most while using the Internet? (Please tick ( ) all that apply)
a)
Slow access speed
b)
Difficulty in finding relevant information
c)
Overload of information on the Internet
d)
No trouble at all
e)
Privacy problem
f)
Any other________________________________________
66
13. In your opinion, using Internet as compared to use of conventional documents is: (Tick () all that
apply)
Time saving
or
Time consuming
More informative
or
Less informative
More expensive
or
More useful
Less expensive
or
More preferred
or
Less useful
Less preferred
14. Are your parents successful on how to use the internet?
Yes
No
15. If your answer is yes, to what extent has this affected you?
Not at all
Not Really
Undecided
Somewhat
Very Much
16. Are your peers (colleagues) at school successful in using the internet?
Yes
No
17. If your answer is yes, to what extent has this affected you?
Not at all
Not Really
Undecided
Somewhat
Very Much
18. Which of the advices you consider the best for searching information using the internet?
Parents
Peers
67
19. Which of the advices you consider the best for using social networking websites?
Parents
Peers
20. Which of the advices you consider the best for using entertainment websites?
Parents
Peers
References
Baker, Eveleth & Stone, Robert W. (2008). Expectancy Theory & Behavioural
Intentions
to
Use
Computer
Applications.
Interdisciplinary
Journal
of
Information, Knowledge& Management. Retrieved March 15, 2010
Bonebrake, K. (2002). College students’ internet use, relationship formation,
and personality correlates. CyberPsychology & Behavior, 5, 551-557.
Campbell, A.J., Cumming, C.R. & Hughes, I. (2006). Internet use by the
socially fearful: Addiction or therapy? CyberPsychology & Behavior, 9, 6981.
Cohen, S. & Willis, T. (1985). Stress, social support and the buffering
hypothesis. Psychological Bulletin, 98, 310-357.
Cooper, N.S. (2003). The identification of psychological and social correlates
of Internet use in children and teenagers . Unpublished doctoral thesis,
Alliant International University, California.
Cummings, J.N., Sproull, L. & Kiesler, S.B. (2002). Beyond hearing: Where
real-world and online support meet. Group Dynamics: Theory, Research and
Practice, 6, 78-88.
68
Deloitte-Turkiye (2008). Turkiye’de İnternet kullanıcıları medyayı nasıl
tuketiyor? [How internet users consume media in Turkey?] Deloitte Touche
Tohmatsu.
Retrieved
from
http://www.deloitte.com/dtt/cda/doc/content/Turkeytr_tmt_MediaSurveyBooklet_250608.pdf .
Eastin, Matthew S. (2005). Teen Internet Use: Relating Social Perceptions and
Cognitive Models to Behavior. Cyber psychology & Behaviour. Retrieved
February
1,
2010
from
http://web.ebscohost.com/ehost/pdf?vid=1&hid=6&sid=1065c357-4f2b465f-a71c-5e35151a2d3d%40sessionmgr13
Engelberg, E. & Sjoberg, L. (2004). Internet use, social skills and adjustment.
CyberPsychology & Behavior, 7, 41-47.
Glanz et al, (2002). Social Cognitive Theory. University of Twente. Retrieved
February
1,
2010
from
http://www.cw.utwente.nl/theorieenoverzicht/Theory%20clusters/Interpersona
l%20Communication%20and%20Relations/Social_cognitive_theory.doc/
Gross, E.F., Juvonen, J. & Gable, S.L. (2002). Internet use and well-being in
adolescents. The Society for the Psychological Study of Social Issues, 58 (1) ,
75-90.
Hanway, Steve. (2003). Minority Teens Less Likely to Socialize Via Web. The
Gallup
Organization.
Retrieved
March
15,
2010
from
http://www.gallup.com/poll/tb/educaYouth/20030610.asp?Version=p
Hillier, L. & Harrison, L. (2007). Building realities less limited than their
own: Young people practising same-sex attraction on the internet. Sexualities,
10, 82-100.
Kraut, R., Patterson, M., Lundmark, V., Kiesler, S., Mukopadhyay, T. &
Scherlis, W. (1998). Internet paradox: A social technology that reduces social
69
involvement and psychological well-being? American Psychologist, 53,
1017-1031.
Kraut, R., Kiesler, S., Boneva, B., Cummings, J.N., Helgeson, V. & Crawford,
A.M. (2002). Internet paradox revisited. Journal of Social Issues, 58, 49-74.
LaRose, Robert; Eastin, Matthew S.; & Gregg, Jennifer. (2001). Reformulating
the Internet Paradox: Social Cognitive Explanations of Internet Use and
Depression. Journal of Online Behaviour. Retrieved March 17, 2010 from
http://www.behavior.net/JOB/v1n1/paradox.html
LaRose, Robert & Eastin, Matthew S. (2004, September). A Social Cognitive
Theory of
Internet Uses and Gratifications: Toward a New Model of Media Attendance.
Journal of Broadcasting & Electronic Media. Retrieved February 1, 2010
from
http://heinonline.org/HOL/LandingPage?collection=journals&handle=hein.jo
urnals/jbem48&div=29&id=&page=
Lenhart, Amanda; Purcell, Kristen; Smith, Aaron; & Zickuhr, Kathryn.
(2010). Social Media & Mobile Internet Use Among Teens& Young Adults. Pew
Research
Center.
Retrieved
March
15,
2010
from
http://pewinternet.org/Reports/2010/Social-Media-and-Young-Adults.aspx
Lavin, M., Marvin, K., McLarney, A., Nola, V., & Scott, L. (1999). Sensation
seeking and collegiate vulnerability to Internet dependence. CyberPsychology
& Behavior, 2, 425–430.
Morahan-Martin, J., & Schumacher, P. (2000). Incidence and correlates of
pathological Internet use among college students. Computers in Human
Behavior, 16, 13-29.
NTIA
(National
Telecommunications
and
Information
Administration).
(2002). A nation online: how Americans are expanding their use of the
70
Internet.
Retrieved
March
15,
2010
from
www.ntia.doc.gov/ntiahome/dn/html/anationonline2.htm
Nie, N., & Erbring, 1. (2000). Internet and society: a preliminary report.
Stanford: Stanford Institute for the Quantitative Study of Society.
Odell, P.M., Korgen, K.O., Schumacher, P. (2000). Internet use among female
and male college students. CyberPsychology & Behavior, 3, 855–862.
O’Toole, K. (2000). Study offers early look at how Internet is changing daily
life
.
Stanford
News.
Retrieved
from
www.stanford.edu/dept/news/pr/00/000216internet.html
Pajares (2002). Overview of social cognitive theory and of self-efficacy.
Retrieved
month
day,
year,
from
http://www.emory.edu/EDUCATION/mfp/eff.html
Perse, E.M. & Ferguson, D.A. (2000). The benefits and costs of web surfing.
Communication Quarterly, 48, 343-359.
Russell, D. (1996). UCLA Loneliness Scale (Version 3): reliability, validity,
and factor structure. Journal of Personality Assessment, 66, 20-40.
Schumacher, P. & Morahan-Martin, J. (2001). Gender, Internet and computer
attitudes and experiences. Computers in Human Behavior, 17, 95–110.
Shaw, L.H. & Gant, L.M. (2002). In defense of the Internet: The relationship
between internet communication and depression, loneliness, self-esteem and
perceived social support. CyberPsychology & Behavior, 5, 157-171.
Stoll, C. (1995). Silicon Snake Oi l. New York: Anchor Books.
Tavsancıl, E. & Keser, H. (2002). İnternet kullanımına yonelik likert tutum
olceğinin gelistirilmesi [Development of a Likert type attitude scale for
internet using]. Journal of Educational Science and Applications, 1 (1), 79-
100.
71
Turkle, S. (1996). Virtuality and its discontents: Searching for community in
cyberspace. The American Prospect, 24, 50-57.
Weiser, E.B. (2001). The functions of internet use and their social and
psychological consequences. CyberPsychology & Behavior, 4, 723-743.
Widyanto, L. & McMurran, M. (2004). The psychometric properties of the
Internet addiction test. CyberPsychology & Behavior, 7, 443-450.
Whitty, M.T. & McLaughlin, D. (2007). Online recreation: The relationship
between loneliness, Internet self-efficacy and the use of the Internet for
entertainment purposes. Computers in Human Behavior, 23, 1435–1446.
Yao-Guo, G., Lin-Yan, S. & Feng-Lin, C. (2006). A research on emotion and
personality characteristics in junior high school students with internet
addiction disorders. Chinese Journal of Clinical Psychology, 14, 153-155.
72