Socioeconomic status influences Turkish digital natives internet use habitus

Behaviour & Information Technology
ISSN: (Print) (Online) Journal homepage: https://www.tandfonline.com/loi/tbit20
Socioeconomic status influences Turkish digital
natives’ internet use habitus
Mustafa Kerem Kobul
To cite this article: Mustafa Kerem Kobul (2023) Socioeconomic status influences Turkish
digital natives’ internet use habitus, Behaviour & Information Technology, 42:5, 624-642, DOI:
10.1080/0144929X.2022.2034970
To link to this article: https://doi.org/10.1080/0144929X.2022.2034970
Published online: 10 Feb 2022.
Submit your article to this journal
Article views: 692
View related articles
View Crossmark data
Citing articles: 3 View citing articles
Full Terms & Conditions of access and use can be found at
https://www.tandfonline.com/action/journalInformation?journalCode=tbit20
BEHAVIOUR & INFORMATION TECHNOLOGY
2023, VOL. 42, NO. 5, 624–642
https://doi.org/10.1080/0144929X.2022.2034970
Socioeconomic status influences Turkish digital natives’ internet use habitus
Mustafa Kerem Kobul
Faculty of Economics, Administrative and Social Sciences, Department of Translation & Interpreting, Samsun University, Samsun, Turkey
ABSTRACT
‘: Digital native’ has been a buzzword within the last decade particularly in educational research.
Learners born after the 1980s are presumed somehow different just because they were born in a
digital world. Although scholars have labelled these young generations as digital natives or
millennials as a homogenous construct, the questions of how and for what purpose(s) these
younger generations use these newer technologies remain unresolved. Acknowledging the
abundance of literature documenting adults’ viewing their younger ones different throughout
history, this study investigates the relationship between socioeconomic level variables, paternal
and maternal education level, household income, and their internet use frequency and
purposes. Data were gathered from 327 undergraduate students, born between 1997 and 2000,
enrolled at a Turkey state university. Findings indicate that maternal and paternal education
levels significantly influence the internet use duration of the younger generation, yet observe
no significant effect on household income. The results also reveal significant differences in
participants’ internet use for academic and a variety of non-academic purposes concerning
socioeconomic status variables. Overall, simply being born into a technological world does not
make the younger generation a monolithic group of tech-savvy individuals as their adults
assume them to be.
1. Introduction
The controversy that has permeated society over adults’
seeing the young generation as different and stereotypic
has been an age-old concern and still raged unabated for
centuries. Looking across the now vast literature, wellarticulated arguments of adults criticizing (Considine,
Horton, and Moorman 2009), blaming, even accusing
(Turiel 2002) their contemporary ‘newer’ generation
would be profoundly found. Extant literature dates
this aspect back to Socrates’ time with his lament:
‘Our youth now love luxury. They have bad manners,
contempt for authority (and) disrespect for older
people. They contradict their parents and tyrannize
their teachers’ (Mattoon 2005, 45). Accordingly, Berliner and Biddle (1995) coined this phenomenon as ‘the
Socrates Legacy’. More recently, Turiel (2006) also highlights that ‘During the 1920s, much concern was
expressed in the United States regarding the moral
state of youth, cultural disintegration, and social
chaos’ (799). By and large, history presents a bunch of
dismays or stereotypical conceptions of the younger
generations (Buckingham 2007).
Ariés (1965), in his earth-shattering work, highlights
that ‘childhood’ is no more than an adult perception of
ARTICLE HISTORY
Received 20 August 2020
Accepted 21 January 2022
KEYWORDS
Digital native; internet use;
socioeconomic status;
mother education level;
father education level;
household income
the modern world, or put differently, a modern adult
mind’s artifact which matured in centuries. Postman
(1985) in another canonical tract remarks that ‘For the
past 350 years, we’ve been developing and refining this
conception of childhood’ (295). Correspondingly, there
is a myriad of literature available suggesting that younger
generations were exposed to, if not suffer, their adults’
perceptions, intuitions, beliefs, assumptions, stereotypes,
sometimes prejudices about them, particularly in educational settings (Byrne 2005; Carrell and Wise 1998;
O’Keeffee, McCarthy, and Carter 2007; Postman 1985;
Turiel, 2007). A considerable amount of research lends
support to this phenomenon. To illustrate, Short 2008
criticises children’s literature as he remarks ‘There are
books, however, that children reject as nostalgic or sentimental because they reflect adult perspectives of looking
back on childhood or portray adult emotions of cynicism
and despair’ (95). Thus, these and various other extant
writings signal that these adult perceptions of children
do not always reflect reality to a certain extent. Accordingly, the digital native notion might also be an adult perception rather than a reflection of reality.
However, not all impressions of young generations
have been negatively loaded. Particularly after the
CONTACT Mustafa Kerem Kobul
kerem.kobul@samsun.edu.tr, mkeremkobul@hotmail.com
Samsun University, Faculty of Economic, Administrative
and Social Sciences, Department of Translation and Interpreting, Samsun, Turkey
This article has been republished with minor changes. These changes do not impact the academic content of the article.
© 2022 Informa UK Limited, trading as Taylor & Francis Group
BEHAVIOUR & INFORMATION TECHNOLOGY
unprecedented growth of new technologies and their
ubiquitous use within the last few decades, positive attributions have also been directed toward the new generation (Twenge 2018). Correspondingly, some labels
were attached by scholars, such as x, y, or z generations,
millennials, the net generation whose members had
some peculiarities that their contemporary adults presumably lacked (McCrindle and Wolfinger 2009).
Although it is difficult to set a clear borderline between
the so-called x, y, z generations, the generic term ‘digital
natives’ was coined by Prensky (2001) in his seminal
treatise. These digital natives are considered natives,
not immigrants, to the new technological and digital
world, they are born in. Moreover, they are believed
to have some traits that make them more competent
in technology use. Presumptively, these remarks about
this new generation have perennially impregnated the
society and the scholarly publishing.
2. Literature review
2.1. Digital natives
A cornucopia of definitions and the characteristics of
these so-called digital natives have been put forward
by numerous scholars for years. In their essence, these
new labels suggest that new millennials (Gee 2015;
Howe and Strauss 2000), digital natives (Prensky
2001), the Net generation (Tapscott 1999), Zippies or
Generation Z (McKay 2004) are assumed to differ
from earlier generations of boomers and Xers
(Zemke, Raines, and Filipczak 1999) in numerous
aspects. Firstly, they are more technologically savvy consider modern technology as a standard or norm rather
than an exception or privilege (Blass and Davis 2003).
Secondly, they are more open to team work and collaboration, prefer discovery learning (Flowerdew 2009)
and prefer technology integrated and anytime anywhere
learning (Viswanathan 2009) rather than ‘traditional
classroom structure, in which they sit motionless, take
notes while the teacher lectures, and speak only when
they are spoken to’ (Li 1998, 691). More specifically,
they prefer typing to handwriting (Oblinger 2003), use
technology ‘for socialization and for text-based communication with their friends and classmates’ (Wang
and Heffernan 2009, 474), like experiential learning
(Bennett, Maton, and Kervin 2008), and they are used
to ‘receiving information quickly’ (MacLean and
Elwood 2009, 158). Furthermore, they prefer reading
on screen more than hardcopy print (Baron 2015),
and have shorter attention spans (Zhang and Bonk
2009). Last but not least, they are considered a monolithic group of proficient users of all these readily
625
available new technologies already integrated into
their lives. On the whole, these and some other
peculiarities allegedly make them different from the earlier generations.
Conversely, these assumptions have attracted criticism and scorn founded on the premise that they
might be nothing more than mere adult perceptions of
youth at a theoretical level. Furthermore, it is claimed
that they are not that tech-savvy (Bennett, Maton, and
Kervin 2008; Warschauer 2001). Chun (2011) rejects
these assumptions by remarking ‘simply growing up surrounded by technology does not ensure that they will be
effective communicators in online realms, just as growing up in a print world did not automatically make one
a good reader and writer’ (665). Even more surprisingly,
there is ample evidence suggesting that although these
digital natives were born in a digital and high-technology
world, they do not use these technologies for pedagogical, learning or academic purposes (McQuiggan et al.
2015; Tam 2017; Thornton and Houser 2005; Viswanathan 2009). Correspondingly, Farooq and Javid
(2012) in a recent study conducted with Saudi Arabian
university students enrolled in Information Technology
and Engineering department report that ‘majority of the
students have access to computer and internet but they
are not motivated to use technology in their learning’
(17). Likewise, a similar conclusion emerged from
Crook and Barrowcliff’s (2001) work, demonstrating
that ‘ready computer access does not significantly
remediate study practices itself. When students were
engaged in private study, those with networked room
PCs were no more likely to report using the technology
for their study than those without this resource’ (253).
The primary aim of their technology use seems mainly
to participate in the social media (i.e. facebook, instagram, twitter, snapchat, Whatsapp), reading e-mail,
surfing on the internet, gaming and check their mailbox,
sometimes even excessively (De Abreu and Goes 2011;
Klopfer 2008; Ravizza, Uitvlugt, and Fenn 2017; Shin
2017; Van Looy 2016; Viswanathan 2009; Young
2017). Even on-line studies that observe students’ live
internet traffic reveal that they use these technologies
mainly for social networking more than learning purposes (Ravizza, Uitvlugt, and Fenn 2017). Furthermore,
some addiction studies highlight that some young adults
use internet technology to help alleviate psychopathologies, such as compulsive behaviour, social phobia,
depression, loneliness, negative thinking etc., while
forming new pathologies of addiction (Beard 2011; De
Abreu and Goes 2011). Thus, the end-product mostly
is not for the benefit of younger generation. By and
large, serious voices have been raised against assuming
this younger generation as tech-savvy and adept in
626
M. K. KOBUL
using all kinds of technology simply because they were
born in a technological or digital world.
More recently, the homogeneity of ‘the digital
natives’ as a single, monolithic or uniform generation
per se has been duly questioned (Hague and Williamson
2009; Pegrum 2011), particularly about the threshold
age posited by Prensky (2001), who considered all
born after 1980 as the digital natives. However, much
criticism was levelled, particularly at the claims regarding this supposed homogeneity of the digital natives. On
the one hand, Pegrum (2011) draws attention to possible variations among these digital natives as ‘a growing
body of research shows that factors like gender, race,
language, geographic location, socioeconomic status,
and education level complicate easy assumptions
about young people’s access to and use of technology’
(10). On the other hand, as the years passed on, exponential growth of new technologies advanced and
newer generations are also inducted in these ever-evolving newer technologies every day. In line with this,
Helsper and Eynon (2010) remark that ‘Arguably the
rise of Web 2.0 applications might have created a second
generation of digital natives, which can be separated
from the first due to its familiarity and immersion in
this new, Web 2.0, digital world.’ (508). Nevertheless,
by the time their study was published Web 2.0 was
very common and Web 3.0 or semantic Web, which
provides contextual information as artificial intelligence, had newly arrived and is getting more common.
Thus, it would not be unwise to think of an incoming
third generation of digital natives. In this regard,
defining a monolithic group of digital natives is likely
to be an unceasing difficult enterprise for the scholarly
world.
2.2. Socioeconomic status (SES)
Socioeconomic Status (SES) has received constant scholarly attention as one of the most investigated confounding variables in social studies, along with age and gender
(Chambers 2004). Although there is a myriad of published studies, a consensus on what SES per se is and
how it can or should be measured seems far to be reached
so far (Bradley and Corwyn 2002; McLoyd 1998; Sirin
2005). On the other hand, this incessant wrangling situation has made it one of the most fertile grounds for
researchers. Some scholars argue that this controversy
results from the variety of social structures and/or stratification in different societies (Bourdieu 1984; Bradley
and Corwyn 2002; Coulmas 2013; Hofstede, Hofstede,
and Minkov 2010; Mueller and Parcel 1981), while
some others argue for cultural components (Hofstede,
Hofstede, and Minkov 2010; Miller and Caubet 2009).
On the whole, the exact definition or measurement of
SES has never been attested to the satisfaction of the
scientists.
However, looking across the literature on SES, it has
been consistently confirmed that researchers tend to
overlook the SES aspect and not much seems to have
changed since Mueller and Parcel’s (1981) lament that
‘there appears to be a heavy reliance on impressionistic
criteria in identifying SES levels’ with expressions such
as ‘Subjects were middle-class white children’ (14). A
portrait depicts as if all the participants constituted a
single homogenous group (Bottero 2004; Miller and
Caubet 2009; Mugler and Benton 2009). Correspondingly, numerous studies still use the terms loosely as
‘upper, middle or lower socioeconomic level or social
class’ (Garton and Pratt 2009; Taner and Başal 2005).
Within most of the published work, the researchers
tend to use the terms even without explicating what
they refer to and how they are classified as so
(Guisande et a., 2007; Taner and Başal 2005) or use
measures whose reliability and validity regarding the
comprehensiveness can be easily questioned (Gelbal
2010; Karatay and Kubilay 2004; Mueller and Parcel
1981). SES and social class are mostly used interchangeably in educational research (Bernstein 2003; Goldenberg 2011; Heath 1983; Keating and Egbert 2004;
Pachler, Bachmair, and Cook 2010). In such a convoluted construct like socioeconomic level, which carries
so many complex concerns in various respects, such
overlooking or untied mentions of the variable might
offer some challenges and limitations for the
researchers.
Existing attempts to suggest an exhaustive socioeconomic status measure with regard to the conceptual
meaning and definition have yet to provide a simple,
singular, or cogent instrument to the satisfaction of
the scholarly world. Although a good many otherwise
viable theories have been offered on the aspects to be
considered, most of seminal studies take income, occupation and educational level as main variables to identify SES of the participants in research (Bradley and
Corwyn 2002; Coulmas 2013; Marks 2016; Sirin 2005).
More broadly, some other variables, such as family
type (Caro and Cortés 2012; Marks 2016), minority
and immigrant status (Hull and Hernandez 2008),
single parenthood (Marks 2016), crowding in the household and the number of siblings present (Evans, Maxwell, and Hart 1999), have also been discussed as
variables contributing to the overall SES within the
extant literature. On the whole, as Coulmas (2003)
asserts it, ‘Income, profession, and educational level
are most commonly used’ (567) components of the
SES paradigm. However, they all have encountered
BEHAVIOUR & INFORMATION TECHNOLOGY
virulent criticism on various grounds from the instablity
of the income (Mueller and Parcel 1981) going through
the issue of whose of the spouse’s social status to be considered (Bradley and Corwyn 2002; Mueller and Parcel
1981) even further to the reliability (Rossi et al. 1974) of
the raters of the vignettes used in the measures per se
and the issue of interrater reliability concerns (Cirino
et al. 2002; Mueller and Parcel 1981). McLoyd (1998)
argues that even the validity of the official poverty
index per se receives serious criticism. Having considered all these aspects, parental educational level
(both mother and father education level) and household
income were taken as measures of socioeconomic status
in this study. However, the occupation was not chosen
as a measure because serious concerns and doubts are
already voiced on its inconsistent (Macionis 2012),
unstable and culturally variant nature (Coulmas 2013;
Miller and Caubet 2009) along with rater reliability concerns (Mueller and Parcel 1981).
More crucially, there is a regrettable lack of comprehensive and coherent occupational prestige index
for Turkey social milieu that will quench the thirst of the
scholarly world. However, there are various indexes for
Western society such as Hollingshead’s (1975) Four-factor Index of Social Status, Duncan’s (1961)Socioeconomic Index of Occupations and the Siegel’s (1971)
Prestige Scale, the Socioeconomic Index for Occupations in Canada (Blishen, Carroll, and Moore 1987),
and the International Socio-Economic Index of Occupational Status (Ganzeboom, De Graaf, and Treiman
1992).
In line with these arguments, Coulmas (2013) culturally questions the practicality of the term as ’analyzing
social systems outside the world of Western industrialized countries cannot be taken for granted’ (567).
Accordingly, there is a foregoing discussion that Turkey’s
occupational structure is not similar to Western countries
as unregistered working force is common in Turkey, i.e.,
there is a highly considerable amount of non-farmer
workers, who are informally employed and not enrolled
in any social security system (Demiral 2016; Kaya 2007;
Sirin 2005; Sunar 2018). Moreover, it is further claimed
that Turkey could be considered even as a ‘classless and
unpriviledged’ society (Kalaycioglu, Celik, Celen & Turkyilmaz, 2010). More broadly, Sunar (2018) laments
that there is a dearth of work on social stratification
and mobility, which will help set foundations at theoretical and conceptual grounds in Turkey.
The impact of Socio Economic Status (SES) on various grounds of human life has been duly noted within
the extant literature. Current literature of SES provides a
vast range of grounds, such as parenting practices
(Baron 2015; Christie 2010; Evans, Schoon, and Weale
627
2012; Gadsden 2000; Grabe 2009; Garton and Pratt
2009; Okagaki and Sternberg 1993; Pressley 2006), literacy practices (Gee 2015; Heath 1983; Warriner
2011), health (Giles and Billings 2004; Walters 2012),
psychological and social living conditions (Bottero
2004; Eisenberg 2013), culture (Bourdieu 1984; Garton
and Pratt 2009; Hofstede, Hofstede, and Minkov
2010), consumer behaviour (Slama and Tashchian
1985; Vigneron and Johnson 1999; Woodward and
Tunstall-Pedoe 1999), academic achievement (Christie
2010; Bradley and Corwyn 2002; Caro and Cortés
2012; Hull and Hernandez 2008; Keating and Egbert
2004; Marks 2016; Oakes 2005), language acquisition
(Ellis and Robinson 2008), language behaviour (Collins,
Peters, and Watt 2011; Clancy 2010; Giles and Billings
2004; Regan 2013), language teaching (Barkhuizen
2004; Ricento 2010), teaching reading in L1 and L2
(Goldenberg 2011; Grabe 2009; Wigfield and Asher
1984), stressors (Jargowsky 1994). Considering all
these published studies, there is almost a commonsense assumption that there is an interaction between
SES and degree of access to the available social, educational, and material resources of the society(Bourdieu
1984). Put more aptly; the higher socioeconomic status
one has, the better their opportunities in reaching the
resources, opportunities, assets, or commodities of the
society they live in (Bottero 2004).
2.3. Research questions
Taking all the notions and variables mentioned above
into consideration, the research questions posed at the
outset of this study are the following:
1. What technological devices do the participants use in
accessing the internet?
2. Does the Internet use frequency of participants differ
with respect to constituent components of socioeconomic status (a) maternal education level (b)
paternal education level (c) household income?
3. Does Internet use purposes of participants differ
with respect to constituent components of socioeconomic status (a) maternal education level (b)
paternal education level (c) household income?
3. Methodology
3.1. Nature of the study
In this study, a cross-sectional quantitative descriptive
research methodology was used to provide an accurate
profile of EFL undergraduate prep year students’ mobile
628
M. K. KOBUL
technology use behaviour along with diverse associational data. In broad terms, descriptive research is considered as an aid to provide an accurate and undistorted
portray of reality and to fathom out the phenomenon
that is being researched (Fraenkel and Wallen 2006;
Porte 2010; Ruane 2005). De Vaus (2002) remarks
‘descriptive research plays a key role in highlighting
the existence and extent of social problems, can stimulate social action and provide the basis of well-targeted
social policy interventions’ (23). More specifically, Chapelle (2008) highlights that ‘In technology studies for
language learning, like other areas of educational technology, the foremost research issue is to understand
how learners work with technology for learning, and
therefore descriptive research has been a priority’
(590). However, numerous scholars lament that there
is a dearth of descriptive research in education discipline regarding the actual classroom situations and student behaviour and habits (Chapelle 2008; Stern 1991;
Richards and Schmidt 2013).
3.2. Sampling & data collection
Inititally a convenience sample of 336 undergraduate
students enrolled in an English language preparatory
programme at a university on the North-East coast of
Turkey was recruited for this study. The participants
were learners of English as a foreign language (henceforth, EFL). Eleven participants were excluded from
the final dataset due to incomplete questionnaire
items, which caused some missing data. Thus, the number of students who voluntarily participated in this
study amounts to 327 (196 female, 131 male) students.
The students were following the English as a foreign
language preparatory programme to attend the mechanical engineering departments, geophysical engineering,
international relations and English language and literature in the following academic year during the spring
semester of 2018–2019 academic calendar. The participants were mainly born between 1997 and 2000. Furthermore, the survey method, as the most common
descriptive methodology (Fraenkel and Wallen 2006;
Jackson 2012; Mackey and Gass 2015) was used. Volunteer participants were invited to fill in a self-report, penand-paper survey questionnaire at the end of the class.
Students were handed out a self-report, pen-and-paper
survey questionnaire to fill in. The items (4-point and
5-point Likert-type) in the survey questionnaire were
developed, compiled and adapted from various studies
on mobile learning of different cultures (Farooq and
Javid 2012; Thornton and Houser 2005). All procedures
followed in the study comply with the ethical standards
of
the
responsible
committee
on
human
experimentation (institutional and national) and with
the Helsinki Declaration of 1975, as revised in 2008.
Informed consent was received from the participants,
who were fully informed about the purpose and nature
of the study and their right to withdraw at any time
from the study without any penalty, harm or loss of
benefits. They were also ensured of complete confidentiality and protection of anonymity. The participants
were not asked for any identifying information. Participants were thanked for their contributions.
4. Findings
The data were analysed using SPSS V. 23. Several descriptive (frequency, percentages, means, and standard deviations) and inferential statistics (chi-square test of
goodness-of-fit, chi-square test, Kruskal–Wallis-H test)
were computed to analyse the gathered data (Jackson
2012; Larson-Hall 2015). Descriptive statistics revealed
that most mothers (41.5%) were elementary school and
below, 20.5% were secondary school graduates, 27.2%
finished high school, and 10.4% had undergraduate and
above degree, as degrees get higher, the number of
mothers decreases. On the other hand, 23.5% of the
fathers were elementary school and below, 18.7% secondary school, 37.3% of the fathers were high school graduates and 18.9% had an undergraduate and above degrees
(Figure 1). This signals that fathers had higher education
levels than mothers in general.
The first research question explored the technological
devices participants use in accessing the internet. All
participants had owned smart phones. Moreover,
15.6% self-reported having desktop computers, while
74.6% had laptops. Put differently, 84.4% do not have
desktops, while 25.1% do not have laptop computers
(Figure 2). Furthermore, a one-way goodness-of-fit
chi-square test was implemented to test whether mobile
device preference of undergraduate EFL learners
differed statistically from a known distribution such as
normal distribution (Gries 2013; Larson-Hall 2015).
The test was conducted to see if the participants used
these devices more than would be predicted by chance
(Larson-Hall 2015) or due to sampling error (De Vaus
2002). It was assumed that all the devices would be
equally preferred, which means a 25% chance of preferring each one (laptop, smartphone, tablet, and other).
However, findings of the test indicate that the difference
between observed and predicted counts of preferences
was significantly higher than what might be expected
by chance (χ2 = 326.00, p < .05). Participants used laptops and smartphones significantly more than expected
by chance. This result is in line with Turkish Statistical
Institute’s (2018) latest comparable data, which reveal
BEHAVIOUR & INFORMATION TECHNOLOGY
629
Figure 1. Participants’ parental education level.
that the availability ratio of laptop computers in households is lower than that of smartphones.
In pursuit of answering the second research question
that reads: ‘Does Internet use frequency of participants
differ with respect to constituent components of socioeconomic status: (a) maternal education level (b) paternal
education level (c) household income?’, chi-square tests
were implemented. Chi-square tests are used to analyse
categorical data (Cohen, Manion and Morrison 2007;
Fraenkel and Wallen 2006; Lazaraton 2005).
Figure 2. Participants’ technological device ownership.
4.1. Relationship between internet use frequency
and socioeconomic status
A chi-square test was conducted to check an association
between participants’ internet use and their maternal
education level. Table 1 shows the relation between
these variables was significant (χ² = 30.25, df = 6,
p < .05), yet the contingency coefficient level was not
large (C = .29). Internet use duration of participants
whose mothers were university graduates is significantly
higher than what would be expected by chance, while
630
M. K. KOBUL
Table 1. Chi-Square test results of the relationship between internet use frequency and maternal education level.
Mother education level
Internet use frequency
n
%
4–6 h
n
%
7 h or more
n
%
n
Total
%
χ² = 30.25 df = 6 p = .001 C = .29.
3 h or less
Elementary school or less
Secondary school
High school
University and post-graduate
Total
59
43.4
57
41.9
20
14.7
136
100
15
22.4
28
41.8
24
33.8
67
100
30
33.7
36
40.4
23
25.8
89
100
4
11.8
12
35.3
18
52.9
34
100
108
33.1
133
40.8
85
26.1
326
100
internet use of participants whose mothers finished
elementary school or did not go to school was less likely
to use the internet for long hours.
The relationship between participants’ internet use
and their paternal education level was investigated by
a chi-square test. Table 2 shows the relationship
between participants’ internet use and their paternal
education level was significant (χ² = 15.39, df = 6,
p < .05), while contingency coefficient level was not significant (C = .21). The results revealed that participants,
who had fathers with high school or university degree
education level, used the internet for 7 or more hoursi.e. used the internet significantly more than expected.
However, the internet use of the participants, whose
fathers were elementary school or secondary school
graduates, did not differ radically.
The relationship between participants’ internet use
frequency and their household income level was determined according to Turkish Statistical Institute’s
(2018) classification, and a chi-square analysis was computed. The test results revealed no significant relationship between these two variables (χ² = 7.81, df = 8,
p > .05). Put differently, participants’ internet use frequency did not reveal statistically significant difference
concerning their household income level (Table 3).
4.2. Relationship between internet use purposes
and socioeconomic status
Turning now to the third research question, which aims
to determine whether internet use purposes of
participants differ with respect to constituent components of socioeconomic status (a) maternal education
level (b) paternal education level (c) household income?
The participants in the study were asked to indicate
their internet use purposes i.e. education, entertainment, communication and online shopping, other purposes, and frequency on a 5-point Likert scale from 1
(Never) to 5 (Always). Based upon the results of a
normality test on the data, Kruskal–Wallis H tests
were performed to explore whether the internet use purposes of the participants differed significantly with
respect to their mother education level. A p-value <
0.05 was considered significant. The tests revealed statistically significant differences in participants’ internet
use for education (H = 9.57, df = 3, p < .05), entertainment (H = 16.52, df = 3, p < .05), communication (H =
14.41, df = 3, p < .05), online shopping (H = 12.34, df =
3, p < .05) and other purposes (H = 18.73, df = 3,
p < .05) with respect to maternal education level. To
find the source of the differences post hoc analyses
were conducted as follow-up statistics (Table 4). It was
found that the frequency of internet use for education
purposes in participants, whose mothers are university
graduates, was statistically less than students whose
mothers finished elementary school (U = 1773.00, Z =
−2.75, p = .006) and secondary school (U = 702.00,
Z = −3.09, p = .002). The statistical tests regarding
the internet use for entertainment purposes revealed
that participants, whose mothers are a university (U =
1614.50, Z = −2.80, p = .005) and high school (U =
4456.00, Z = −3.45, p = .001) graduates, scored
Table 2. Chi-Square test results of the relationship between internet use frequency and paternal education level.
Father education level
Internet use frequency
3 h or less
4–6 h
7 h or more
Total
n
%
n
%
n
%
n
%
χ² = 15.39 df = 6 p = .017 C = .21.
Elementary school or less
Secondary school
High school
University and post-graduate
Total
37
48.1
29
37.7
11
14.3
77
100
17
27.9
31
50.8
13
21.3
61
100
34
27.9
49
40.2
39
32.0
122
100
20
32.3
23
37.1
19
30.6
62
100
108
33.5
132
41.0
82
25.5
322
100
BEHAVIOUR & INFORMATION TECHNOLOGY
Table 3. Chi-Square test results of the relationship between
internet use frequency and household income level.
Monthly income level
Internet use
frequency
3 h or less
Very
low
n
17
%
37.8
4–6 h
n
22
%
48.9
7 h or more
n
6
%
13.3
n
45
Total
% 100
χ² = 7.81 df = 8 p = .452.
Low
Middle
High
Very
high
Total
32
32.7
37
37.8
29
29.6
98
100
23
29.1
30
38.0
26
32.9
79
100
21
34.4
24
39.3
16
26.2
61
100
13
31.7
20
48.8
8
19.5
41
100
106
32.7
133
41.0
85
26.2
324
100
statistically higher than those of elementary school
graduates. Moreover, the difference in the internet use
for communication purposes arises from the scores of
participants with elementary school graduate mothers
were significantly less than those of secondary school
(U = 3389.50, Z = −3.14, p = .002) as well as high
school graduates (U = 4660.50, Z = −2.97, p = .003).
As far as the internet use for online shopping is concerned, participants with undergraduate degree holder
mothers used the internet statistically more than
elementary school (U = 1517.00, Z = −2.94, p = .003)
and secondary school graduates (U = 740.00, Z =
−2.80, p = .009). In the case of internet use for other
purposes, the use of participants with mothers of
elementary school graduates was significantly less than
those of mothers with secondary school (U = 947.00,
Z = −3.84, p = .001) and high school degrees (U =
1254.50, Z = −3.11, p = .002).
On a 5-point Likert scale from 1 (Never) to 5
(Always), the participants in the study were asked to
indicate their internet use purposes i.e. education,
entertainment, communication and online shopping,
631
other purposes, and frequency. The data were subjected to Kuruskal Wallis-H test, as the data were
not normally distributed, to find out whether participants’ internet use was significantly different with
respect to their father’s education level. A p-value <
0.05 was considered significant. The results of the analyses are presented in Table 5. Significant differences
were reached in participants’ internet use for education (H = 8.27, df = 3, p < .05) and other purposes
(H = 15.48, df = 3, p < .05) regarding father’s education level. o find where the differences were, the
groups were further pairwise compared using Mann–
Whitney analyses. The results revealed that participants whose fathers were secondary school graduates
used the internet statistically more than participants
whose fathers are high school (U = 2866.00, Z =
−2.55, p = .011) and university graduates (U =
1403.50, Z = −2.57, p = .010). In addition, the
observed difference in the internet use for other purposes lies in the fact that participants, whose fathers
were elementary school graduates, scored significantly
less than participants, whose fathers were secondary
school (U = 727.50, Z = −2.78, p = .023) and high
school graduates (U = 912.50, Z = −3.81, p = .001).
Participants were instructed to rate their internet
use frequency for education, entertainment, communication, online shopping, and other purposes on a 5point Likert scale from 1 (Never) to 5 (Always). Kruskal–Wallis H tests were conducted to explore the
relationship between participants’ internet use for
different purposes and their household income levels.
The results are presented in Table 6. Statistically significant differences were found in participants’ internet
use for online shopping (H = 21.05, df = 4, p < .05)
and other purposes (H = 14.41, df = 4, p < .05) with
Table 4. Kruskal–Wallis H test results of the relationship between internet use purposes and the mother’s education level.
Variable
Maternal Education level
For Education
a.Elementary School
b.Secondary School
c. High School
d. University
a.Elementary School
b.Secondary School
c. High School
d. University
a.Elementary School
b.Secondary School
c. High School
d. University
a.Elementary School
b.Secondary School
c. High School
d. University
a.Elementary School
b.Secondary School
c. High School
d. University
For Entertainment
For Communication
For Online-Shopping
For Other Purposes
N
Mean rank
H
df
P
136
67
89
33
135
67
89
34
136
67
88
33
134
65
87
33
77
42
48
14
167.81
171.49
166.32
116.98
140.53
168.33
183.17
188.94
143.86
184.18
179.72
149.36
148.97
145.61
172.88
199.17
73.81
111.61
102.99
82.61
9.57
3
.023
d < a,b
16.52
3
.001
d>a
c>a
14.41
3
.002
b>a
c>a
12.34
3
.006
d > a,b
18.73
3
.001
d > a,b
Source of the difference
632
M. K. KOBUL
Table 5. Kruskal–Wallis H test results of the relationship between internet use purposes and the father’s education level.
Variable
Paternal Education level
For Education
a.Elementary School
b.Secondary School
c. High School
d. University
a.Elementary School
b.Secondary School
c. High School
d. University
a.Elementary School
b.Secondary School
c. High School
d. University
a.Elementary School
b.Secondary School
c. High School
d. University
a.Elementary School
b.Secondary School
c. High School
d. University
For Entertainment
For Communication
For Online-Shopping
For Other Purposes
N
Mean rank
H
df
p
77
61
121
62
77
61
122
61
77
61
121
61
75
60
120
60
54
37
57
32
161.84
189.01
153.32
147.39
152.53
165.78
158.69
171.52
150.45
175.48
151.41
176.23
156.61
152.27
151.40
178.68
69.06
92.85
105.82
96.66
8.27
3
.041
1.87
3
.599
–
6.21
3
.102
–
4.42
3
.219
–
15.48
3
.001
respect to their household income levels. To determine
the differences, Pair-wise Mann–Whitney U tests
which were conducted and they revealed that the
internet use for online shopping of participants with
very high household income level was statistically
higher than the participants with very low (U = 514.00,
Z = −3.16, p = .002) and low household income levels
(U = 1204.50, Z = −3.72, p = .001). Moreover, the
difference found in internet use for other purposes
stemmed from the fact that the scores of very
high household income group were significantly
higher than participants with vey low (U = 173.00,
Z = −2.43, p = .015), low (U = 498.50, Z = −2.01,
Source of the difference
b > c,d
a < c,d
p = .043) and middle (U = 241.00, Z = −3.41, p =
.001) household income levels.
5. Discussion
This study set out mainly to investigate Turkish digital
natives’ possessions and preferences of technological
devices and the internet use frequency, internet use
purposes for the constituent components of socioeconomic status: (a) maternal education level (b) paternal
education level (c) household income level. The results
showed that all participants owned smartphones. The
results also showed that only 15.6% of the participants
Table 6. Kruskal–Wallis H test results of the relationship between internet use purposes and household income level.
Variable
Household Income Level
N
Mean Rank
H
df
p
For Education/Learning/Academic
a.Very Low
b. Low
c. Middle
d. High
e. Very High
a.Very Low
b. Low
c. Middle
d. High
e. Very High
a.Very Low
b. Low
c. Middle
d. High
e. Very High
a.Very Low
b. Low
c. Middle
d. High
e. Very High
a.Very Low
b. Low
c. Middle
d. High
e. Very High
45
98
79
61
40
45
98
78
61
41
45
98
78
61
40
42
98
76
61
40
25
60
41
30
23
163.82
170.79
171.72
142.98
148.25
145.80
164.52
175.18
149.87
166.73
153.08
162.66
168.38
156.76
161.94
136.06
136.52
174.86
164.23
200.06
77.58
92.47
71.71
100.83
115.54
5.51
4
.239
4.65
4
.325
1.11
4
.893
21.05
4
.001
e > a,b
14.41
4
.006
e>c
For Entertainment
For Communication
For Online-Shopping
For Other Purposes
Source of the Difference
BEHAVIOUR & INFORMATION TECHNOLOGY
owned a desktop computer, while 74.6% reported
owning a laptop computer. These results align with
the extant literature that college- or university-level
students have more laptops than desktop computers
(Baron 2015; Kukulska-Hulme 2012; Ozkan 2010).
However, there is merit to mention that they had
more laptops but not desktops may be that the participants are mostly mobile and staying in dormitories
with a limited capacity of private areas; thus, the compact and compatible nature of laptops seems to be a
viable option for them.
5.1. Internet use frequency
For the second research question, results show that a
relationship exists between participants’ internet use
frequency and their maternal and paternal education
levels. However, no significant relation was found
between participants’ internet use frequency and household income level. The results reveal a considerable
difference in participants’ internet use frequency with
respect to their maternal education level. Participants,
whose mothers are elementary school graduates or less
educated, tend to use the internet less than three
hours a day. However, quite surprisingly, as the
mothers’ education level increases, the duration of
internet use gets longer. Most of participants, who use
7 h or more internet, are children of mothers with university or higher degree. In psycho-pathological studies,
7 h or more use of the internet is considered excessive
use or internet addiction (Kuss and Lopez-Fernandez
2016). Some recent studies also consider 3 or more
hours of internet use as problematic (Anand et al.
2018; Paska and Yan 2011; Shin 2017). However, this
result is surprising as the extant literature suggests
that children of less-educated mothers have a greater
tendency to use the internet excessively (Ni et al.,
2009). Druin (2009) also reports that young adults,
whose mothers are less educated, spend more time
with these technologies at problematic levels. Moreover,
current literature reveals that as the mother’s education
level decreases, the tendency to develop addictions gets
higher (Young 2017). Nonetheless, the results of this
study can be attributed to participants’ limited opportunities to access the internet. Alterntatively, due to monetary problems these students might be underprivileged
with respect to internet access and the devices that
might decrease the duration of internet use (Pachler,
Bachmair, and Cook 2010). Similarly in some developing countries access to technological devices and online
access can be remarkably expensive and limited (Druin
2009; Jones and Marsden 2006). Furthermore, it is
almost axiomatic that as maternal education level
633
increases, so do paternal education and income levels
(Arnold and Doctoroff 2003; Macionis 2012; Sirin
2005; Tezcan 2018). Thus, this makes access to technological devices and the internet quite easier than the
lower-level groups. This might be the reason for the
higher duration of internet use among participants
whose mothers have higher education levels.
Only 26.1% of the participants self-reported using the
internet excessively (more than 7 h). 33.1% of the students used the internet 3 h or less, while 40.8% used
4–6 h daily. These results align with the existing literature suggesting similar results within different parts of
the world. Accordingly, Ayub, Hamid, and Nawawi
(2014) found that university students use the internet
4.48 h, while some other studies report 2–4 h of internet
use daily (Toprakci 2005).
In a very different vein, the general tendency to use
the internet for long hours can be attributed to participants’ homesickness or other psychological difficulties
related to insecurity or culture shock (Christ 2012; Jackson 2012) as most of the students come to the university
from different cities and cultures; they are accepted to
these universities via a nation-wide central university
exam and it was their first year when the data were
gathered.
A statistically significant association was found concerning paternal education level of the participants.
The results indicate that participants who used the
internet 3 h or less were children of fathers with elementary school diplomas. However, as the father’s education
level increased, namely, high school graduates or higher,
the participants were more inclined to use the internet
for longer durations. There is lack of work investigating
the association between father education level and internet use frequency. Yet, the father’s education level is
important because the father is considered the ‘household head’ (Mueller and Parcel 1981) in most cultures
globally. In line with this, common wisdom has it that
higher and better education means higher income and
as the father’s education level increases, the earning
also gets higher (Gee 2015; Hofstede, Hofstede, and
Minkov 2010; Tezcan 2018). Higher earning means
easier access to technology and the internet. That
might be the reason that participants with fathers of
higher education use the internet significantly more
than fathers with lower education.
This result might be attributable to paternal involvement practices of child-rearing. There is a tendency that
fathers do not get involved much with their children
and act only when they are needed or asked for (Dunckley 2017). However, current literature documents that
the higher father’s education level, the stronger they
forge the bonds within the family. Moreover, it is almost
634
M. K. KOBUL
common sense that internet addiction or other psychopathologies are closely associated with negative family
bonds or parent–child alienation (Lam 2015; Paska
and Yan 2011). Rather scant research probing into
father education level variable mainly rallies around
addiction studies. Meşeli Allard (2014) found that children of fathers with undergraduate or higher degrees
showed lower levels of nicotine dependency, while
higher levels of internet addiction. On the other hand,
father-absence is mainly associated with problematic
internet use in the literature. Young adults with no
father present at home for several reasons such as divorce or abandoning the family have a higher tendency
for problematic internet use and/or internet addiction
(Eisenstein, Morais, and Ting 2017). Current literature
also provides research on problematic use of internet
in the absence of father at home which is believed to
cause internet addiction as it is considered as a safe
place to escape by the younger generation (Young
2017). Thus, these findings corroborate earlier research
findings in this sense.
No significant difference was in participants’ internet
use frequency concerning household income level.
Household income is a commonly used sub-construct
of socio-economic status/level variable. Researchers
have reached controversial results with respect to household income in various fields. However, it is widely
known that as income increases, people have better
opportunities to get and use the resources provided by
society (Macionis 2012; McLoyd 1998; Pachler, Bachmair, and Cook 2010). Put differently, lower-income
families are considered mainly as the ‘disadvantaged’
group (Coulmas 2013). To illustrate, common sense
suggests that students whose families have higherincome levels are expected to reach the internet and various other high-technology devices easier than lowerincome families. Thus, they are expected to use the
internet more frequently, though not necessarily at a
problematic level, as they already have easier access
whenever they need it (Shin 2017). A recent report in
Turkey by British Council (2013) highlights that ‘the
share of students who do not have computer or internet
access (37%) is much higher in low income households
than that among high income households (10%)’ (98).
In a study with undergraduate EFL learners Kobul and
Yılmaz (2018) found significant differences in participants’ mobile learning readiness levels concerning
household income level indicating a positive correlation
between income and readiness levels. These works
support the access privilege of higher-income families
and the disadvantaged status of families with lowerincome levels.
Lower-income level families are also considered as
cognitively disadvantaged and more likely to manifest
symptoms of maladaptive social functioning (Bradley
and Corwyn 2002; McLoyd 1998), thus, prone to use
the internet excessively (Ni et al., 2009; Paska and Yan
2011; Young 2017). Moore (2017) draws attention to
children on the autism spectrum, of which key diagnostic markers ‘lend themselves to early preoccupation with
technology to an extent that can result in compulsions
or addictions’ (97). In the last decade, there has been a
trend among parents to give their children smartphones
or tablets and let them play games for long durations,
thus sedating them while they are busy doing other
things. Accordingly, Baron (2015) warns that ‘handing
young children digital devices rather than talking with
them hampers development of their communication
skills’ (171). This is also reported to inhibit cognitive
development (Knight and Nisbett 2007; Marks 2016).
Likewise, excessive television viewers are also common
within low-income families (Pachler, Bachmair, and
Cook 2010). However, contrary evidence is reported
in recent research that directly associates problematic
internet use or internet addiction with higher income
(Kuss and Lopez-Fernandez 2016). Thus, the disagreement on the impact of income on young adults’ internet
use frequency will likely remain in flux, at least within
the near future.
5.2. Internet use purposes
A statistically significant difference was found for all
purposes of use regarding participants’ mother education level. Surprisingly, participants with university
degree mothers used the internet for academic purposes
significantly less than elementary school graduate
mothers. There is the scarcity of work investigating
the influence of mother education level on university
students’ internet use purposes. Recent literature documents that university degree students use the internet
mainly for non-academic purposes, such as socialising
(i.e. facebook, instagram, twitter, snap chat including
Whatsapp) on the net and checking e-mail rather than
for academic purposes, not to mention online gaming
( Ravizza, Uitvlugt, and Fenn 2017; Tadasad, Maheswarappa, and Alur 2003, Viswanathan 2009). However, the
results can be interpreted with inferences from other
works investigating the influence of mother education
level on various other disciplines. The findings of this
study contradict with the existing literature because
mothers with higher education level are known to
speak more to their children, use lexically and syntactically more complex sentences in a harmonious and
BEHAVIOUR & INFORMATION TECHNOLOGY
communicative style (Druin 2009; Hoff 2003), read to
them more (Arnold and Doctoroff 2003, Heath 1983),
provide more teaching experience (Arnold and Doctoroff 2003; Grabe 2009), provide scaffolding and complex verbal strategies (Borduin and Henggeler 1981), get
more involved with school matters (Diamond and
Gomez 2004). Hence, all these practices contribute
much to the cognitive development of their children.
Even more remarkable is the fact that mothers with
higher education levels regulate and monitor the duration of TV their children watch (Hess et al., 1984).
Conventional wisdom has it that people from lowersocioeconomic level strata are more likely to watch
TV for longer hours (Pachler, Bachmair, and Cook
2010). Taking these aspects and empirical evidence
into account it can be inferred that a lower mother education level might lead to, as Bradley and Corwyn (2002)
put it ‘poorer self-regulation and less academic and psychosocial competence’ (385), thus maladaptive functioning. There is an abundance of work documenting
the close relationship between lower maternal education
level with dysfunctional or maladaptive behaviour and
neglectful and harsh parenting practices which lead to
lower school performance (Bradley and Corwyn 2002;
Heath 1983; Sirin 2005; Tezcan 2018). Hence, based
on the evidence from the studies cited, mothers with
higher education are expected to monitor and regulate
their children’s internet use as they do with TV watching times; thus, participants whose mothers have higher
education are more likely to use the internet significantly more for academic purposes than participants
whose mothers have lower education level.
Findings reveal statistically significant differences
only in internet use for academic purposes concerning
father education level. Yet, no significant difference
was found for entertainment, communication and
online shopping purposes. Further analyses indicated
that the difference stems from significantly higher internet use of participants whose fathers had secondary
school education than other groups. There is a palpable
dearth of studies directly investigating the association
between father education level and internet use.
Although the father’s education level is considered
highly important as it is typically associated with the
father’s occupation and household’s income level (Bradley and Corwyn 2002; Erola, Jalonen, and Lehti 2016;
Park 2015), there is a scant of work in this field. Earlier
studies were more interested in the father’s education
solely for its effect on their labour force status (Sirin
2005). However, the results obtained here contradict
some literature findings. It is well established that as
the education level of the father increases, the attitude
towards school and learning gets more positive. Fathers
635
get more inclined to value literacy practices and provide
a wide range of opportunities to access educational
materials activities that stimulate learning (Bradley
and Corwyn 2002; Li 2007). Educated middle-class
fathers’ children are known to have higher education
(Erola, Jalonen, and Lehti 2016; McIntyre 2010; Park
2015; Tezcan 2018) as their fathers get more involved
and monitor their children’s schooling and academic
issues (Garton and Pratt 2009) resulting in longer
schooling and higher academic achievement. In this
sense, participants whose fathers had higher education
seem more likely to use the internet more for academic
purposes.
Another remarkable result from this study is that participants use the internet for educational/learning/academic and non-academic purposes. This result
corroborates earlier research findings which suggest
that young adults use the internet for learning and/or
academic and non-academic purposes (Ayub, Hamid,
and Nawawi 2014; Hawi 2012; Toprakci, 2007). However, there is the dearth of work conducted particularly
to investigate the association between the household
income level and young adults’ internet use purposes.
Internet provides a wide range of highly stimulating
and rewarding environments which offer inherently
pleasant things, and current literature provides mounds
of data that internet use is most commonly associated
with socialising, video watching, online gaming, and
shopping rather than academic and/or learning purposes among young adults (Paska and Yan 2011; Park
2015; Tam 2017; Viswanathan 2009).
Statistical analyses did not yield any significant effect
of household income on participants’ internet use for
academic purposes. However, empirical evidence
suggests that as the household income level increases,
participants are more likely to arrange their time more
appropriately for academic or educational goals than
float through online social media. Higher-income
parents spend more time and have richer exchanges
with their students (Bradley and Corwyn 2002) rather
than letting them do whatever they want, such as playing online games or surfing the internet frivolously. Furthermore, Barkhuizen (2004) remarks that middle-class
families have higher achieving students and compared
to working-class houses since there are more home-literacy materials (Gee 2015; Grabe 2009) and more literacy practices, such as ‘story-telling and educationaltype toys’ (Barkhuizen, 557). Consequently, children
of affluent families engage more with literacy practices
that match with school literacy practices (Heath, 1983)
and come to school more advantaged than low-income
families with respect to school readiness, academic
achievement (Christie 2010) and study time
636
M. K. KOBUL
management and skills (Samruayruen et al. 2013).
Thence, participants from more affluent families are
more likely to use the internet for meaningful and academic purposes rather than float through aimlessly.
Household income had a significant effect only on
the use of the internet for online shopping. In line
with the expectations, the findings suggest that internet
use for online shopping increases as the household
income level raises (Bourlakis, Papagiannidis, and Fox
2008; Jusoh and Ling 2012; Lubis 2018). This can be
attributed to the general spending habits of youth
(Furnham 1999; Jorgensen and Savla 2010). Yet, people
can spend up to 100% more online than they would
spend in cash since there is no pain of payment (Prelec
and Loewenstein 1998; Prelec and Simester 2001). Thus,
participants with higher household incomes are already
expected to use the internet more for online shopping,
which corroborates these findings.
However, these results contradict some published
studies concerning purchasing behaviour of lower
socioeconomic groups. The analyses of this study
revealed that participants from lower household income
level families do significantly less online shopping.
However, literature indicates that lower socio-economic
strata are more likely to do ‘impulse purchasing’ i.e. purchase suddenly and more frequently (Szmigin and Piacentini 2018) and are less likely to evaluate the
product according to different criteria, such as pondering the pros and cons, price, availability of alternative
products option, confidence in the brand, etc., before
purchase. Likewise, there is substantial evidence that
people with lower household incomes are more inclined
to purchase online app (Mueller and de Zwaan 2008;
Shin 2017; Tam 2017).
6. Conclusion
Although numerous scholars have labelled the current
young generation as digital natives or millennials as
an almost homogenous construct with peculiarities
with a non-exhaustive list of different learning styles,
preferences, cognitive skills, better skills of technology
use, etc., there is the dearth of empirical data published
depicting how these digital natives are different with
respect to their use of these newer technologies and
the internet. literature is abundant, suggesting that
younger generations have perennially been exposed to
their adults’ perceptions, intuitions, beliefs, stereotypes,
sometimes prejudices about them. Furthermore, many
studies suggest that some opinions of adults about
their youngsters cannot go beyond mere perceptions
rather than being empirical. In line with this, there is
empirical evidence that the younger generation might
not be as tech-savvy as adults assume them to be.
What’s more, they are roughly considered as if they
were a socially homogenous group of digital natives,
yet these kinds of assumptions seem to be complex
and largely elusive. Recent literature reveals that most
groups labelled or assumed as homogenous are largely
heterogenous in reality (Mueller and Parcel 1981; Phillips and Lonigan 2005). Our findings indicate that, like
many other sociological and psychological constructs,
digital native is contingent upon some socioeconomic
level variables.
This study, conducted with undergraduate students
enrolled in an undergraduate preparatory programme
for learners of English as a foreign language at a university in the North-Eastern coast of Turkey, offers considerable insight into the internet use frequency and
purposes of the current young generation. The most
striking finding to emerge from the study is that it provides additional evidence to the idea that it would not be
wise to consider the younger generation, i.e. digital
natives, as a homogenous construct or a monolithic
group. Remarkably, significant differences were found
in participants’ internet use frequency and use purposes.
As with other social phenomenonon in life, digital
natives also take their share from social stratificiation
and show significant differences concerning father and
mother education level and household income level.
Internet use frequency of the participants differs significantly for mother and father education level yet, surprisingly, not for household income level. Participants
whose mothers had graduate degrees used the internet
significantly more than children of elementary school
graduate mothers. Similarly, results revealed that participants whose fathers had high school and above
degrees used the internet significantly more than fathers
with elementary and middle school graduation. These
can be attributed to the almost common-sense assumption that as the education level increases, people get better jobs with better earnings which leads to easier access
to commodities their society provides, including internet access. Interestingly, the controversy about the
impact of household income on internet use frequency
has raged unabated for the last two decades. Many
studies published offer contradictory findings, which
also urge the need for more studies in different fields.
Internet use purposes of participants also show significant variation with respect to mother education
levels. General wisdom suggests that mothers with
higher education communicate more and better with
their children and exercise proper supervision for
their children. These and other practices of mothers
with higher education, such as providing more scaffolding, literacy and teaching practices and materials,
BEHAVIOUR & INFORMATION TECHNOLOGY
contribute significantly to the cognitive development of
their children. Thus, these young adults who were born
and raised with such literacy practices and systematic
monitoring are known to have more ‘organized’ or ‘literate minds’ (Gee 1986; Goody and Watt 1963; Ong
2013) which help them regulate their time and activities
more efficiently. They are also known to have higher
educational attainment and academic achievement in
their future lives. However, quite surprisingly, the
results of this study showed that internet use for academic/learning purposes decreased as the maternal education level increased. Moreover, the participants whose
mothers finished high school or higher degree selfreported using the internet for non-academic purposes,
such as entertainment, significantly more than middle
and elementary school graduates.
Father education level is another important covariant
of socioeconomic level that emerged from this study.
Although the father education level is gaining a great
deal of visibility as a covariant of research on a socioeconomic level, the research literature addressing the
concept has remained exceptionally fragmented so far.
It is well known that fathers, mainly considered as the
household head, have a great contribution to the general
atmosphere of the house as they are the breadwinners.
Their education level and professions affect their lifestyle considerably. The way people communicate with
other people and establish a relationship with their children are remarkably determined mainly by fathers’ level
of education. Fathers with higher education provide a
warmer home environment and develop closer bonds
and better communication with their children. Children
who have stronger bonds with their fathers are less likely
to show dysfunctional or maladaptive behaviour and
more likely to have better academic achievement and
social relations within the community. These lead to
more organised, systematic and purposeful time management and fewer problematic relations and escapist
behaviour as the internet offers a wide array of stimulating and pleasant opportunities making it one of the
easiest places to escape or hide when children have problems with their family and school.
Another crucial finding of the study demonstrates
that these young adults are not as tech-savvy as they
are assumed to be. They might be spending too much
time online, even excessively, yet this does not necessarily guarantee that they make the best of their time
for academic purposes and improve themselves. They
mainly log on to the internet for social media and gaming purposes. How long they stay online and what they
do on the internet are heavily influenced by their
mother and father’s education level and household
income. Particularly, as the household income increases
637
online shopping of young adults also increases.
However, this does not help or lead to internet use for
academic purposes. Likewise, there is compelling
evidence that ownership of personal computers in the
household has no impact on adolecents’ internet use
and frequency for academic purposes.
It has been almost a tradition for adults to criticise or
label their youth. This aspect can be traced back to the
ancient past as it is named as the ‘Socrates Legacy’.
Thus, the generational gap or conflict phenomenon
has been a perennial debate among scholars. Digital
native seems to be a new buzzword that the adults
coin the younger generation today. Data from several
sources, including this study, have identified that
assumptions or labels on the practices of current new
generation should be exercised with caution rather
than being taken for granted. Apparently, it is difficult
to present an operational definition of what constitutes
a digital native. The relationship between digital natives
and digital literacy is also being questioned. However,
digital literacy is also considered a controversial
phenomenon per se in that there is no comprehensive
definition suggested or a scale available to measure
one’s digital literacy.
There is merit to highlight that considerable differences exist between Western societies and Turkey context for gender roles, child-rearing practices along
economic structure of the countries. The significant
contribution of this study has been to provide data
from a society that has been documented to have
remarkable cultural, economical, and political
peculiarities.
6.1. Limitations and further implications
There is the palpable dearth of work on the actual
behaviour of this new young generation. This study provides key insights into the significance of avoiding such
stereotypical or presumptive overgeneralisations about
contemporary youth. However, it bears some limitations. A convenience sample from a North-East coast
University of Turkey might constitute some limitations
though the participants were already accepted to university by a nationwide exam from different cities of Turkey. Thus, further studies with more participants from
diverse backgrounds in different regions, cultures and
socioeconomic levels might help dispel the myths and
contribute substantially to enhance our understanding
of this newer generation’s relationship with technology
and their peculiarities.
Internet use frequency of the participants differs significantly for mother and father education level. Since
research into internet use already piles on psycho-
638
M. K. KOBUL
pathological studies, more studies with an educational
focus on the internet use frequency and purposes of
this newer generation of young adults are direly needed.
It is highly important to diagnose the fields and
groups that need help within society. This study offers
some implications for the policy-makers, yet more
data from these kinds of studies would contribute significantly to providing help where and when necessary
to improve the welfare of society.
Disclosure statement
No potential conflict of interest was reported by the author(s).
ORCID
Mustafa Kerem Kobul
0712
http://orcid.org/0000-0002-7744-
References
Anand, N., C. Thomas, P. A. Jain, A. Bhat, C. Thomas, P. V.
Prathyusha, and A. V. Cherian. 2018. “Internet Use
Behaviors, Internet Addiction and Psychological Distress
Among Medical College Students: A Multi Centre Study
from South India.” Asian Journal of Psychiatry 37: 71–77.
doi:10.1016/j.ajp.2018.07.020.
Ariés, P. 1965. Centuries of Childhood: A Social History of
Family Life. New York: McGraw-Hill.
Arnold, D. H., and G. L. Doctoroff. 2003. “The Early
Education
of
Socioeconomically
Disadvantaged
Children.” Annual Review of Psychology 54: 517–545.
Ayub, A. F. M., W. H. W. Hamid, and M. H. Nawawi. 2014.
“Use of Internet for Academic Purposes Among Students
in Malaysian Institutions of Higher Education.” Turkish
Online Journal of Educational Technology-TOJET 13 (1):
232–241.
Barkhuizen, G. 2004. “Social Influences on Language Learning.”
In The Handbook of Applied Linguistics, edited by A. Davies
and C. Elder, 552–575. Malden: Blackwell Publishing Ltd.
Baron, N. S. 2015. Words Onscreen: The Fate of Reading in a
Digital World. New York: Oxford University Press.
Beard, K. W. 2011. “Working with Adolescents Addicted to
the Internet.” In Internet Addiction: A Handbook and
Guide to Evaluation and Treatment, edited by K. S.
Young and C. N. de Abreu, 173–189. Hoboken, New
Jersey: John Wiley & Sons Inc.
Bennett, S., K. Maton, and L. Kervin. 2008. “The ‘Digital
Natives’ Debate: A Critical Review of the Evidence.”
British Journal of Educational Technology 39 (5): 775–786.
Berliner, D. C., and B. J. Biddle. 1995. The Manufactured
Crisis: Myths, Fraud, and the Attack on America’s Public
Schools. Massachusetts: Addison-Wesley.
Bernstein, B. 2003. Class, Codes, and Control. 1 vol. London:
Routledge.
Blass, E., and A. Davis. 2003. “Building on Solid Foundations:
Establishing Criteria for e-learning Development.” Journal
of Further and Higher Education 27 (3): 227–245.
Blishen, B. R., W. K. Carroll, and C. Moore. 1987. “The 1981
Socioeconomic Index for Occupations in Canada.”
Canadian Review of Sociology and Anthropology 24: 465–488.
Borduin, C. M., and S. W. Henggeler. 1981. “Social Class,
Experimental Setting, and Task Characteristics as
Determinants
of
Mother-Child
Interaction.”
Developmental Psychology 17: 209–214.
Bottero, W. 2004. Stratification: Social Division and
Inequality. London: Routledge.
Bourdieu, P. 1984. Distinction: A Social Critique of the
Judgement of Taste. Massachusetts: Harvard University
Press.
Bourlakis, M., S. Papagiannidis, and H. Fox. 2008. “E-consumer Behaviour: Past, Present and Future Trajectories of an
Evolving Retail Revolution.” International Journal of
E-Business Research (IJEBR) 4 (3): 64–76.
Bradley, R. H., and R. F. Corwyn. 2002. “Socioeconomic
Status and Child Development.” Annual Review of
Psychology 53 (1): 371–399.
British Council. 2013. Turkey National Needs Assessment of
State School English Language Teaching. Ankara: Mattek.
Buckingham, D. 2007. Beyond Technology: Childrens’ Learning
in an Age of Digital Culture. Cambridge: Polity Press.
Byrne, B. 2005. “Theories of Learning to Read.” In Blackwell
Handbooks of Developmental Psychology. The Science of
Reading: A Handbook, edited by M. J. Snowling and C.
Hulme, 104–119. Oxford: Blackwell Publishing.
Caro, D. H., and D. Cortés. 2012. “Measuring Family
Socioeconomic Status: An Illustration Using Data from
PIRLS 2006.” IERI Monograph Series. Issues and
Methodologies in Large-Scale Assessments 5: 9–33.
Carrell, P. L., and T. E. Wise. 1998. “The Relationship Between
Prior Knowledge and Topic Interest in Second Language
Reading.” Studies in Second Language Acquisition 20 (03):
285–309.
Chambers, J. K. 2004. “Patterns of Variation Including
Change.” In The Handbook of Language Variation and
Change, edited by J. K. Chambers, P. Trudgill, and N.
Schilling-Estes, 297–324. Oxford: Blackwell.
Chapelle, C. A. 2008. “Computer Assisted Language
Learning.” In The Handbook of Educational Linguistics, edited by B. Spolsky and F. M. Hult, 585–595. Malden, MA:
Blackwell Publishing.
Christ, S. 2012. “Agency and Everyday Knowledge of Filipina
Migrants in Dubai, United Arab Emirates.” In International
Handbook of Migration, Minorities and Education:
Understanding Cultural, Social Differences in Process of
Learning, edited by Zvi Bekerman and Thomas Geisen,
677–695. New York: Springer.
Christie, F. 2010. “The Ontogenesis of Writing in Childhood
and Adolescence.” In The International Handbook of
English, Language and Literacy Teaching, edited by D.
Wyse, R. Andrews, and J. Hoffman, 146–158. London:
Routledge.
Chun, D. M. 2011. “Computer-assisted Language Learning.”
In Handbook of Research in Second Language Teaching
and Learning, sedited by E. Hinkel, vol 2, 663–680.
New York: Routledge.
Cirino, P. T., C. E. Chin, R. A. Sevcik, M. Wolf, M. Lovett, and
R. D. Morris. 2002. “Measuring Socioeconomic Status:
Reliability and Preliminary Validity for Different
Approaches.” Assessment 9 (2): 145–155.
BEHAVIOUR & INFORMATION TECHNOLOGY
Clancy, B. 2010. “Building a Corpus to Represent a Variety of
Language.” In The Routledge Handbook of Corpus
Linguistics, edited by A. O’Keeffe and M. McCarthy, 80–
92. London: Routledge.
Cohen, L., L. Manion, and K. Morrison. 2007. Research
Methods in Education. New York: Routledge.
Collins, S., S. Peters, and I. Watt. 2011. “Medical
Communication.” In The Routledge Handbook of Applied
Linguistics, edited by J. Simpson, 96–110. London: Taylor
& Francis.
Considine, D., J. Horton, and G. Moorman. 2009. “Teaching
and Reaching the Millennial Generation Through Media
Literacy.” Journal of Adolescent & Adult Literacy 52 (6):
471–481.
Coulmas, F. 2003. “Sociolinguistics..” In The Handbook of
Linguistics., edited by Mark Aronoff and Janie ReesMiller, 563–581. Oxford: Blackwell Publisher.
Coulmas, F. 2013. Sociolinguistics: The Study of Speakers’
Choices. Cambridge, UK: Cambridge University Press.
Crook, C., and D. Barrowcliff. 2001. “Ubiquitous Computing
on Campus: Patterns of Engagement by University
Students.” International Journal of Human–Computer
Interaction 13 (2): 245–256.
De Abreu, C. N., and D. S. Goes. 2011. “Psychotherapy for
Internet Addiction.” In Internet Addiction: A Handbook
and Guide to Evaluation and Treatment, edited by K. S.
Young and C. N. de Abreu, 155–173. Hoboken, NJ: John
Wiley & Sons Inc.
Demiral, S. 2016. “Farkli sosyal siniflarin mekânsal ayrişma
eğilimleri:
Üst
tabakalarin
mekân
tercihlerine
Istanbul’dan karşilaştirmali örnekler.” Unpublished
Master’s Thesis, Institute of Social Sciences, İstanbul.
De Vaus, D. 2002. Surveys in Social Research. 5th ed. London:
Routledge.
Diamond, J. B., and K. Gomez. 2004. “African American
Parents’ Educational Orientations: The Importance of
Social Class and Parents’ Perceptions of Schools.”
Education and Urban Society 36: 383–427.
Druin, A. 2009. Mobile Technology for Children: Designing for
Interaction and Learning. Burlington, MA: Morgan
Kaufmann.
Duncan, O. D. 1961. “A Socioeconomic Index for all
Occupations.” In Occupations and Social Status, edited by
A. J. Reiss Jr., 109–138. New York: Free Press.
Dunckley, V. L. 2017. “Electronic Screen Syndrome:
Prevention and Treatment.” In Internet Addiction in
Children and Adolescents: Risk Factors, Assessment, and
Treatment, edited by K. Young and C. De Abreu, 213–
242. New York: Springer Publishing.
Eisenberg, A. F. 2013. “A Neurosociology of Mental Health.”
In Handbook of Neurosociology, edited by D. D. Franks and
J. H. Turner, 385–401. New York: Springer.
Eisenstein, E., T. Morais, and E. Ting. 2017. “Resilience and
Preventing Parenting.” In Internet Addiction in Children
and Adolescents: Risk Factors, Assessment, and Treatment,
edited by K. S. Young and C. N. de Abreu, 161–175. New
York: Springer Publishing Company.
Ellis, N. C., and P. Robinson. 2008. “An Introduction to
Cognitive Linguistics, Second Language Acquisition, and
Language Instruction.” In Handbook of Cognitive
Linguistics and Second Language Acquisition, edited by P.
Robinson and N. C. Ellis, 3–24. London: Routledge.
639
Erola, J., S. Jalonen, and H. Lehti. 2016. “Parental Education,
Class and Income Over Early Life Course and Children’s
Achievement.” Research in Social Stratification and
Mobility 44: 33–43.
Evans, G. W., L. E. Maxwell, and B. Hart. 1999. “Parental
Language and Verbal Responsiveness to Children in
Crowded Homes.” Developmental Psychology 35 (4):
1020–1023.
Evans, K., I. Schoon, and M. Weale. 2012. “Life Chances,
Learning and the Dynamics of Risk in the Life Course.”
In Second International Handbook of Lifelong Learning, edited by D. Aspin, J. Chapman, K. Evans, and R. B, 245–267.
London, NY: Springer.
Farooq, M., and C. Javid. 2012. “Attitude of Students Towards
E-Learning: A Study of English Language Learners at Taif
University English Language Centre.” NUML Journal of
Critical Inquiry 10 (2): 17–31.
Flowerdew, J. 2009. “Corpora in Language Teaching.” In The
Handbook of Language Teaching, edited by M. H. Long and
C. J. Doughty, 327–350. Mahwah, NJ: Erlbaum.
Fraenkel, J. R., and N. E. Wallen. 2006. How to Design
and Evaluate Research in Education. London: McGraw
Hill.
Furnham, A. 1999. “The Saving and Spending Habits of Young
People.” Journal of Economic Psychology 20 (6): 677–697.
Gadsden, V. L. 2000. “Intergenerational Literacy Within
Families.” In Handbook of Reading Research, edited by M.
L. Kamil, P. B. Mosenthal, P. D. Pearson, and R. Barr, vol
4, 871–888. Mahwah, NJ: Erlbaum.
Ganzeboom, H. B. G., P. M. De Graaf, and D. J. Treiman.
1992. “A Standard International Socioeconomic Index of
Occupational Status.” Social Science Research 21: 1–56.
Garton, A., and C. Pratt. 2009. “Cultural and Developmental
Predispositions to Literacy.” In The Cambridge Handbook
of Literacy, edited by D. R. Olson and N. Torrance, 501–
517. New York: Cambridge University Press.
Gee, J. P. 1986. “Orality and Literacy: From the Savage Mind
to Ways with Words.” TESOL Quarterly 20 (4): 719–746.
Gee, J. P. 2015. Social Linguistics and Literacies: Ideology in
Discourses. New York: Routledge.
Gelbal, S. 2010. “The Effect of Socio-Economic Status of
Eighth Grade Students on Their Achievement in
Turkish.” Education & Science 33 (150): 1–13.
Giles, H., and A. C. Billings. 2004. “Assessing Language
Attitudes: Speaker Evaluation Studies.” In The Handbook
of Applied Linguistics, edited by A. Davies and C. Elder,
187–209. London: Routledge.
Goldenberg, C. 2011. “Reading Instruction for English
Language Learners.” In Handbook of Reading Research, edited by M. L. Kamil, P. D. Pearson, E. B. Moje, and P. P.
Afflerback, 684–710. London: Routledge.
Goody, J., and I. Watt. 1963. “The Consequences of Literacy.”
Comparative Studies in Society and History 5 (3): 304–345.
Grabe, W. 2009. Reading in a Second Language: Moving from
Theory to Practice. Cambridge: Cambridge University
Press.
Gries, S. T. 2013. Statistics for Linguistics with R: A Practical
Introduction. Berlin/Boston: Walter de Gruyter GmbH.
Guisande, M. A., M. F. Páramo, C. Tinajero, and L. S.
Almeida. 2007. “Field Dependence-Independence (FDI)
Cognitive Style: An Analysis of Attentional Functioning.”
Psicothema 19 (4): 572–577.
640
M. K. KOBUL
Hague, C., and B. Williamson. 2009. “Digital Participation,
Digital Literacy and School Subjects: A Review of
Policies, Literature and Evidence.” Futurelab. [online].
http://www.futurelab.org.uk/sites/default/files/Digital_Part
icipation_review.pdf.
Hawi, N. S. 2012. “Internet Addiction Among Adolescents in
Lebanon.” Computers in Human Behavior 28: 1044–1053.
Heath, S. B. 1983. Ways with Words: Language, Life and Work
in Communities and Classrooms. Cambridge: Cambridge
University Press.
Helsper, E. J., and R. Eynon. 2010. “Digital Natives: Where is
the Evidence?” British Educational Research Journal 36 (3):
503–520.
Hess, Robert D., Susan D. Holloway, W. Patrick Dickson, and
Gary G. Price. 1984. “Maternal Variables as Predictors of
Children’s School Readiness and Later Achievement in
Vocabulary and Mathematics in Sixth Grade.” Child
Development 55 (5): 1902. doi:10.2307/1129937.
Hoff, E. 2003. “Causes and Consequences of SES-Related
Differences in Parent-to-Child Speech.” In Monographs in
Parenting Series. Socioeconomic Status, Parenting, and
Child Development, edited by M. Bornstein and R.
Bradley, 145–160. Mahwah, NJ: Lawrence Erlbaum
Associates.
Hofstede, G., G. J. Hofstede, and M. Minkov. 2010. Cultures
and Organizations: Software of the Mind. 2 vols. London:
McGraw-Hill.
Hollingshead, A. B. 1975. Four Factor Index of Social Status.
New Haven, CT: Yale University.
Howe, N., and W. Strauss. 2000. Millennials Rising: The Next
Great Generation. New York: Vintage.
Hull, G. A., and G. Hernandez. 2008. “Literacy.” In The
Handbook of Educational Linguistics, edited by B. Spolsky
and F. M. Hult, 328–340. Oxford: Blackwell Publishing Ltd.
Jackson, J. 2012. “Education Abroad.” In Routledge Handbook
of Language and Intercultural Communication, edited by J.
Jackson, 449–463. London: Routledge.
Jargowsky, P. 1994. “Ghetto Poverty among Blacks in the
1980s.” Journal of Policy Analysis and Management 13:
288–310.
Jones, M., and G. Marsden. 2006. Mobile Interaction Design.
New York: John Wiley and Sons.
Jorgensen, B. L., and J. Savla. 2010. “Financial Literacy of
Young Adults: The Importance of Parental Socialization.”
Family Relations 59 (4): 465–478.
Jusoh, Z. M., and G. H. Ling. 2012. “Factors Influencing
Consumers’ Attitude Towards e-Commerce Purchases
Through Online Shopping.” International Journal of
Humanities and Social Science 2 (4): 223–230.
Kalaycioglu, S., K. Çelik, Ü Çelen, and S. Turkyilmaz. 2010.
“Temsili bir örneklemde sosyo-ekonomik statü (SES)
ölçüm aracı geliştirilmesi: Ankara kent merkezi örneği.”
Sosyoloji Araştırmaları Dergisi 13 (1): 183–220.
Karatay, G., and G. Kubilay. 2004. “The Determination of
Substance Use and Influenced Factors at Two Highschool;
Which Have Different Socio-Economic Status.”
Hemşirelikte Araştırma Geliştirme Dergisi 1 (2): 57–70.
Kaya, Y. 2007. “Globalization, Industrialization and Social
Class in Less Developed Countries, 1980–2005.”
Unpublished PhD diss., Department of Sociology, Duke
University
Keating, E., and M. Egbert. 2004. “Conversation as a Cultural
Activity.” In A Companion to Linguistic Anthropology.
Alessandro Duranti, 167–196. Malden, MA: Blackwell
Publishing.
Klopfer, E. 2008. Augmented Learning: Research and Design of
Mobile Educational Games. Cambridge: MIT Press.
Knight, N., and R. E. Nisbett. 2007. “Culture, Class and
Cognition: Evidence from Italy.” Journal of Cognition and
Culture 7: 283–291.
Kobul, M. K., and O. Yılmaz. 2018. “Mobile Learning
Readiness Level of Turkish EFL Undergraduate Students
and its Relationship with Academic Achievement and
Certain Ses Variables, II.” International Teacher
Education and Accreditation Congress (ITEAC 2018),
27–29 Ekim 2018, Rize, Türkiye, pp. 73–85
Kukulska-Hulme, A. 2012. “How Should the Higher
Education Workforce Adapt to Advancements in
Technology for Teaching and Learning?” The Internet
and Higher Education 15 (4): 247–254.
Kuss, D. J., and O. Lopez-Fernandez. 2016. “Internet
Addiction and Problematic Internet Use: A Systematic
Review of Clinical Research.” World Journal of Psychiatry
6 (1): 143.
Lam, L. T. 2015. Internet Addiction in Children and
Adolescents: Risk Factors, Assessment, and Treatment, edited by K Young and C De Abreu, 123–140. New York:
Springer Publishing Company.
Larson-Hall, J. 2015. A Guide to Doing Statistics in Second
Language Research Using SPSS and R. New York:
Routledge.
Lazaraton, A. 2005. “Quantitative Research Methods.” In
Handbook of Research in Second Language Teaching and
Learning, edited by E. Hinkel, 209–224. Mahwah, NJ:
Lawrence Erlbaum.
Li, D. 1998. “It’s Always More Difficult Than You Plan and
Imagine. Teachers’ Perceived Difficulties in Introducing
the Communicative Approach in South Korea.” TESOL
Quarterly 32: 677–703.
Li, G. 2007. “Home Environment and Second-Language
Acquisition: the Importance of Family Capital.” British
Journal of Sociology of Education 28: 285–299.
Lubis, A. N. 2018. “Evaluating the Customer Preferences of
Online Shopping: Demographic Factors and Online Shop
Application Issue.” Academy of Strategic Management
Journal 17 (2): 1–13.
Macionis, J. 2012. Sociology. 14th ed. Boston: Pearson.
Mackey, A., and S. M. Gass. 2015. Second Language Research:
Methodology and Design. New York: Routledge.
MacLean, G., and J. Elwood. 2009. “Digital Natives, Learner
Perceptions and the Use of ICT.” In Handbook of
Research on Web 2.0 and Second Language Learning, edited
by M. Thomas, 156–175. London: IGI Global.
Marks, G. N. 2016. “The Relative Effects of Socio-Economic,
Demographic, Non-Cognitive and Cognitive Influences
on Student Achievement in Australia.” Learning and
Individual Differences 49: 1–10.
Mattoon, M. A. 2005. Jung and the Human Psyche: An
Understandable Introduction. London: Taylor & Francis.
McCrindle, Mark, and Emily Wolfinger. 2009. The ABC of
XYZ: Understanding the Global Generations. Sydney:
McCrindle Research Pty Ltd..
BEHAVIOUR & INFORMATION TECHNOLOGY
Mcintyre, E. 2010. “Sociocultural Perspectives on Children
with Reading Difficulties.” In Handbook of Research on
Reading Disabilities, edited by Richard L. allington and
Anne McGill-Franzen, 41–56. New York: Routledge.
McKay, J. 2004, March 21. For the ‘Zippies,’ Life is Good:
High-tech Workers Forge Lifestyles very Different from
their Parents. Pittsburgh Post-Gazette, Series (Sunday).
McLoyd, V. C. 1998. “Socioeconomic Disadvantage and Child
Development.” American Psychologist 53 (2): 185–204.
McQuiggan, S., J. McQuiggan, J. Sabourin, and L. Kosturko.
2015. Mobile Learning: A Handbook for Developers,
Educators, and Learners. Hoboken, NJ: John Wiley &
Sons Inc.
Meşeli Allard, S. 2014. “The Relatedness of Attention Deficit
Hyperactivity and Conduct Disorder Symptoms to
Substance use and Internet Addiction: Importance of
Negative Life Events.” Unpublished Master’s thesis,
Department of Psychology, Middle East Technical
University, Turkey.
Miller, C., and D. Caubet. 2009. “Arabic Sociolinguistics in the
Middle East and North Africa (MENA).” In The Routledge
Handbook of Sociolinguistics Around the World, edited by
M. J. Ball, 238–256. London: Routledge.
Moore, D. 2017. “Internet and Gaming Addiction in Youth on
the Autism Spectrum: A Particularly Vulnerable
Population.” In Internet Addiction in Children and
Adolescents: Risk Factors, Assessment, and Treatment, edited by K. Young and C. De Abreu, 83–101. New York:
Springer Publishing Company.
Mueller, A., and M. de Zwaan. 2008, August. “Treatment of
Compulsive Buying.” Fortschritte der Neurologie
Psychiatrie 76 (8): 478–483.
Mueller, C. W., and T. L. Parcel. 1981. “Measures of
Socioeconomic Status: Alternatives and Recommendations.”
Child Development 52 (1): 13–30.
Mugler, F., and R. Benton. 2009. “Sociolinguistics in the South
Pacific.” In The Routledge Handbook of Sociolinguistics
Around the World, edited by M. J. Ball, 190–198. London:
Routledge.
Ni, X., H. Yan, S. Chen, and Z. Liu. 2009. “Factors Influencing
Internet Addiction in a Sample of Freshmen University
Students in China.” Cyber Psychology and Behavior 12:
327–330.
Oakes, J. 2005. Keeping Track: How Schools Structure
Inequality. 2nd ed. New Haven, CT: Yale University Press.
Oblinger, D. 2003. “Boomers gen-xers millennials.” Educause
Review 500 (4): 37–47.
Okagaki, L., and R. J. Sternberg. 1993. “Parental Beliefs and
Children’s School Performance.” Child Development 64:
36–56.
O’Keeffee, A., M. McCarthy, and R. Carter. 2007. From Corpus
to Classroom. Cambridge: CUP.
Ong, W J. 2013. Orality and Literacy. 3rd ed. New York:
Routledge.
Ozkan, B. C. 2010. “Implementing e-learning in University
2.0: Are Universities Ready for the Digital Age?” In
Handbook of Research on Practices and Outcomes in ELearning: Issues and Trends, edited by H. H. Yang and S.
C. Yuen, 278–293. New York : IGI Global.
Pachler, N., B. Bachmair, and J. Cook. 2010. Mobile Learning:
Structures, Agency, Practices. New York: Springer.
641
Park, H. 2015. “The Relationship Between Internet Use for
Non-Academic Purposes and Happiness and Stress in
Adolescents.” Journal of Korean Academy of Community
Health Nursing 26 (2): 169–177.
Park, H. 2015. “The Relationship Between Internet Use for
Non-Academic Purposes and Happiness and Stress in
Adolescents.” Journal of Korean Academy of Community
Health Nursing 26: 169–177.
Paska, L. M., and Z. Yan. 2011. “Internet Addiction in
Adolescence and Emerging Adulthood: A Comparison
Between the United States and China.” In Internet
Addiction, edited by H. O. Price, 1–29. Hauppauge, NY:
Nova Science Publishers.
Pegrum, M. 2011. “Modified, Multiplied, and (Re-)Mixed:
Social Media and Digital Literacies.” In Digital Education:
Opportunities for Social Collaboration, edited by M.
Thomas, 9–35. New York: Palgrave Macmillan.
Phillips, B. M., and C. J. Lonigan. 2005. “Social Correlates of
Emergent Literacy.” In The Science of Reading: A
Handbook, edited by M. Snowling and C. Hulme, 173–
187. New York: Blackwell Publishing.
Porte, G. K. 2010. Appraising Research in Second Language
Learning: A Practical Approach to Critical Analysis of
Quantitative Research. Vol. 28. Amsterdam/Philadelphia:
John Benjamins Publishing.
Postman, N. 1985. “The Disappearance of Childhood.”
Childhood Education 61 (4): 286–293.
Prelec, D., and G. Loewenstein. 1998. “The Red and the Black:
Mental Accounting of Savings and Debt.” Marketing
Science 17 (1): 4–28.
Prelec, D., and D. Simester. 2001. “Always Leave Home
Without it: A Further Investigation of the Credit-Card
Effect on Willingness to Pay.” Marketing Letters 12 (1): 5–12.
Prensky, M. 2001. “Digital Natives, Digital Immigrants.” On
the Horizon 9 (5): 1–6.
Pressley, M. 2006. Reading Instruction That Works: The Case
for Balanced Teaching. New York: Guilford Press.
Ravizza, S. M., M. G. Uitvlugt, and K. M. Fenn. 2017. “Logged
in and Zoned Out: How Laptop Internet Use Relates to
Classroom Learning.” Psychological Science 28 (2):
171–180.
Regan, V. 2013. “Variation.” In The Cambridge Handbook for
Second Language Acquisition, edited by J. Herschensohn
and M. Young-Scholten, 272–291. Cambridge: Cambridge
University Press.
Ricento, T. 2010. “Language Policy and Globalization.” In The
Handbook of Language and Globalization, edited by N.
Coupland, 123–141. Chichester: Wiley-Blackwell.
Richards, J. C., and R. W. Schmidt. 2013. Longman Dictionary
of Language Teaching and Applied Linguistics. New York:
Routledge.
Rossi, P. H., W. A. Sampson, C. E. Bose, G. Jasso, and J. Passel.
1974. “Measuring Household Social Standing.” Social
Science Research 3 (3): 169–190.
Ruane, J. M. 2005. Essentials of Research Methods: A Guide to
Social Science Research. Oxford: Blackwell.
Samruayruen, B., J. Enriquez, O. Natakuatoong, and K.
Samruayruen. 2013. “Self-regulated Learning: AKey of a
Successful Learner in Online Learning Environments in
Thailand.” Journal of Educational Computing Research 48
(1): 45–69.
642
M. K. KOBUL
Shin, Y. M. 2017. “Smartphone Addiction in Children and
Adolescents.” In Internet Addiction in Children and
Adolescents: Risk Factors, Assessment, and Treatment, edited by K. S. Young and C. N. de Abreu, 29–45. New
York: Springer Publishing Company.
Short, K. G. 2008. “Children’s Literature: Connecting and
Transforming Children’s Worlds.” In Literacy for a New
Century , edited by E. Guzzetti, 95–110. Westport, CN:
Greenwood.
Siegel, P. M. 1971. “The American Occupational Prestige
Structure.” Unpublished Ph.D. diss., University of Chicago.
Sirin, S. R. 2005. “Socioeconomic Status and Academic
Achievement: A Meta-Analytic Review of Research.”
Review of Educational Research 75 (3): 417–453.
Slama, M. E., and A. Tashchian. 1985. “Selected Socioeconomic
and Demographic Characteristics Associated with
Purchasing Involvement.” The Journal of Marketing 49:
72–82.
Stern, H. H. 1991. Fundamental Concepts of Language
Teaching. Oxford: Oxford University Press.
Sunar, L. 2018. Sosyal tabakalaşma-kavramlar, kuramlar ve
temel meseleler. Ankara: Nobel Akademik Yayıncılık.
Szmigin, I., and M. Piacentini. 2018. Consumer Behaviour.
Oxford: Oxford University Press.
Tadasad, P. G., B. Maheswarappa, and S. A. Alur. 2003. “Use
of Internet by Undergraduate Students of PDA College of
Engineering.” Gulbarga 50: 31–42.
Tam, P. 2017. “The Improve Tool: A Resource to Assist
Families and Clinicians.” In Internet Addiction in
Children and Adolescents: Risk Factors, Assessment, and
Treatment, edited by K. S. Young and C. N. de Abreu,
189–211. New York: Springer Publishing Company.
Taner, M., and H. A. Başal. 2005. “Compare Language
Development in First Grade Primary School Students
from Different Socioeconomic Levels Who Take and Do
Not Take Pre-School Education According to the
Gender.” Uludağ University Education Faculty Journal 18
(2): 395–420.
Tapscott, D. 1999. “Educating the Net Generation.”
Educational Leadership 56 (5): 6–11.
Tezcan, M. 2018. Sociology of Education. 18th ed. Ankara: Ani
Publishing.
Thornton, P., and C. Houser. 2005. “Using Mobile Phones in
English Education in Japan.” Journal of Computer Assisted
Learning 21 (3): 217–228.
Toprakci, E. 2005. “The Profiles of the Use of the Internet for
Study Purposes Among University Students.” The Turkish
Online Journal of Educational Technology 6: 129–144.
Turiel, E. 2002. The Culture of Morality: Social Development,
Context, and Conflict. Cambridge: Cambridge University
Press.
Turiel, E.. 2006. “The Development of Morality .” In
Handbook of Child Psychology Vol. 3: Social, emotional,
and personality development , edited by W. Damon and
W. Lerner, 789–857. Hoboken (NJ): John Willey & Sons.
Turkish Statistical Institute. 2018. “Household Budget and
Income Distribution Survey.” http://www.tuik.gov.tr.
Twenge, J. M. 2018. iGen: Why Today’s Super-Connected Kids
are Growing up Less Rebellious, More Tolerant, Less
Happy— and Completely Unprepared for Adulthood.
New York, NY: Atria Books.
Van Looy, A. 2016. Social Media Management. Technologies
and Strategies for Creating Business Value. Switzerland:
Springer.
Vigneron, F., and L. W. Johnson. 1999. “A Review and a
Conceptual Framework of Prestige-Seeking Consumer
Behavior.” Academy of Marketing Science Review 1 (1):
1–15.
Viswanathan, R. 2009. “Using Mobile Technology and
Podcasts to Teach Soft Skills.” In Handbook of Research
on Language Acquisition Technologies: Web 2.0
Transformation of Learning, edited by M. Thomas,
223–236. Hershey: PA: IGI.
Walters, S. 2012. “Focusing on the Heart: Lifelong, Life-Wide
and Life-Deep Learning in the Time of HIV and AIDS.” In
Second International Handbook of Lifelong Learning, edited
by D. Aspin, J. Chapman, and Evans K. Bagnall, 195–208.
New York: Springer.
Wang, S., and N. Heffernan. 2009. “Mobile 2.0 and Mobile
Language Learning.” In Handbook of Research on Web
2.0 and Second Language Learning, edited by M. Thomas,
472–490. Hershey, PA: IGI Global.
Warriner, D. 2011. “Literacy.” In The Routledge Handbook of
Applied Linguistics, edited by J. Simpson, 529–540. London:
Routledge.
Warschauer, M. 2001. “Millennialism and Media: Language,
Literacy, and Technology in the 21st Century.” AILA
Review 14: 49–59.
Wigfield, A., and S. R. Asher. 1984. “Social and Motivational
Influences on Reading.” In Handbook of Reading Research,
edited by P. D. Pearson, 423–452. Mahwah, NJ: Erlbaum/
Taylor & Francis.
Woodward, M., and H. Tunstall-Pedoe. 1999. “Coffee and
Tea Consumption in the Scottish Heart Health Study
Follow up: Conflicting Relations with Coronary Risk
Factors, Coronary Disease, and all Cause Mortality.”
Journal of Epidemiology & Community Health 53 (8):
481–487.
Young, K. S. 2017. “Family therapy for adolescent and childhood internet gaming addiction.” In Internet Addiction in
Children and Adolescents: Risk Factors, Assessment, and
Treatment, edited by K. Young and C. De Abreu, 243–
256. New York: Springer Publishing.
Zemke, R., C. Raines, and C. Filipczak. 1999. “Generation
Gaps in the Classroom.” Training 36: 48–54.
Zhang, K., and C. Bonk. 2009. “Generational Learners &
e-Learning Technologies.” In Handbook of Research
on Practices and Outcomes in e-learning: Issues
and Trends: Issues and Trends, edited by H. Yang and S.
C.-Y. Yuen, 76–92. Hershey, PA: IGI Publishing.