Risk factors for using mobile phones and social media among students
in higher education institutions
Factores de riesgo en el uso del teléfono móvil y de las redes sociales en los
estudiantes universitarios
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January-June of 2024
ISSN: 1696-019X / e-ISSN: 2386-3978
How to cite this article:
Sánchez-Sánchez, A. M.; Sánchez-Sánchez, F. J. and Ruiz-Muñoz, D. (2024). Risk factors for using mobile phones and social media among students
in higher education institutions.
Doxa Comunicación
, 38, pp. 19-39.
https://doi.org/10.31921/doxacom.n38a1959
Ana María Sánchez-Sánchez.
Full-time lecturer in the Department of Economics, Quantitative Methods and Economic
History at the Universidad Pablo Olavide (Seville, Spain). She holds a PhD in Business Administration and Management
from the same university. Her research interests revolve around model analysis, especially through Multivariate Analysis
and DEA methodology. As a result of this research, she has authored articles in scientic journals indexed in JCR and
SJR on dierent areas of Social Sciences such as tourism, employment, communication and sexuality. Moreover, she has
spoken at several national and international conferences.
Universidad Pablo Olavide, Spain
[email protected]
ORCID: 0000-0002-6591-954X
Francisca Jesús Sánchez-Sánchez.
Professor in the department of Economics, Quantitative methods and Economic
History at Pablo de Olavide University (Seville, Spain). I have a PhD in Business Administration and Management from
the Pablo de Olavide University.
Universidad Pablo Olavide, Spain
[email protected]
ORCID: 0000-0001-5325-3667
David Ruiz-Muñoz.
Internal Auditor at Junta de Andalucía. He was a Lecturer in Statistics and Managerial Accounting
at the Universidad Pablo Olavide. He holds a PhD in Business Administration and Management from the same
university. His research interests include Multidisciplinary Statistical and Demoscopic studies and the study of
business management control systems. As a consequence of this investigation, he has authored multiple papers
featured in recognized scientic publications (JCR and SJR), covering diverse Social Science elds such as accounting,
communication, tourism, and sexuality.
Junta of Andalucía, Spain
[email protected]
ORCID: 0000-0003-4538-7774
is content is published under Creative Commons Attribution Non-Commercial License. International License CC BY-NC 4.0
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Received: 24/04/2023 - Accepted: 30/10/2023 - Early access: 14/11/2023 - Published: 01/01/2024
Recibido: 24/04/2023 - Aceptado: 30/10/2023 - En edición: 14/11/2023 - Publicado: 01/01/2024
Abstract:
Mobile phones have become indispensable devices for young people,
transforming traditional socializing spaces into virtual ones. Excessive
use of mobile phones can lead to problematic or addictive behavior.
is study analyzes the dependence of university students on new
technologies, specically mobile phones and social networks. It aims
to identify correlations between various variables that inuence this
dependence and associate them with parameters characterizing
other types of addiction, such as substance use. e study employed
the “Mobile Phone Problem Use Scale” questionnaire along with a
questionnaire developed by the researchers to collect data on socio-
demographic, psychosocial, and social network variables. A total of
371 students from Pablo Olavide University in Seville participated in
the study. e results showed that 53.4% of the participants considered
themselves dependent on mobile phones, while 35.1% reported
dependence on social networks. Interestingly, the age of the students
did not appear to inuence problematic mobile phone use, but there
was a notable gender dierence, with women being more likely to
misuse mobile phones than men.
Keywords:
University students; mobile phones; dependence; social
networks; addiction.
Resumen:
El teléfono móvil se ha convertido en un objeto indispensable para los
jóvenes, transformando los espacios de socialización tradicionales en
otros virtuales. Su utilización excesiva genera un uso problemático o
adictivo. Nuestro estudio analiza la dependencia de los universitarios
a las nuevas tecnologías (teléfono móvil y redes sociales), identicando
correlaciones entre variables que condicionen esa dependencia, y
asociándolos con parámetros que caracterizan a otras adicciones
relacionadas con el consumo de sustancias. Aplicamos el cuestionario
«Mobile Phone Problem Use Scale», y otro de elaboración propia para
las variables socio-demográcas, psicosociales y de redes sociales.
Participaron 371 estudiantes de la Universidad Pablo Olavide (Sevilla).
El 53.4% y el 35.1% se consideraron dependientes al teléfono móvil y a
las redes sociales respectivamente. La edad de los estudiantes no inuye
en el uso problemático de los móviles. La probabilidad de efectuar un
mal uso de los móviles es mayor en las mujeres que en los hombres.
Palabras clave:
Universitarios; teléfonos móviles; dependencia; redes sociales; adicción.
1. Introduction
Addiction has various denitions. According to Griths (2010), it encompasses behaviors meeting six criteria: salience, mood
swings, tolerance, withdrawal, conict, and relapse. On the other hand, Koob and Volkow (2010) describe addiction as a
chronic process characterized by:
–
Compulsive search and consumption behavior.
–
Loss of control.
–
Rapid need for reduction of a negative or dysphoric emotional state (anxiety, irritability), resulting in a withdrawal syndrome
only alleviated by substance use. It’s essential to dierentiate between use, abuse, and actual addictive behavior.
e DSM-4 (Diagnostic and Statistical Manual of Mental Disorders) initially recognized addictions with substances under the
label “Substance Abuse Disorders.” It’s crucial to distinguish between abuse and dependence. In the DSM-4, abuse was dened
as a maladaptive pattern of use that led to signicant and recurrent adverse consequences related to repeated substance use,
with no identied dependence, tolerance, or compulsive use pattern. Conversely, addiction, classied under “Substance Use
Disorders,” encompasses concepts like dependence, tolerance, and withdrawal.
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1.1. Addiction without Substance
Addictive behaviors aren’t limited to substances; they can include any pleasurable activity that, for some individuals, turns into
an addiction. A core element of this disorder is losing control over the chosen activity, even if it leads to adverse consequences.
e DSM-5 classies pathological gambling, previously an “Impulse Control Disorder” in the DSM-4, as a form of addiction
known as “Non-substance addiction.” Non-substance addiction involves repetitive behaviors that provide pleasure and stress
relief, particularly in their initial phases, leading to a loss of control and negatively aecting an individual’s life in various
domains –family, work, or social. It shares characteristics such as withdrawal syndrome, tolerance, and loss of control with
“Substance-Related Disorders and Addictive Disorders.”
1.2. Abuse or Addiction to Mobile Phones and Social Networks
Excessive mobile phone use has been widely discussed in the media, sparking debates on whether it should be considered
an addiction (Caro, 2018). Many people exhibit problematic use patterns, especially adolescents and young adults, leading to
extensive studies on mobile phone addiction. While the DSM-5 doesn’t explicitly categorize excessive mobile phone use as a
behavioral disorder, it shares signicant similarities with other addictive behaviors, like substance dependence. Several studies
suggest that technology use can produce symptoms akin to other addictions. e term “addiction to new technologies” hasn’t
been ocially recognized by organizations like the American Psychiatric Association (APA) or the World Health Organization,
but it’s considered part of social or behavioral addictions in the specialized literature. (Prieto and Moreno, 2015).
While there is no specic category in the DSM-5 that includes excessive mobile phone usage as a behavioural disorder, it is
apparent that there are traits that exhibit signicant resemblances with other disorders, like substance dependence. However, a
number of studies suggest that the use of technology may lead to symptoms similar to those seen in other addictions (Echeburúa
and de Corral, 2010; Labrador and Villadangos, 2010). e concept of addiction or dependence to mobile phones or the internet
has been proposed to explain the harmful use and lack of control over technology, which can result in behavioural, emotional,
and social issues (Shi et al., 2023). However, the term ‘addiction to new technologies’ has not yet been ocially recognised by
organisations such as the American Psychiatric Association (APA) or the World Health Organisation. Nevertheless, specialised
literature regards it as a social or behavioural addiction analogous to other types of already established addictions (García del
Castillo, 2013; Marciales and Cabra, 2010; Young, 2005).
One potential denition of mobile phone addiction, or an individual who is addicted to using a mobile phone, is someone who
experiences a fear of not being able to utilise their device (Kara et al., 2021). is form of addiction could result in signicant
maladaptive reactions in the addicted individual (Anshari et al., 2019; Zwilling, 2022), including anxiety, depressive states,
emotional imbalances, or disruptions to sleep and eating patterns (Elhai et al., 2017; Jahrami et al., 2021).
e issue does not stem from reliance on devices, but rather from the factors that lead to their misuse (Ahmed et al., 2011;
Buchinger et al., 2011). Individuals prioritise social interaction through various online platforms, such as social networks, over
more traditional forms of communication (Chóliz, 2010, 2012; Echeburúa et al., 2009; Ontiveros, 2015). Dependence on social
networks is characterized by excessive usage leading to loss of control, withdrawal symptoms such as anxiety, depression, and
irritability when unable to access the network temporarily, tolerance for increased online time, and negative impacts on daily
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life (Sharma et al., 2023). While instruments have been developed to assess the addictive potential of these platforms, there is
a lack of conclusive evidence (Chi et al., 2022).
1.3. Young People’s Vulnerability to Mobile Phone and Social Media Abuse
Not all individuals become addicted to substances or behaviors, even if they engage in abusive behaviors. People are exposed
to rewarding environments and stimuli, and not all cases lead to addictions, as each individual has a certain degree of
vulnerability, which determines addictive behaviors. Vulnerability is inuenced by dierent personality characteristics, such
as impulsivity, sensitivity to immediate reinforcement, which conditions the inability to postpone (Hogarth, 2011), the need
for seeking sensations or experiences, especially during adolescence (Zuckerman et al., 1993), low self-esteem, intolerance
to frustration, the absence or diculty of coping with daily challenges, as well as specic emotional variables, including a
tendency to dysphoric moods, lack of aection, and poor social or family relationships (Echeburúa et al., 2009).
e rapid progress of new technologies and their easy accessibility has led young people to develop an intense relationship
with the Internet and mobile telephony. is has made them the rst generations in which changes in customs, habits, and
attitudes are evident (Figueredo and Ramírez, 2008).
New technologies are integral to people’s socialization process, inuencing their way of life, attitudes, behaviors, customs, and
more (Castellana et al., 2007). is is facilitated by the characteristics of the Internet itself, such as anonymity and exibility,
which encourage interaction. ese features are particularly advantageous for communication among introverted individuals
(Williams and Merten, 2008). Young people’s virtual communication is inuenced by their own perception, self-esteem, and
the social benets they derive from it (Bianchi and Phillips, 2005).
Numerous studies analyze the relationship between young people and new technologies, focusing on attitudes toward these
technologies, their most common uses, the associated risks, and resulting addictions, as well as security systems (Vidales-
Bolaños and Sádaba-Chalezquer, 2017), and dierent types of parental control (Wang et al., 2023).
e use of mobile phones in the eld of learning is currently on the rise (Lepp et al., 2015), with young university students
using them as tools to nd information about their studies, organize, and communicate with their peers (Deribigbe et al., 2022;
Zogheib and Daniela, 2022). is trend highlights potential dierences in usage based on gender, age, or the eld of study
among university students. Adolescents and university students are considered the most at risk regarding new technologies.
University students are an interesting population for studying technology dependence, especially since this university period
represents a transition, often involving independence from the family and the start of new friendships and relationships,
which can lead to changes in technology use habits (Fernández-Villa et al., 2015).
Regarding the analysis of social networks by young people, two main issues are addressed: the frequency of use and motivations
for using them (Zheng and Cheok, 2011). Young people increasingly express themselves via mobile phones using virtual
communication systems and social networks (Aguado and Martínez, 2006). Psychological motivations for their use are also
being explored (Zheng et al., 2009), with a social approach seeking to analyze concepts like social capital and social well-being
(Vidales-Bolaños and Sádaba-Chalezquer, 2017; Appel et al., 2014).
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A signicant portion of these studies involve university students as they are considered the most at-risk population (Polo
et al., 2017). Based on these theoretical contributions and previous research, this study established various objectives. It
aims to assess the degree of addiction to smartphones and social networks among university students at Pablo de la Olavide
University and determine the sociodemographic and psychosocial factors favoring inappropriate use of smartphones and
social networks. In accordance with the results of other studies (Álvarez & Moral,2020; Haro et al.,2022), the following research
questions were formulated:
–
Does student gender inuence possible addictive use of mobile phones?
–
Does the age of the student inuence a possible addictive use of mobile phones?
–
Does problematic mobile phone use lead to dependence on social media?
e study is conducted among students who evaluate their dependence on mobile phones using a questionnaire, a method
employed in similar studies (Fekih-Romdhane et al., 2023; Li et al., 2023). is approach will help in developing proles of
university students’ dependence on mobile phones and social networks, identifying potential correlations between various
variables that may inuence their use. It also contributes to the ongoing debate about whether excessive technology use
should be considered as addictions.
2. Sample Design and Methodology
A quantitative methodology was employed to describe the observed reality (Hernández et al., 2016). Internationally
standardized scales were used as data collection instruments to quantify addiction to mobile phones and social networks as
the primary variables of analysis.
e questionnaire used is divided into three blocks that collect information on:
–
Self-generated socio-demographic variables that gather information on population characteristics such as age, gender, self-
assessment of socio-economic level, family situation, family independence, area of residence, smoking habits, and alcohol
consumption.
–
Variables analyzing mobile phone use based on the Spanish adaptation of the Mobile Phone Problem Use Scale (MPPUSA)
(López-Fernández et al., 2012). e components that make up the questions on mobile phone use include: 1) frequency of
use, 2) time of use, 3) purpose of use, 4) impact on other activities, 5) relationships and friendships, 6) emotional state, 7)
habit, and 8) costs.
–
Self-generated variables that analyze the use of social networks. Information is collected on the social networks used,
frequency of publication of news or photos, the importance of the number of followers, and self-assessment of dependence
on social networks, with the aim of assessing the possible consideration of problematic use of mobile phones and social
networks as an addiction.
e last two blocks of questions provide certain items that facilitate the analysis for the possible consideration of problematic
use of mobile phones and social networks as an addiction.
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e study population comprises the students of Pablo Olavide University (UPO) in Seville, Spain.
For the collection of research data, ethical guidelines were adhered to. Research participants were made aware of the entire
process and were informed about the research objectives, the type of participation required, or expected, and the use that will
be made of the results obtained (Abad, 2016). Independent of the data collection function, accountability was addressed to the
group of students who responded to the questionnaires, with the aim of informing them through classroom comments based
on the results and the existence of a previous report. Among the ethical principles considered, informed consent was ensured,
guaranteeing the autonomy and the right to privacy of the informant’s data. Participants were made aware of the benets
and consequences that could arise during their participation in the research (Vargas et al., 2007). Data collection through
questionnaires was conducted during April and May 2022.
e sampling design used is simple random sampling at a condence level of 95%. e maximum sample error considered
is 5%. e sample size consists of 371 students from dierent degree programs, including Business Administration and
Management, Double Degree in Business Administration and Management and Law, Physical Activity and Sport Sciences,
Environmental Sciences, and Labour Relations and Human Resources. e participating degree programs were randomly
selected. e application was carried out in the classroom, with a member of the research team present to explain the study’s
aim, the privacy of the data collected, and to invite students to participate voluntarily. ey were also provided with an
explanation of the concept of dependence on mobile phones and social networks to assess their usage properly.
For data analysis, a descriptive study of the variables was conducted. e Chi-Square (Chi2) test of independence was used
to examine the dependence between variables, complemented by Yule’s Q coecient to measure the association between
dichotomous nominal variables.
Once the variables that show a relationship were identied, binary discrete choice models were applied to explain this
relationship and quantify it. ese models reect an individual’s choice between various possible alternatives. In our study,
as there are only two alternatives, we used binary choice models, specically the Probit model. is model helps identify the
characteristics or factors that inuence an individual’s behavior in response to a specic decision process.
e Probit model is a non-linear binary choice model based on the Normal distribution function. It provides a measure of
the probability of choosing the alternative under study. In our case, we quantify the probability of self-rated dependence on
mobile phones and the probability of dependence on social networks.
IBM SPSS Statistics v27.0.0.0 and Econometrics E-views 9.5 were used for the statistical processing of the data.
3. Results
e results can be divided into two parts: descriptive analysis of the variables and analysis of the dependence between
variables.
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3.1. Descriptive analysis of the variables
e following tables show the percentage of positive responses to the items analyzing variables referring to mobile phone use.
Table 1. Mobile phone usage
Frequency of usePercentage (%)
Mainly on working days
3
Mainly at weekends
1.3
Every day
95.7
Time of usePercentage (%)
Less than 30 minutes a day
.8
30 minutes to 1 hour per day
6.5
1 to 2 hours per day
21.8
2 to 3 hours per day
22.4
More than 3 hours a day
48.5
Purpose of usePercentage (%)
Calls14.5
WhatsApp33.3
SMS.1
Entertainment with gaming applications
6
Social media
32.1
Surng the internet
14
Source: own elaboration
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Table 2. Psychosocial aspects of mobile phone use
Impact on other activities
Percentage (%)
YesNo
Stops activities (study or work)
32.667.4
Spending time on your mobile phone when you should be doing other things and this is causing you
problems
36.163.9
Mobile phone use has taken away hours of sleep
48.851.2
When he is on the phone and doing something else, he gets carried away with the conversation and I
don’t pay attention to what he is doing.
55.344.7
Decrease in performance
17.582.5
Discomfort associated with mobile phones
5.994.1
Preference of mobile phone use over other more pressing issues
13.786.3
Arriving late when you are hooked on your mobile and you shouldn’t be
4.395.7
In a hurry because the mobile phone has rung in class, cinema or theatre.
31.868.2
Relationships and friendships
Percentage (%)
YesNo
Hiding time spent on mobile phones from others
4.395.7
Friends and family complain about mobile phone use
27.872.2
If I didn’t have a mobile phone, it would be dicult for friends to get in touch.
77.622.4
Friends don’t like it when my mobile phone is switched o.
32.167.9
Emotional state
Percentage (%)
YesNo
Able to bear not having a mobile phone
62.337.7
Feels impatient and annoyed when not holding a mobile phone
14.385.7
Keeping your mobile in mind even when you’re not using it
1090
Diculty concentrating in class while working or doing homework
27.872.2
When he has felt bad he has used his mobile phone to feel better.
32.367.7
When you are not reachable on your mobile phone, it makes you nervous.
21.878.2
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Using the mobile phone to talk when feeling lonely or isolated
6634
Nervousness when not checking the mobile phone for a long time
22.477.6
Dreaming of mobile phones
.899.2
Bad mood when you switch o your mobile phone
3.896.2
Feeling lost without a mobile phone
25.974.1
Impatience for the latest technology in mobile devices
18.981.1
Habit
Percentage (%)
YesNo
Never enough time for the mobile phone
18.381.7
Time spent on mobile phones has increased in the last 12 months.
28.871.2
Tries to spend less time on mobile phones but is unable to do so
12.187.9
He nds it dicult to switch o his mobile phone
29.170.9
You nd yourself hooked to your mobile phone more than you would like to be
37.562.5
He has been told that he spends too much time on his mobile phone.
31.568.5
Costs
Percentage (%)
YesNo
You have spent more on mobile than you should or could aord to pay for.
8.691.4
Self-assessment of the unit
Percentage
(%)
YesNo
To mobile phone
46.653.4
To social networks
34.765.3
Source: own elaboration
Self-assessment of Dependence: 46.6% of university students self-assess themselves as dependent on mobile phones, while
the percentage drops to 34.7% in the case of dependence on social networks.
Social Networks: In the analysis of social network usage, 35.1% of university students acknowledge excessive use of them, with
91.4% of students using social networks. Among those who use them, 30.9% say they use Instagram, 26.2% use Facebook, 24.9%
use Youtube, 12.6% use Twitter, 1.1% use LinkedIn, and 4.2% say they use other social networks. 60.8% post news or photos
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on social networks from time to time or when they have time, 18% post news or photos 2 or 3 times a week, 13.9% mostly on
weekends, 5.9% every day, and 1.5% several times a day. 89.1% do not attach any importance to the number of followers they
have on social networks, compared to 10.9% who do.
3.2. Analysis of the Dependence Between Variables
We investigated the relationship between university students’ self-assessment of their dependence on mobile phones and
social networks with respect to socio-demographic variables. For this analysis, the Chi2 test of independence was applied, and
the results are presented in Tables 3 and 4.
e Chi2 statistic and the signicance level indicate that the dependence of university students on mobile phones is associated
with the variables of gender and social network dependence. e results reveal a strong positive relationship between mobile
phone dependence and social network dependence, implying that problematic mobile phone use leads to social network
dependence. On the other hand, the association between mobile phone dependence and gender is negative, indicating a
greater dependence on mobile phones in women than in men.
Table 3. Analysis of independence with respect to the assessment of mobile phone dependence
VariablesChi2
Signicance level
Q Yule
Age
8.289.082
-
Gender
10.381.001*
-.3262
Socio-economic level
2.478.290
-
Family situation
5.882.117
-
Family independence
.036.850
-
Area of residence
.019.890
-
Alcoholic beverages
1.099.294
-
Smoker
2.537.111
-
Dependence on social networks
146.637.000*
.9315
*p<.01 Source: own elaboration
e relationship between university students’ assessment of dependence on social networks and socio-demographic variables
was studied. e results indicate the existence of an association between age and dependence on social networks, as well as
between gender and dependence on social networks (see Table 4). e association between age and social network dependence
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is weak, while the relationship between social network dependence and gender is moderate and negative, indicating that
women are more dependent on social networks than men.
Table 4. Analysis of independence with respect to dependence on social networks
VariablesChi2
Signicance levelQ for Yule
Age9.612.047*-
Gender25.144.000**-.5097
Socio-economic level
3.441.179-
Family situation
2.939.401-
Family independence
.090.764-
Area of residence
.495.482-
Alcoholic beverages
.276.599-
Smoker.973.324-
*p<.05. **p<.01 Source: own elaboration
e analysis now focuses exclusively on those variables that exhibit statistically signicant dependence. ese variables
determine and quantify the categories between which this relationship is established, and dierent Probit models are
proposed and estimated. e rst of these has the dependent variable as the assessment of dependence on the mobile phone,
with gender as the independent variable (Model I).
Table 5 provides the estimated coecients of Model I, which are used to construct the Probit model equation. e estimation
of the model provides the probability that a student is dened as mobile phone dependent. It also includes the test statistics
(Z-statistic) and the signicance level to evaluate the hypothesis of statistical signicance associated with each variable. e
gender variable is statistically signicant in explaining the mobile phone dependence variable. It can be concluded that the
model constructed is globally signicant (LR level of signicance).
e coecients estimated by Model I indicate that the probability of a student considering themselves dependent on a mobile
phone is higher for females than for males.
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Table 5. Summary of Model I
Variables
Estimated coecients
Z-statistic
Signicance level
Constant.13491.4277.1534
Gender-.4231-3.2190.0013*
Statistician LR
10.4243
Signicance level (LR)
.0012*
*p<.01 Source: own elaboration
e marginal eect of each variable is quantied by the product of the value of the Normal density function at a given point
(the mean will be taken as the reference point) and the corresponding parameter. In our study, the average marginal eect of
gender on the probability of mobile phone dependence is -16.70%, indicating that the average probability of mobile phone
dependence is 16.70% higher for females than for males.
In Model II, dependence on social networks is proposed as a dependent variable, with gender and dependence on mobile
phones as independent variables. Age has not been included as an independent variable in the Probit model, despite showing
dependence on social networks in the Chi2 test analysis. is is because including it in the model indicates that it is not
statistically signicant, and the results derived from its inclusion would be erroneous. erefore, it has been excluded from
the analysis.
Table 6. Summary of Model II
Variables
Estimated coecients
Z-statistic
Signicance level
Constant-1.2056-7.5928.0000*
Gender-.5744-3.5110.0004*
Self-assessment of mobile phone dependence
1.917011.1381.0000*
Statistician LR
176.6049
Signicance level (LR)
.0000*
*p<.01 Source: own elaboration
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e coecients estimated by the model indicate that the probability of considering oneself as dependent on social networks
is higher for women than for men, and that the probability of dependence on social networks is higher for those with mobile
phone dependence. Gender and mobile phone dependence are statistically signicant, conrming the validity of the model
(see Table 6).
e analysis of the average marginal eect that gender has on the probability of dependence on social networks is -14.17%,
indicating that the average probability of dependence on social networks is 14.17% higher for women than for men.
e study of the average marginal eect of mobile phone dependence on the probability of social network dependence is
58.22%, with the average probability of social network dependence being 58.22% higher for students with mobile phone
dependence.
4. Discussion
In relation to our rst research proposal, which aims to determine both personal and psychosocial variables related to
dependence on mobile phones and social networks among university students, we can highlight the high percentage of
students who recognize that they are dependent on mobile phones (46.6%). is nding suggests that when young people
have access to this technology, it is challenging for them to do without it, leading to prolonged usage hours and increased
vulnerability to excessive and addictive use (Ertemel et al., 2023). Approximately 30% of them reported diculties in switching
o their handsets, indicating that they have recognized the signicant increase in mobile phone use over the past year and
have received comments from their peers and environment regarding their excessive mobile phone usage.
e coexistence of smartphone addiction and depressive symptoms among university students is notable, as these aspects are
highly correlated (Shi et al., 2023). is is consistent with the results of our study, as mobile phone use is linked to emotional
states. For instance, students reported using mobile phones to cope with loneliness (66%) or when feeling down (32.3%). ese
ndings align with previous studies (Romero and Aznar, 2019; Echeburúa, 2012; Pourafshari et al., 2022; Wei et al., 2023).
It’s important to note that percentages of mobile phone dependence in the literature vary widely, with gures ranging from 2.8%
to 26.1% for dierent studies, making direct comparisons challenging due to dierences in the basic concept of dependence.
In our study, the primary uses of mobile phones are connecting to the internet, particularly through WhatsApp (33.3%), social
networks (32.1%), and general internet browsing (14%). Traditional phone calls appear to be a lower priority, with only 14.5%
of students using their phones for this purpose.
Problematic internet use rates also vary across studies, with percentages between 3.7% (Estévez et al., 2009), 6.1% (Carbonell et
al., 2012) and 9.9% (Muñoz-Rivas et al., 2010). In our analysis, the percentages of social network use are very high, with 91.4%
of students using them, and 35.1% of these students consider themselves dependent on social networks. e most frequently
used social networks include Instagram (30.9%), Facebook (26.2%), Youtube (24.9%), Twitter (12.6%), LinkedIn (1.1%), and
other social networks (4.2%).
e results of our study indicate a strong relationship between dependence on mobile phones and social networks, suggesting
that university Students dependent on mobile phones are more likely to be dependent on social networks (58.8%).
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In terms of socio-demographic variables, we did nd statistically signicant dierences between men and women with regard
to the use of mobile phones and social networks. Specically, women are 16.7% more likely to engage in problematic mobile
phone use than men, and 14.17% more likely to exhibit social network dependence (Tu et al., 2022). While a mild correlation
between social network dependence and age is observed, this relationship does not attain statistical signicance in the
regression model that explains this association, which diers from Labrador and Villadangos (2010).
Other socio-demographic variables, such as socio-economic level, family situation, family independence, area of residence,
and alcohol and tobacco consumption, do not appear to be determinants of dependence on mobile phones and social
networks in our results. is contrasts with the ndings of Sánchez-Martínez and Otero (2009), which highlighted the role of
certain socio-demographic variables in determining intensive mobile phone use. Consequently, we conclude that only gender
is a predisposing factor for dependence on mobile phones and social networks.
Based on our results, university students who admit to problematic mobile phone use and dependence on social networks are
aware of their excessive or addictive use of new technologies. Approximately half of the students acknowledge dependence
on mobile phones, and more than a third admit dependence on social networks. is emotional connection plays a crucial
role, and there is a strong correlation between dependence on mobile phones and social networks (Moral and Suárez, 2016).
University students who recognize problematic mobile phone use are nearly 60% more likely to engage in excessive use of
social networks. Additionally, women are more likely to be dependent on either of the two technologies analyzed.
In terms of considering the abusive use of these technologies as a potential addiction, the questionnaire we used introduced
parameters to identify addictive behaviors, such as salience, mood changes, tolerance, withdrawal, conict, and relapse
(Griths, 2010).
Our results align with these parameters as they identify certain psychosocial aspects related to salience. More than 30% of
respondents acknowledged that they spend excessive time on their mobile phones when they should be engaging in other
activities like studying, working, or physical activities (Huang et al., 2022). is behavior led to problems such as sleep
deprivation (48.8%) (Brautsch et al., 2023) or neglect of other, more urgent matters (13.7%) (Aydın and Aydin, 2022). Other
aspects analyzed are linked to the withdrawal syndrome. For instance, 37.7% of university students do not believe they can
go without their mobile phones, 14.3% become impatient or irritable when not holding their phones, and 22.4% experience
nervousness when not checking their mobile phones for some time (Wong et al., 2022). Additionally, 3.8% experience irritability
or moodiness when switching o their phones. Aspects associated with conict were also explored. Approximately 27.8%
of respondents reported that their friends and family have complained about their excessive mobile phone use, and 4.3%
admitted that they hide the time spent on their mobile phones from others. ese ndings align with other studies indicating
an inverse relationship between social skills and social network addiction (Vásquez et al., 2020).
ese results demonstrate signicant similarities between problematic use of these technologies and what is traditionally
termed substance-related addictions.
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5. Conclusions
e results of this study underscore the importance of focusing on the education of young people to promote controlled
and responsible technology use. Preventing and educating about attitudes towards technology is fundamental and should
commence from an early age. With the increasing access to mobile devices at younger ages, it is imperative to develop
prevention and treatment programs that provide comprehensive interventions, such as psychosocial support, self-control
techniques, psychoeducation, stress management, and emotional re-education. ese interventions can foster adaptive
technology use, particularly among vulnerable populations like adolescents.
Regarding the potential classication of problematic mobile phone and social network use as an addiction, it’s important to
acknowledge that this remains a topic of debate within the scientic community. As technology continuously evolves, users
adapt, and distinguishing between transient disruptions inherent to any evolutionary process and those that may constitute
a behavioral addiction becomes challenging. Currently, no behaviors resulting from technology use are considered addictive
diseases. However, the introduction of “non-substance related addictions” in DSM-5 is a signicant step toward incorporating
behavioral addictions into diagnostic classications. As these behaviors evolve, their inclusion in mental health categories
will elevate their importance and spur the development of multidisciplinary techniques and resources for diagnosis and
comprehensive treatment.
e primary limitation of this study is its limited scope, given that the sample consists of students from a single university. is
limitation reduces the generalizability of the ndings and introduces the possibility of bias. Future research could build upon
these ndings by expanding the study to include students from other regional, national, or international universities.
Further investigations could delve into the types of social relationships that university students maintain through mobile phones
and social networks, exploring how these relationships can be positive without rendering the mobile phone indispensable.
Additionally, it would be benecial to examine the habits and activities of university students who are less dependent on new
technologies, such as participation in sports, social or cultural activities, or involvement in associations. is analysis could
provide insights into whether promoting these alternative activities among students with problematic mobile device use might
reduce their level of dependence.
6. Acknowledgments
e English version of this article has been reviewed by Marvellous Adebayo to whom we are grateful for his work.
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7. Specic contributions from each autor
Name and Surname
Conception and design of the work
Ana M. Sánchez-Sánchez, Francisca J. Sánchez-Sánchez,
David Ruiz-Muñoz
Methodology
Ana M. Sánchez-Sánchez y Francisca J. Sánchez-Sánchez,
David Ruiz-Muñoz
Data collection and analysisAna M. Sánchez-Sánchez y Francisca J. Sánchez-Sánchez
Discussion and conclusions
Ana M. Sánchez-Sánchez, Francisca J. Sánchez-Sánchez,
David Ruiz-Muñoz
Drafting, formatting, version review and approval
David Ruiz-Muñoz, Ana M. Sánchez-Sánchez, Francisca J.
Sánchez- Sánchez
8. Conict of interest
e authors declare that there is no conict of interest contained in this article.
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doxa.comunicación |
nº 38, pp.19-39
January-June of 2024
Ana María Sánchez-Sánchez, Francisca Jesús Sánchez-Sánchez and David Ruiz-Muñoz
ISSN: 1696-019X / e-ISSN: 2386-3978
| 39
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