2.1 Youths’ Uses of Smartphones

Young people have always shown a closer link to the Internet and mobile technologies. For instance, text messaging by mobile phones became very popular at the onset of the new millennium (^{Haste, 2005}). A little later, in the first 2010s, manufacturers began to incorporate front and rear cameras in their mobiles to satisfy the demand for video recording and photos, including selfies. Thus, cellphones transmuted from being devices to comply with security and business functions to meet social and aesthetic needs, especially among the younger users (^{Ramirez-Correa et al., 2020}).

Already in 2010, ^{Kohut et al. (2010}) found that millennials outperformed previous generations in virtually all uses of the Internet and mobile phones. According to these authors, young people prefer functions and applications to catch and interchange images, photographs, and audiovisual content through mass social interactions (^{Thulin, 2018}). ^{Canavilhas et al. (2020}) recorded more than 300,000 social interactions and concluded that users mostly use smartphones to check on social network apps, as cell phones are no longer used to talk but to text, play and watch. The former mentioned is consistent with the study conducted by ^{Bury (2017}), which showed that many young people consume TV on their smartphones.

Not all younger users feel the same intensive connection with their smartphones and use them for the same reasons. According to ^{Bettina & Hedvig (2019}), there are significant differences in socioeconomic segments, age and gender. A recent Gallup survey confirmed this. The percentage of young Americans who say they use their smartphones “too much” has sharply risen in recent years, with women showing slightly more concern: 81% of Americans ages 18-29 years say they use their phones too long, being the generation with the most intensive use of smartphones in America. Older generations seem less concerned: 47% of the US population between 50 and 64 believe they use their smartphones too much. Only 30% of people over 65 share this belief (^{Saad, 2022}).

Scholars have researched the influence of smartphones on a broad set of social, mental, emotional and health factors. For instance, they have analyzed the association of smartphones with academic performance (^{Amez & Baert, 2020}; ^{Baert et al., 2019}; ^{Glass & Kang, 2019}), and have related academic stress with smartphone addiction (^{Shen et al., 2021}; ^{Xu et al., 2019}), finding that “depression played a significant role in the relationship between academic stress and smartphone addiction” (Shen et al., 2021, p. 733). Some scholars have also established that the higher the use of smartphones for news and information, the more the levels of political participation. However, this relation is moderated by individual’s level of education (^{Kim et al., 2016}). They have also tried to measure or relate the use of smartphones with sleep alterations (^{Carter et al., 2016}); anxiety, loneliness, depression (Boumosleh & Jaalouk, 2017); satisfaction with life (Samaha & Hawi, 2016); social relationships (Chen & Peng, 2008); substance addictions (Ho et al., 2014); and attention deficit and hyperactivity disorder (Ho et al., 2014).

Smartphones can engage people through the applications people download and use, and the exposure to content people get through the Internet, social media, WhatsApp, etc. This engagement/use can be productive or unproductive, positive or negative to themselves. For instance, quality content can enhance social and language skills, whilst inappropriate content can have the opposite effect, as learning with mobile phones can be positive or negative (^{Ponti et al., 2017}). According to Griffith et al. (2020), educational apps can have a productive learning effect on young children, whilst other scholars have established a negative association between smartphones and academic performance (^{Amez & Baert, 2020}; R. S. ^{Chen & Ji, 2015}; ^{Lepp et al., 2014}; ^{Li et al., 2015}; ^{Olufadi, 2015}; ^{Wentworth & Middleton, 2014}).

A negative effect on mental health is another problem commonly mentioned (Veissière & Stendel, 2018). ^{King & Dong (2017}) concluded that excessive use of mobile phones could be related to psychological, behavioural d performance problems, having a substantial impact on addiction to them.

Among the most quoted risks when studying smartphones’ impact on youths are cyberbullying, risky contact with strangers, sexual messaging (sexting) and pornography (^{Livingstone & Smith, 2014}). These are periodically the subject of considerable public concern among parents, educators, and clinicians.

Children and teenagers can also be affected by smartphones as these devices monopolize children’s time displacing other positive activities they need to do to reach a healthy social and emotional balance, such as quality face-to-face interactions with parents (^{Ponti et al., 2017}). ^{Stevic et. al (2021}) found conflicting outcomes in this regard, as the face-to-face communication could be strengthened or deteriorated by the specific smartphone’ use. “Given that smartphones can be used anytime and anywhere, such a displacing effect of interpersonal communication with close ties could even amplify” (Stevic et al., 2021, p. 793). According to ^{Pedersen et al. (2022}), limiting screen time could have a positive effect on health and physical activity.

Another problem commonly associated with digital media and smartphones is the increased odds of suffering attention-deficit/hyperactivity disorder (ADHD). Frequent distractions could disrupt the normative development of sustained attention and organization skills. ^{Ra et al. (2018}) concluded that smartphones and other digital media devices had an incomparable potential to engage users excessively due to their higher operating speed, high level of stimulation, and potential for high-frequency exposure: “Modern media devices immediately notify users when new text messages, social media postings, or videogame play invitations arrive. Exposure to such notifications may draw attention away from focal tasks. Additionally, modern media platforms provide instantaneous access to highly stimulating experiences and rapid feedback in response to user input” (Ra et al., 2018, p. 261).

Some authors have highlighted the association of smartphones with youth’s mental disorders, saying that since the irruption of these portable devices, different social, emotional, and health problems among the younger populations have exploded (^{Twenge, Joiner, et al., 2018}). Depressive symptoms, suiciderelated outcomes, and suicide deaths among adolescents have consistently risen since smartphones flooded global markets in the 2010s (^{Twenge, Joiner, et al., 2018}; ^{Twenge, Martin, et al., 2018}).

The Covid-19 pandemic appeared accentuating all these problems, as rates of anxiety and depression between children and adolescents have doubled during the pandemic: depression affected 1 out of 4 children and youth, and problematic anxiety to 1 in 5. “A comparison of these findings to pre-pandemic estimates (12.9% for depression and 11.6% for anxiety) suggests that youth mental health difficulties during the COVID-19 pandemic has likely doubled”, wrote ^{Racine et. al (2021}, p. 1148). They added: “The COVID-19 pandemic, and its associated restrictions and consequences, appear to have taken a considerable toll on youth and their psychological well-being. Loss of peer interactions, social isolation, and reduced contact with buffering supports (e.g., teachers, coaches) may have precipitated these increases” (p. 1148).

Depression and, less commonly, anxiety can lead to suicidal symptoms: In 2019, almost 25% of girls and more than 10% of boys seriously considered suicide (^{Gleason & Thompson, 2022}). Based on this data, some authors and institutions have remarked that American children and tweens face mental-health crises (^{Tingley, 2022}).

Some authors say smartphones and social media are unfairly blamed for negatively affecting people when individuals with distinctive characteristics tend to use digital devices properly or unproperly. For instance, it could be unfair to say that smartphones displace face-to-face

communication with friends and relatives. For Valkenburg et al. (2021), it is incorrect to blame social media for leading people to negative feelings. They confirmed that social media can lead to positive, negative, or no effects among individuals, depending on various dispositional, developmental, social, and situational antecedents, mediators, and moderators. Consequently, it would rely more on the specific person and not on the kind of device or app, what feeling is produced by the exposure to interactive content. Some authors say that “engaging in various forms of social media is a routine activity that research has shown to benefit children and adolescents by enhancing communication, social connection, and even technical skills” (^{O’Keeffe et al., 2011}, p. 800).

2.2 Uses and Gratifications Theory

What motivates young people to access multimedia services through their smartphones? The uses and gratifications theory (UGT) developed by ^{Katz and Blumler (1973}) can help us address this question. UGT focuses on identifying audiences’ needs and the relationship between a person’s selection of a specific medium and the gratification she obtains in engaging its content.

There are five central assumptions undergirding the UGT: a) audiences are active rather than passive; b) the choice of using a particular medium to meet certain gratifications depends on each user’s willingness to engage it; c) competing media exist; d) the audience can explain its decisions; e) and value judgments are avoided when the research is conducted (^{Ruggiero, 2000}). Although the theory has been widely applied in the media management and economics literature (^{Albarran, 2006}), the rapid development of the Internet and new information and communication technologies has encouraged UGT’s broader application. For instance, Ruggiero (2000) asserts that the increased presence and influence of computer-mediated communication has revived UGT’s significance. Similarly, ^{Sundar and Limperos (2013}) propose that as media technologies become more and more affordable, new user needs emerge, “giving rise to new and distinctive gratifications” that should be studied (p. 504).

Not surprisingly, research on social media has applied the uses and gratifications theory. For instance, ^{Dunne, Lawlor and Rowley (2010}) used UGT to explore how young people use social networking sites to manage their identities. ^{Korhan and Ersoy (2016}) investigated social networking site applications and what factors make them particularly appealing to users. For their part, ^{Ha, Kim, Libaque-Saenz, Chang and Park (2015}) concluded that the “mobile convenience variable” triggers cognitive gratifications by enabling users to gain information quickly and easily, thereby increasing interactive gratification by facilitating user communication with acquaintances. Other authors, such as ^{Chan-Olmsted, Lee and Kim (2011}), have determined that portability, convenience, and customization are the most salient gratifications millennials identify while using their smartphones.

^{Leung and Wei (2000}) employed UGT to compare mobile phones with landlines and found some gender differences, as men tended to use their mobile phones more for professional purposes while women engaged in more social uses, with longer calls. Leung and Wei (2000) also confirmed that people communicated through their mobile phones in search of four significant perks: mobility, immediacy, instrumentality (instrument or a means to an end), and sociability. Ten years later, ^{Hostut (2010}) conducted a similar study, concluding that mobile users had four different primary motivations: sociability, relaxation, social status and being fashionable. “Being aware of things” was a critical drive to download applications for accessing to sources of information according to ^{Logan (2017}). In a more market-oriented study, ^{van Weezel and Benavides (2009}) explored the uses and preferences of 18-25 mobile phone users in Santiago, Chile, to yield three consumer archetypes: traditional, musical, and heavy user. Mobile phones allowed traditional youths to talk privately and escape parental control, musicals related to value listening to music downloaded, and heavy users were technology experts who required advanced phone features, such as Internet access, email accounts and various apps and functions.

A study of American millennials by ^{Ezumah (2013}) showed similar results. College students used the new media platforms and devices to keep in touch with friends (98.9%), share photos (81.7%), stay in touch with family (79.3%), and other spending-time reasons (70.9%). Facebook emerged as the students’ preferred short message network (SMS), followed by Twitter. As predicted by UGT, motives for using Facebook were found to be important factors in relation to dependency. The UGT was also applied to Facebook’s users by ^{Ferris and Hollenbaugh (2018}), who found that motivations and individual psychological characteristics played direct and indirect relationships of social media’s dependency. For instance, people with lower self-esteem and social cohesion were more dependent upon Facebook.

^{Saeed and Hassan (2020}) examined smartphone-related publications with the UGT between 2016 and 2019. After analyzing 25 papers, they found that most studies focused on two areas: those exploring the full use of the phone and its applications and concentrating on specific health, fitness or dating applications.

^{Su and Chen (2020}) explored smartphones’ gratifications considering them as multitasking medium and audiovisual consumption tools. ^{Gentina and Rowe (2020}) studied two groups of gratifications obtained from smartphone use in adolescents: the social one, which focuses on relationship building, and the one that focuses on the enjoyment of using it. In both cases, if people do not manage the time spent on the device correctly, overuse could relate to dependency. To this end, they recommended “increasing outdoor activities that divert attention away from the use of the phone while performing such actions” (p. 10).

The UGT has received some criticism in the past. According to ^{Shade, Kornfield and Oliver (2015}), one of its significant flaws is that it relies on self-reports, assuming that audiences are active rather than passive actors. However, the self-report research methodology has been broadly validated (^{Bourque & Fielder, 1995}; ^{Churchill Jr et al., 1985}; ^{Román & Iacobucci, 2010}). Self-reporting is a very practical and convenient research method commonly applied in the social sciences (Bourque & Fielder, 1995; ^{del Valle & Zamora, 2021}). There are a lot of benefits using self-reporting in the social sciences field, being the greatest the lowest cost compared with other methods. Besides a lower cost, self-reporting presents advantages regarding sampling (extending the geographic coverage and getting larger samples), implementation (self-administered questionnaires are much easier to implement than other kinds of surveys), timing (all members of the sample can receive the questionnaire simultaneously) and dealing with sensitive topics (people can be more likely to give complete and truthful information about sensitive topics in a self-administered questionnaire). Self-report also has disadvantages: response rates are often low; and researchers must rely on the visual acuity and literacy level of potential respondents (Bourque & Fielder, 1995).

^{Sundar and Limperos (2013}) criticized the UGT for identifying users’ general motivations to use technologies, not specific ones. In this regard, we should note that this study identifies and assesses specific uses and motivations declared by Chilean millennials who actively use their smartphones.

4.1 Descriptive Data

We can observe the evolution of different apps used by young Chilean mobile owners in table 1. These applications are commonly downloaded on portable devices and regularly used until some of them reach a technological natural death. As we can observe, the popularity of most digital applications fluctuates significantly, as some disappear over time. For instance, Facebook was not a popular app in 2009 but was very cool in 2015 (92% of young Chilean respondents had this digital outlet then). Nowadays, Facebook has lost some appeal-only 58% of subjects downloaded this app in 2021-as Facebook has been partly replaced by other emergent and cooler digital killing-times, such as WhatsApp (full penetration in the young Chilean group), Instagram (98% of penetration), Spotify (86%), Tik-Tok (67%) and streaming channels, as Netflix (62%). It is interesting to note how the habit of listening music with portable devices has quickly changed over time. Some years ago, people used to “store” and “download” music into their phones, and, for this reason, young people used to associate smartphones with Ipods and CD players. However, with streaming apps as IMusic or Spotify, youths do not need to gather songs into their devices but rather listening online music through cloud computing service providers. Also, with the irruption of teleworking and online education, Zoom has also taken a significant place on the mobile phones of young people: 50% of subjects have downloaded this application. Another surprising finding is the reduction of digital gaming. This decrease could be based on that people spending a significant amount of time on this activity do it now with consoles as Nintendo or Xbox, but more research is needed to try to explain the causes of this significant change.

Table 2 details how young Chileans have been significantly talking less on the phone over the years. In 2009, half of those surveyed acknowledged talking on the phone for more than 1 hour. In 2021, only 11% of respondents spoke on the phone for more than 1 hour. A dramatic change can be observed in this regard: young people now use their mobile phones more frequently to communicate with others through instant texting and voice messages, sharing messages on social media, and sending and receiving photos, videos, reels, memes, gifs, and emojis.

In 2015, male users reported talking on their smartphones for an average of 12 minutes daily, while female users averaged 15 minutes daily. In 2021, male users talked by smartphone for 26 minutes per day, and female users, for 21 minutes. We can assume that this moderate increase can be attributed partly to Covid-19, as people needed to communicate more with others than before because of continuous quarantines, curfews, and restrictions to travel and mobilization.

Table 3 reports how much time young Chileans text and some significant differences by gender arise: while young male Chileans text daily for a couple of hours, maintaining this average over time, female participants exhibited a slight decrease between 2021 and 2015, probably because of the Covid-19 changed habits (^{Sañudo et al., 2020}). Some reports have shown that the time checking on social media decreased during the pandemic years. For instance, the Statista Digital Economy Compass report (^{Statista, 2021}) concluded that the global average time spent on social media by internet users decreased in 2021 to 142 minutes per day, from 145 minutes per day reported in 2020 and 2019. It was the first time-decrease since 2012, when people used to spend 90 minutes per day checking on social media. The time-decrease observed in our survey in this regard seems to be coherent with those measures.

Anyway, young people spend a considerable amount of time texting on the phone, which is commonly shared with other activities, such as watching TV, driving, commuting, studying, eating, and different varied ways of multitasking: men spend around 2 hours texting on the phone daily according to our survey, and women almost 3. It is a highly significant gap between both genders-the t-test assuming equal variances has a p=.00. However, the gap of means of the amount of time spent by men and women is reduced to less than an hour compared to the previous study. This can partly be explained by the increase in voice messaging among young people today.

Table 4 illustrates young people’s different motivations for using their smartphones. Motivations such as checking on Instagram, keeping in touch with family and friends, and sharing videos and photos were relevant for almost 100% of the young Chileans. Other drivers such as listening to music and getting Spotify access were “critical” at the time of using their smartphones for 94% of young Chileans. And getting Internet access, catching up with the news, and checking on new emails were fundamental for near 90% of young Chileans.

Most motivations to use smartphones measured in our survey of 2021 were the same as measured in 2009, but others were new. For instance, Tik-Tok did not exist in 2009 (designed in 2016) but was a very appealing app in 2021. The same happened with streaming services such as Disney+ or HBO MAX. Apart from these emergent functions, most motivations to use smartphones were the same in 2009 and 2021. This also seems to demonstrate how audiovisual content is consumed today and how it has changed over time (Dasgupta & Grover, 2019). A film or a TV series can be watched at home at night or on the way to university or work. This explains why OTT applications become relevant for young people.

We conducted a comparison of means to measure how the importance of these motivations changed over time. Firstly, we averaged the responses given by respondents in 2009 and 2021 at the time of evaluating the importance they gave to the use of different apps and functions incorporated into their smartphones-being 1= very important, 2= important, 3= unimportant, and 4= highly unimportant. Means from 2009 and 2021 were compared with a t-test, and some significant differences arose. For instance, for spending leisure time, the smartphone was significantly more critical in 2021 than in 2009. The same can be observed for motivations such as “sharing videos and photos”, “listening to music”, “watching YouTube”, “checking on Facebook”, and “getting news and information”. The importance of all these motivations increased significantly between 2021 and 2009, indicating that the smartphone is today an inseparable accessory for almost all young people.

“Keeping in touch with family and friends” has been the most vital motivation to use the mobile phone (average in 2021 = 1.04 vs average in 2009 = 1.1). Still, this social function now competes with other musical or entertainment roles, such as listening to music (average in 2021 = 1.07 vs average in 2009 = 2.61), as the smartphone has replaced former devices specifically oriented to reproduce CDs, MP3 and old cassettes.

4.2 Factor Analysis and Regression

An exploratory factor analysis was conducted to determine the profile of smartphone users. Factor analysis is a valuable and suitable statistic technique to look for structure among a set of variables and summarize the information contained in those many variables, reducing them into a smaller group of new, composite factors (^{Hair et al., 2000}, 2006; ^{Mazzocchi, 2008}). In this case, with the uses and motivations detailed in Table 4, we got a parsimonious set of reasons to use smartphones, and a new group of condensed interests.

Before the factor analysis, we successfully conducted two tests recommended by ^{Hair, Bush, & Ortinau (2006})-the Bartlett test of sphericity and the Kaiser-Meyer-Olkin measure of sampling adequacy (KMO-MSA). The first one is a statistical test for the presence of correlations among variables, and our sample showed a highly significant correlation (p= .00). The second one is an index ranging from 0 to 1, reaching 1 when each variable is perfectly predicted without error by other variables. The KMO-MSA of our sample was .780.

Following validated criteria recommended by ^{Hair, Bush, & Ortinau (2000}), five principal components were extracted using the Varimax rotation method, which explained 52% of the variance. All of them were considered significant, reaching eigenvalues greater than 1. They all showed factor loadings greater than .30, considered appropriate for our sample size (n= 846).

We labelled the first factor “binge-watching” for motivations primarily associated with checking on YouTube and other popular streaming services such as Netflix, Apple+, Amazon Prime Video, etc. “Binge-watching” is the practice of viewing entertainment or informational content for a prolonged time. The second factor was coined as “clubby”, for friendly and pleasant activities. This factor reduced variables regarding smartphone use to check on Instagram or Tik-Tok, share videos and photos, and spend leisure time. The third condensed group was called “organized”, summarizing variables such as using the smartphone to plan, check the personal schedule, and achieve safer financial issues. The fourth factor was named “connected”, grouping activities linked to checking email regularly, surfing the Internet browser, and keeping in touch with family and friends. The last group of variables was coined “musical” for activities like listening to music and getting Spotify access.

Factor scores are composite measures of each factor computed for each sample subject. They represent the degree to which each score is high on the group of items with high loadings on a factor (^{Hair et al., 2000}). With the factor analysis scores, we conducted two linear regression analyses.

For the first regression model, we introduced the five summarized dimensions as independent variables (“binge-watching”, “clubby”, “organized”, “connected”, and “musical”). The independent variable was the time our young Chilean sample spent texting-minutes texting during a day. This model reached a high level of significance (p= .002) and a low size effect (R ² = .024). With this model, two factors reached an appropriate level of significance: “clubby” and “organized”.

For the second regression model, we introduced the five summarized dimensions as independent variables, plus three control variables, i. e. age, gender and socioeconomic level (high, medium and low). This second model also showed a high level of significance (p= .001), with the size effect increasing (R ² = .048). Collinearity statistics confirmed that predictors were independent: tolerance and variance inflation factor of predictors exhibited acceptable levels, indicating that independent variables did not overlap. Two factors were relevant to explain the variance of the dependent variable (time spent texting): “organized” and the control variable gender. Consequently, both models set up a positive and significant relationship between being organized with the time spent texting.