essay review about smartphone

Smartphone Essay

500 words essay on smartphone.

Smartphones have become a very important form of communication these days. It is impossible for a rational person to deny the advantages of smartphones as they are devices suitable for a wide variety of tasks. Let us try to understand smartphones along with their benefits with this smartphone essay.

                                                                                                                                    Smartphone Essay

Understanding the Smartphone

A smartphone is a mobile device that facilitates the combination of cellular and mobile computing functions into one single unit. Moreover, smartphones have stronger hardware capabilities and extensive mobile operating systems in comparison to feature phones.

The strong operating systems of smartphones make possible multimedia functionality, wider software, and the internet including web browsing. They also support core phone functions like text messaging and voice calls.

There are a number of metal–oxide–semiconductor (MOS) integrated circuit (IC) chips within a smartphone. Moreover, such chips include various sensors whose leveraging is possible by their software.

The marketing of early smartphones was primarily towards the enterprise market. Furthermore, the attempt of the smartphone manufacturers was to bridge the functionality of standalone personal digital assistant (PDA) devices along with support for cellular telephony. However, the early smartphones had problems of slow analogue cellular network, short battery life, and bulky size.

With the passage of time, experts were able to resolve these issues. Furthermore, this became possible with faster digital mobile data networks, miniaturization of MOS transistors down to sub-micron levels, and exponential scaling. Moreover, the development of more mature software platforms led to enhancement in the capability of smartphones.

Benefits of Smartphone

People can make use of smartphones to access the internet and find out information regarding almost anything. Furthermore, due to the portability of a smartphone, people can access the internet from any location, even while travelling.

Smartphones have greatly increased the rate of work. This is possible because smartphones facilitate a highly efficient and quick form of communication from anywhere. For example, a person can participate in an official business meeting, without wasting time, from the comfort of his home via a live video chat application of a smartphone.

Smartphones can also be of tremendous benefit to students in general. Furthermore, students can quickly resolve any issue related to studies by accessing the internet , using a calculator, reading a pdf file, or contacting a teacher. Most noteworthy, all of this is possible due to the smartphone.

People can get in touch with the larger global community by communicating and sharing their views via social media. Furthermore, this provides a suitable platform to express their views, conduct business with online transactions , or find new people or jobs. One can do all that from anywhere, thanks to the smartphone.

These were just a few benefits of smartphones. Overall, the total benefits of a smartphone are just too many to enumerate here. Most importantly, smartphones have made our lives more efficient as well as comfortable.

Get the huge list of more than 500 Essay Topics and Ideas

Conclusion of Smartphone Essay

Smartphones have proven to be a revolution for human society. Furthermore, they have made the whole world united like never before. In spite of its demerits, there is no doubt that the smartphone is a tremendous blessing to mankind and it will continue to play a major role in its development.

FAQs For Smartphone Essay

Question 1: How is a smartphone different from a feature phone?

Answer 1: Smartphones have stronger hardware capabilities and extensive mobile operating systems when compared to feature phones. Furthermore, the smartphone can perform almost all computing functions that a feature phone can’t. The internet and camera capabilities of a feature phone are nowhere near as powerful as that of a smartphone.

Question 2: What is meant by a smartphone?

Answer 2: A smartphone refers to a handheld electronic device that facilitates a connection to a cellular network. Furthermore, smartphones let people access the internet, make phone calls, send text messages, along with a wide variety of functions that one can perform on a pc or a laptop. Overall, it is a fully functioning miniaturized computer.

Customize your course in 30 seconds

Which class are you in.

• Travelling Essay
• Picnic Essay
• Our Country Essay
• My Parents Essay
• Essay on Favourite Personality
• Essay on Memorable Day of My Life
• Essay on Knowledge is Power
• Essay on Gurpurab
• Essay on My Favourite Season
• Essay on Types of Sports

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Download the App

The impact of smartphone use on learning effectiveness: A case study of primary school students

• Published: 11 November 2022
• Volume 28 , pages 6287–6320, ( 2023 )

Cite this article

• Jen Chun Wang 1 ,
• Chia-Yen Hsieh   ORCID: orcid.org/0000-0001-5476-2674 2 &
• Shih-Hao Kung 1  

108k Accesses

7 Citations

2 Altmetric

Explore all metrics

This study investigated the effects of smartphone use on the perceived academic performance of elementary school students. Following the derivation of four hypotheses from the literature, descriptive analysis, t testing, one-way analysis of variance (ANOVA), Pearson correlation analysis, and one-way multivariate ANOVA (MANOVA) were performed to characterize the relationship between smartphone behavior and academic performance with regard to learning effectiveness. All coefficients were positive and significant, supporting all four hypotheses. We also used structural equation modeling (SEM) to determine whether smartphone behavior is a mediator of academic performance. The MANOVA results revealed that the students in the high smartphone use group academically outperformed those in the low smartphone use group. The results indicate that smartphone use constitutes a potential inequality in learning opportunities among elementary school students. Finally, in a discussion of whether smartphone behavior is a mediator of academic performance, it is proved that smartphone behavior is the mediating variable impacting academic performance. Fewer smartphone access opportunities may adversely affect learning effectiveness and academic performance. Elementary school teachers must be aware of this issue, especially during the ongoing COVID-19 pandemic. The findings serve as a reference for policymakers and educators on how smartphone use in learning activities affects academic performance.

Similar content being viewed by others

Smartphone Usage, Social Media Engagement, and Academic Performance: Mediating Effect of Digital Learning

Learning Hard or Hardly Learning: Smartphones in the University’s Classrooms

The smartphone in self-regulated learning and student success: clarifying relationships and testing an intervention

Avoid common mistakes on your manuscript.

1 Introduction

The advent of the Fourth Industrial Revolution has stimulated interest in educational reforms for the integration of information and communication technology (ICT) into instruction. Smartphones have become immensely popular ICT devices. In 2019, approximately 96.8% of the global population had access to mobile devices with the coverage rate reaching 100% in various developed countries (Sarker et al., 2019 ). Given their versatile functions, smartphones have been rapidly integrated into communication and learning, among other domains, and have become an inseparable part of daily life for many. Smartphones are perceived as convenient, easy-to-use tools that promote interaction and multitasking and facilitate both formal and informal learning (Looi et al., 2016 ; Yi et al., 2016 ). Studies have investigated the impacts of smartphones in education. For example, Anshari et al. ( 2017 ) asserted that the advantages of smartphones in educational contexts include rich content transferability and the facilitation of knowledge sharing and dynamic learning. Modern students expect to experience multiple interactive channels in their studies. These authors also suggested incorporating smartphones into the learning process as a means of addressing inappropriate use of smartphones in class (Anshari et al., 2017 ). For young children, there are differences in demand and attributes and some need for control depending upon the daily smartphone usage of the children (Cho & Lee, 2017 ). To avoid negative impacts, including interference with the learning process, teachers should establish appropriate rules and regulations. In a study by Bluestein and Kim ( 2017 ) on the use of technology in the classroom they examined three themes: acceptance of tablet technology, learning excitement and engagement, and the effects of teacher preparedness and technological proficiency. They suggested that teachers be trained in application selection and appropriate in-class device usage. Cheng et al. ( 2016 ) found that smartphone use facilitated English learning in university students. Some studies have provided empirical evidence of the positive effects of smartphone use, whereas others have questioned the integration of smartphone use into the academic environment. For example, Hawi and Samaha ( 2016 ) investigated whether high academic performance was possible for students at high risk of smartphone addiction. They provided strong evidence of the adverse effects of smartphone addiction on academic performance. Lee et al. ( 2015 ) found a negative correlation between smartphone addiction and learning in university students. There has been a lot of research on the effectiveness of online teaching, but the results are not consistent. Therefore, this study aims to further explore the effects of independent variables on smartphone use behavior and academic performance.

The COVID-19 pandemic has caused many countries to close schools and suspend in-person classes, enforcing the transition to online learning. Carrillo and Flores ( 2020 ) suggested that because of widespread school closures, teachers must learn to manage the online learning environment. Online courses have distinct impacts on students and their families, requiring adequate technological literacy and the formulation of new teaching or learning strategies (Sepulveda-Escobar & Morrison, 2020 ). Since 2020, numerous studies have been conducted on parents’ views regarding the relationship of online learning, using smartphones, computers, and other mobile devices, with learning effectiveness. Widely inconsistent findings have been reported. For instance, in a study by Hadad et al. ( 2020 ), two thirds of parents were opposed to the use of smartphones in school, with more than half expressing active opposition ( n  = 220). By contrast, parents in a study by Garbe et al. ( 2020 ) agreed to the school closure policy and allowed their children to use smartphones to attend online school. Given the differences in the results, further scholarly discourse on smartphone use in online learning is essential.

Questions remain on whether embracing smartphones in learning systems facilitates or undermines learning (i.e., through distraction). Only a few studies have been conducted on the impacts of smartphone use on academic performance in elementary school students (mostly investigating college or high school students). Thus, we investigated the effects of elementary school students’ smartphone use on their academic performance.

2 Literature review

Mobile technologies have driven a paradigm shift in learning; learning activities can now be performed anytime, anywhere, as long as the opportunity to obtain information is available (Martin & Ertzberger, 2013 ).

Kim et al. ( 2014 ) focused on identifying factors that influence smartphone adoption or use. Grant and Hsu ( 2014 ) centered their investigation on user behavior, examining the role of smartphones as learning devices and social interaction tools. Although the contribution of smartphones to learning is evident, few studies have focused on the connection between smartphones and learning, especially in elementary school students. The relationship between factors related to learning with smartphones among this student population is examined in the following sections.

2.1 Behavioral intentions of elementary school students toward smartphone use

Children experience rapid growth and development during elementary school and cultivate various aspects of the human experience, including social skills formed through positive peer interactions. All these experiences exert a substantial impact on the establishment of self-esteem and a positive view of self. Furthermore, students tend to maintain social relationships by interacting with others through various synchronous or asynchronous technologies, including smartphone use (Guo et al., 2011 ). Moreover, students favor communication through instant messaging, in which responses are delivered rapidly. However, for this type of interaction, students must acquire knowledge and develop skills related to smartphones or related technologies which has an impact on social relationships (Kang & Jung, 2014 ; Park & Lee, 2012 ).

Karikoski and Soikkeli ( 2013 ) averred that smartphone use promotes human-to-human interaction both through verbal conversation and through the transmission of textual and graphic information, and cn stimulate the creation and reinforcement of social networks. Park and Lee ( 2012 ) examined the relationship between smartphone use and motivation, social relationships, and mental health. The found smartphone use to be positively correlated with social intimacy. Regarding evidence supporting smartphone use in learning, Firmansyah et al. ( 2020 ) concluded that smartphones significantly benefit student-centered learning, and they can be used in various disciplines and at all stages of education. They also noted the existence of a myriad smartphone applications to fulfill various learning needs. Clayton and Murphy ( 2016 ) suggested that smartphones be used as a mainstay in classroom teaching, and that rather than allowing them to distract from learning, educators should help their students to understand how smartphones can aid learning and facilitate civic participation. In other words, when used properly, smartphones have some features that can lead to better educational performance. For example, their mobility can allow students access to the same (internet-based) services as computers, anytime, anywhere (Lepp et al., 2014 ). Easy accessibility to these functionalities offers students the chance to continuously search for study-related information. Thus, smartphones can provide a multi-media platform to facilitate learning which cannot be replaced by simply reading a textbook (Zhang et al., 2014 ). Furthermore, social networking sites and communication applications may also contribute to the sharing of relevant information. Faster communication between students and between students and faculty may also contribute to more efficient studying and collaboration (Chen et al., 2015 ). College students are more likely to have access to smartphones than elementary school students. The surge in smartphone ownership among college students has spurred interest in studying the impact of smartphone use on all aspects of their lives, especially academic performance. For example, Junco and Cotton ( 2012 ) found that spending a fair amount of time on smartphones while studying had a negative affect on the university student's Grade Point Average (GPA). In addition, multiple studies have found that mobile phone use is inversely related to academic performance (Judd, 2014 ; Karpinski et al., 2013 ). Most research on smartphone use and academic performance has focused on college students. There have few studies focused on elementary school students. Vanderloo ( 2014 ) argued that the excessive use of smartphones may cause numerous problems for the growth and development of children, including increased sedentary time and reduced physical activity. Furthermore, according to Sarwar and Soomro ( 2013 ), rapid and easy access to information and its transmission may hinder concentration and discourage critical thinking and is therefore not conducive to children’s cognitive development.

To sum up, the evidence on the use of smartphones by elementary school students is conflicting. Some studies have demonstrated that smartphone use can help elementary school students build social relationships and maintain their mental health, and have presented findings supporting elementary students’ use of smartphones in their studies. Others have opposed smartphone use in this student population, contending that it can impede growth and development. To take steps towards resolving this conflict, we investigated smartphone use among elementary school students.

In a study conducted in South Korea, Kim ( 2017 ) reported that 50% of their questionnaire respondents reported using smartphones for the first time between grades 4 and 6. Overall, 61.3% of adolescents reported that they had first used smartphones when they were in elementary school. Wang et al. ( 2017 ) obtained similar results in an investigation conducted in Taiwan. However, elementary school students are less likely to have access to smartphones than college students. Some elementary schools in Taiwan prohibit their students from using smartphones in the classroom (although they can use them after school). On the basis of these findings, the present study focused on fifth and sixth graders.

Jeong et al. ( 2016 ), based on a sample of 944 respondents recruited from 20 elementary schools, found that people who use smartphones for accessing Social Network Services (SNS), playing games, and for entertainment were more likely to be addicted to smartphones. Park ( 2020 ) found that games were the most commonly used type of mobile application among participants, comprised of 595 elementary school students. Greater smartphone dependence was associated with greater use of educational applications, videos, and television programs (Park, 2020 ). Three studies in Taiwan showed the same results, that elementary school students in Taiwan enjoy playing games on smartphones (Wang & Cheng, 2019 ; Wang et al., 2017 ). Based on the above, it is reasonable to infer that if elementary school students spend more time playing games on their smartphones, their academic performance will decline. However, several studies have found that using smartphones to help with learning can effectively improve academic performance. In this study we make effort to determine what the key influential factors that affect students' academic performance are.

Kim ( 2017 ) reported that, in Korea, smartphones are used most frequentlyfrom 9 pm to 12 am, which closely overlaps the corresponding period in Taiwan, from 8 to 11 pm In this study, we not only asked students how they obtained their smartphones, but when they most frequently used their smartphones, and who they contacted most frequently on their smartphones were, among other questions. There were a total of eight questions addressing smartphone behavior. Recent research on smartphones and academic performance draws on self-reported survey data on hours and/or minutes of daily use (e.g. Chen et al., 2015 ; Heo & Lee, 2021 ; Lepp et al., 2014 ; Troll et al., 2021 ). Therefore, this study also uses self-reporting to investigate how much time students spend using smartphones.

Various studies have indicated that parental attitudes affect elementary school students’ behavioral intentions toward smartphone use (Chen et al., 2020 ; Daems et al., 2019 ). Bae ( 2015 ) determined that a democratic parenting style (characterized by warmth, supervision, and rational explanation) was related to a lower likelihood of smartphone addiction in children. Park ( 2020 ) suggested that parents should closely monitor their children’s smartphone use patterns and provide consistent discipline to ensure appropriate smartphone use. In a study conducted in Taiwan, Chang et al. ( 2019 ) indicated that restrictive parental mediation reduced the risk of smartphone addiction among children. In essence, parental attitudes critically influence the behavioral intention of elementary school students toward smartphone use. The effect of parental control on smartphone use is also investigated in this study.

Another important question related to student smartphone use is self-control. Jeong et al. ( 2016 ) found that those who have lower self-control and greater stress were more likely to be addicted to smartphones. Self-control is here defined as the ability to control oneself in the absence of any external force, trying to observe appropriate behavior without seeking immediate gratification and thinking about the future (Lee et al., 2015 ). Those with greater self-control focus on long-term results when making decisions. People are able to control their behavior through the conscious revision of automatic actions which is an important factor in retaining self-control in the mobile and on-line environments. Self-control plays an important role in smartphone addiction and the prevention thereof. Previous studies have revealed that the lower one’s self-control, the higher the degree of smartphone dependency (Jeong et al., 2016 ; Lee et al., 2013 ). In other words, those with higher levels of self-control are likely to have lower levels of smartphone addiction. Clearly, self-control is an important factor affecting smartphone usage behavior.

Reviewing the literature related to self-control, we start with self-determination theory (SDT). The SDT (Deci & Ryan, 2008 ) theory of human motivation distinguishes between autonomous and controlled types of behavior. Ryan and Deci ( 2000 ) suggested that some users engage in smartphone communications in response to perceived social pressures, meaning their behavior is externally motivated. However, they may also be  intrinsically  motivated in the sense that they voluntarily use their smartphones because they feel that mobile communication meets their needs (Reinecke et al., 2017 ). The most autonomous form of motivation is referred to as intrinsic motivation. Being intrinsically motivated means engaging in an activity for its own sake, because it appears interesting and enjoyable (Ryan & Deci, 2000 ). Acting due to social pressure represents an externally regulated behavior, which SDT classifies as the most controlled form of motivation (Ryan & Deci, 2000 ). Individuals engage in such behavior not for the sake of the behavior itself, but to achieve a separable outcome, for example, to avoid punishment or to be accepted and liked by others (Ryan & Deci, 2006 ). SDT presumes that controlled and autonomous motivations are not complementary, but “work against each other” (Deci et al., 1999 , p. 628). According to the theory, external rewards alter the perceived cause of action: Individuals no longer voluntarily engage in an activity because it meets their needs, but because they feel controlled (Deci et al., 1999 ). For media users, the temptation to communicate through the smartphone is often irresistible (Meier, 2017 ). Researchers who have examined the reasons why users have difficulty controlling media use have focused on their desire to experience need gratification, which produces pleasurable experiences. The assumption here is that users often subconsciously prefer short-term pleasure gains from media use to the pursuit of long-term goals (Du et al., 2018 ). Accordingly, self-control is very important. Self-control here refers to the motivation and ability to resist temptations (Hofmann et al., 2009 ). Dispositional self-control is a key moderator of yielding to temptation (Hofmann et al., 2009 ). Ryan and Deci ( 2006 ) suggested that people sometimes perform externally controlled behaviors unconsciously, that is, without applying self-control.

Sklar et al. ( 2017 ) described two types of self-control processes: proactive and reactive. They suggested that deficiencies in the resources needed to inhibit temptation impulses lead to failure of self-control. Even when impossible to avoid a temptation entirely, self-control can still be made easier if one avoids attending to the tempting stimulus. For example, young children instructed to actively avoid paying attention to a gift and other attention-drawing temptations are better able to resist the temptation than children who are just asked to focus on their task. Therefore, this study more closely investigates students' self-control abilities in relation to smartphone use asking the questions, ‘How did you obtain your smartphone?’ (to investigate proactivity), and ‘How much time do you spend on your smartphone in a day?’ (to investigate the effects of self-control).

Thus, the following hypotheses are advanced.

Hypothesis 1: Smartphone behavior varies with parental control.

Hypothesis 2: Smartphone behavior varies based on students' self-control.

2.2 Parental control, students' self-control and their effects on learning effectiveness and academic performance

Based on Hypothesis 1 and 2, we believe that we need to focus on two factors, parental control and student self-control and their impact on academic achievement. In East Asia, Confucianism is one of the most prevalent and influential cultural values which affect parent–child relations and parenting practice (Lee et al., 2016 ). In Taiwan, Confucianism shapes another feature of parenting practice: the strong emphasis on academic achievement. The parents’ zeal for their children’s education is characteristic of Taiwan, even in comparison to academic emphasis in other East Asian countries. Hau and Ho ( 2010 ) noted that, in Eastern Asian (Chinese) cultures, academic achievement does not depend on the students’ interests. Chinese students typically do not regard intelligence as fixed, but trainable through learning, which enables them to take a persistent rather than a helpless approach to schoolwork, and subsequently perform well. In Chinese culture, academic achievement has been traditionally regarded as the passport to social success and reputation, and a way to enhance the family's social status (Hau & Ho, 2010 ). Therefore, parents dedicate a large part of their family resources to their children's education, a practice that is still prevalent in Taiwan today (Hsieh, 2020 ). Parental control aimed at better academic achievement is exerted within the behavioral and psychological domains. For instance, Taiwan parents tightly schedule and control their children’s time, planning private tutoring after school and on weekends. Parental control thus refers to “parental intrusiveness, pressure, or domination, with the inverse being parental support of autonomy” (Grolnick & Pomerantz, 2009 ). There are two types of parental control: behavioral and psychological. Behavioral control, which includes parental regulation and monitoring over what children do (Steinberg et al., 1992 ), predict positive psychosocial outcomes for children. Outcomes include low externalizing problems, high academic achievement (Stice & Barrera, 1995 ), and low depression. In contrast, psychological control, which is exerted over the children’s psychological world, is known to be problematic (Stolz et al., 2005 ). Psychological control involves strategies such as guilt induction and love withdrawal (Steinberg et al., 1992 ) and is related with disregard for children’s emotional autonomy and needs (Steinberg et al., 1992 ). Therefore, it is very important to discuss the type of parental control.

Troll et al. ( 2021 ) suggested that it is not the objective amount of smartphone use but the effective handling of smartphones that helps students with higher trait self-control to fare better academically. Heo and Lee ( 2021 ) discussed the mediating effect of self-control. They found that self-control was partially mediated by those who were not at risk for smartphone addiction. That is to say, smartphone addiction could be managed by strengthening self-control to promote healthy use. In an earlier study Hsieh and Lin ( 2021 ), we collected 41 international journal papers involving 136,491students across 15 countries, for meta-analysis. We found that the average and majority of the correlations were both negative. The short conclusion here was that smartphone addiction /reliance may have had a negative impact on learning performance. Clearly, it is very important to investigate the effect of self-control on learning effectiveness with regard to academic performance.

2.3 Smartphone use and its effects on learning effectiveness and academic performance

The impact of new technologies on learning or academic performance has been investigated in the literature. Kates et al. ( 2018 ) conducted a meta-analysis of 39 studies published over a 10-year period (2007–2018) to examine potential relationships between smartphone use and academic achievement. The effect of smartphone use on learning outcomes can be summarized as follows: r  =  − 0.16 with a 95% confidence interval of − 0.20 to − 0.13. In other words, smartphone use and academic achievement were negatively correlated. Amez and Beart ( 2020 ) systematically reviewed the literature on smartphone use and academic performance, observing the predominance of empirical findings supporting a negative correlation. However, they advised caution in interpreting this result because this negative correlation was less often observed in studies analyzing data collected through paper-and-pencil questionnaires than in studies on data collected through online surveys. Furthermore, this correlation was less often noted in studies in which the analyses were based on self-reported grade point averages than in studies in which actual grades were used. Salvation ( 2017 ) revealed that the type of smartphone applications and the method of use determined students’ level of knowledge and overall grades. However, this impact was mediated by the amount of time spent using such applications; that is, when more time is spent on educational smartphone applications, the likelihood of enhancement in knowledge and academic performance is higher. This is because smartphones in this context are used as tools to obtain the information necessary for assignments and tests or examinations. Lin et al. ( 2021 ) provided robust evidence that smartphones can promote improvements in academic performance if used appropriately.

In summary, the findings of empirical investigations into the effects of smartphone use have been inconsistent—positive, negative, or none. Thus, we explore the correlation between elementary school students’ smartphone use and learning effectiveness with regard to academic performance through the following hypotheses:

Hypothesis 3: Smartphone use is associated with learning effectiveness with regard to academic performance.

Hypothesis 4: Differences in smartphone use correspond to differences in learning effectiveness with regard to academic performance.

Hypotheses 1 to 4 are aimed at understanding the mediating effect of smartphone behavior; see Fig.  1 . It is assumed that smartphone behavior is the mediating variable, parental control and self-control are independent variables, and academic performance is the dependent variable. We want to understand the mediation effect of this model.

Model 1: Model to test the impact of parental control and students’ self-control on academic performance

Thus, the following hypotheses are presented.

Hypothesis 5: Smartphone behaviors are the mediating variable to impact the academic performance.

2.4 Effects of the COVID-19 pandemic on smartphone use for online learning

According to 2020 statistics from the United Nations Educational, Scientific and Cultural Organization (UNESCO), since the start of the COVID-19 pandemic, full or partial school closures have affected approximately 800 million learners worldwide, more than half of the global student population. Schools worldwide have been closed for 14 to 22 weeks on average, equivalent to two thirds of an academic year (UNESCO, 2021 ). Because of the pandemic, instructors have been compelled to transition to online teaching (Carrillo & Flores, 2020 ). According to Tang et al. ( 2020 ), online learning is among the most effective responses to the COVID-19 pandemic. However, the effectiveness of online learning for young children is limited by their parents’ technological literacy in terms of their ability to navigate learning platforms and use the relevant resources. Parents’ time availability constitutes another constraint (Dong et al., 2020 ). Furthermore, a fast and stable Internet connection, as well as access to devices such as desktops, laptops, or tablet computers, definitively affects equity in online education. For example, in 2018, 14% of households in the United States lacked Internet access (Morgan, 2020 ). In addition, the availability and stability of network connections cannot be guaranteed in relatively remote areas, including some parts of Australia (Park et al., 2021 ). In Japan, more than 50% of 3-year-old children and 68% of 6-year-old children used the Internet in their studies, but only 21% of households in Thailand have computer equipment (Park et al., 2021 ).

In short, the COVID-19 pandemic has led to changes in educational practices. With advances in Internet technology and computer hardware, online education has become the norm amid. However, the process and effectiveness of learning in this context is affected by multiple factors. Aside from the parents’ financial ability, knowledge of educational concepts, and technological literacy, the availability of computer equipment and Internet connectivity also exert impacts. This is especially true for elementary school students, who rely on their parents in online learning more than do middle or high school students, because of their short attention spans and undeveloped computer skills. Therefore, this study focuses on the use of smartphones by elementary school students during the COVID-19 pandemic and its impact on learning effectiveness.

3.1 Participants

Participants were recruited through stratified random sampling. They comprised 499 Taiwanese elementary school students (in grades 5 and 6) who had used smartphones for at least 12 months. Specifically, the students advanced to grades 5 or 6 at the beginning of the 2018–2019 school year. Boys and girls accounted for 47.7% and 52.3% ( n  = 238 and 261, respectively) of the sample.

3.2 Data collection and measurement

In 2020, a questionnaire survey was conducted to collect relevant data. Of the 620 questionnaires distributed, 575 (92.7%) completed questionnaires were returned. After 64 participants were excluded because they had not used their smartphones continually over the past 12 months and 14 participants were excluded for providing invalid responses, 499 individuals remained. The questionnaire was developed by one of the authors on the basis of a literature review. The questionnaire content can be categorized as follows: (1) students’ demographic characteristics, (2) smartphone use, (3) smartphone behavior, and (4) learning effectiveness. The questionnaire was modified according to evaluation feedback provided by six experts. Exploratory and confirmatory factor analyses were conducted to test the structural validity of the questionnaire. Factor analysis was performed using principal component analysis and oblique rotation. From the exploratory factor analysis, 25 items (15 and 10 items on smartphone behavior and academic performance as constructs, respectively) were extracted and confirmed. According to the results of the exploratory factor analysis, smartphone behavior can be classified into three dimensions: interpersonal communication, leisure and entertainment, and searching for information. Interpersonal communication is defined as when students use smartphones to communicate with classmates or friends, such as in response to questions like ‘I often use my smartphone to call or text my friends’. Leisure and entertainment mean that students spend a lot of their time using their smartphones for leisure and entertainment, e.g. ‘I often use my smartphone to listen to music’ or ‘I often play media games with my smartphone’. Searching for information means that students spend a lot of their time using their smartphones to search for information that will help them learn, such as in response to questions like this ‘I often use my smartphone to search for information online, such as looking up words in a dictionary’ or ‘I will use my smartphone to read e-books and newspapers online’.

Academic performance can be classified into three dimensions: learning activities, learning applications, and learning attitudes. Learning activities are when students use their smartphones to help them with learning, such as in response to a question like ‘I often use some online resources from my smartphone to help with my coursework’. Learning applications are defined as when students apply smartphone software to help them with their learning activities, e.g. ‘With a smartphone, I am more accustomed to using multimedia software’. Learning attitudes define the students’ attitudes toward using the smartphone, with questions like ‘Since I have had a smartphone, I often find class boring; using a smartphone is more fun’ (This is a reverse coded item). The factor analysis results are shown in the appendix (Appendix Tables 10 , 11 , 12 , 13 and 14 ). It can be seen that the KMO value is higher than 0.75, and the Bartlett’s test is also significant. The total variance explained for smartphone behavior is 53.47% and for academic performance it is 59.81%. These results demonstrate the validity of the research tool.

In this study, students were defined as "proactive" if they had asked their parents to buy a smartphone for their own use and "reactive" if their parents gave them a smartphone unsolicited (i.e. they had not asked for it). According to Heo and Lee ( 2021 ), students who proactively asked their parents to buy them a smartphone gave the assurance that they could control themselves and not become addicted, but if they had been given a smartphone (without having to ask for it), they did not need to offer their parents any such guarantees. They defined user addiction (meaning low self-control) as more than four hours of smartphone use per day (Peng et al., 2022 ).

A cross-tabulation of self-control results is presented in Table 2 , with the columns representing “proactive” and “reactive”, and the rows showing “high self-control” and “low self-control”. There are four variables in this cross-tabulation, “Proactive high self-control” (students promised parents they would not become smartphone addicts and were successful), “Proactive low self-control” (assured their parents they would not become smartphone addicts, but were unsuccessful), “Reactive high self-control”, and “Reactive low self-control”.

Regarding internal consistency among the constructs, the Cronbach's α values ranged from 0.850 to 0.884. According to the guidelines established by George and Mallery ( 2010 ), these values were acceptable because they exceeded 0.7. The overall Cronbach's α for the constructs was 0.922. The Cronbach's α value of the smartphone behavior construct was 0.850, whereas that of the academic performance construct was 0.884.

3.3 Data analysis

The participants’ demographic characteristics and smartphone use (expressed as frequencies and percentages) were subjected to a descriptive analysis. To examine hypotheses 1 and 2, an independent samples t test (for gender and grade) and one-way analysis of variance (ANOVA) were performed to test the differences in smartphone use and learning effectiveness with respect to academic performance among elementary school students under various background variables. To test hypothesis 3, Pearson’s correlation analysis was conducted to analyze the association between smartphone behavior and academic performance. To test hypothesis 4, one-way multivariate ANOVA (MANOVA) was employed to examine differences in smartphone behavior and its impacts on learning effectiveness. To test Hypothesis 5, structural equation modeling (SEM) was used to test whether smartphone behavior is a mediator of academic performance.

4.1 Descriptive analysis

The descriptive analysis (Table 1 ) revealed that the parents of 71.1% of the participants ( n  = 499) conditionally controlled their smartphone use. Moreover, 42.5% of the participants noted that they started using smartphones in grade 3 or 4. Notably, 43.3% reported that they used their parents’ old smartphones; in other words, almost half of the students used secondhand smartphones. Overall, 79% of the participants indicated that they most frequently used their smartphones after school. Regarding smartphone use on weekends, 54.1% and 44.1% used their smartphones during the daytime and nighttime, respectively. Family members and classmates (45.1% and 43.3%, respectively) were the people that the participants communicated with the most on their smartphones. Regarding bringing their smartphones to school, 53.1% of the participants indicated that they were most concerned about losing their phones. As for smartphone use duration, 28.3% of the participants indicated that they used their smartphones for less than 1 h a day, whereas 24.4% reported using them for 1 to 2 h a day.

4.2 Smartphone behavior varies with parental control and based on students' self-control

We used the question ‘How did you obtain your smartphone?’ (to investigate proactivity), and ‘How much time do you spend on your smartphone in a day?’ (to investigate the effects of students' self-control). According to the Hsieh and Lin ( 2021 ), and Peng et al. ( 2022 ), addition is defined more than 4 h a day are defined as smartphone addiction (meaning that students have low self-control).

Table 2 gives the cross-tabulation results for self-control ability. Students who asked their parents to buy a smartphone, but use it for less than 4 h a day are defined as having ‘Proactive high self-control’; students using a smartphone for more than 4 h a day are defined as having ‘Proactive low self-control’. Students whose parents gave them a smartphone but use them for less than 4 h a day are defined as having ‘Reactive high self-control’; students given smart phones and using them for more than 4 h a day are defined as having ‘Reactive low self-control’; others, we define as having moderate levels of self-control.

Tables 3 – 5 present the results of the t test and analysis of covariance (ANCOVA) on differences in the smartphone behaviors based on parental control and students' self-control. As mentioned, smartphone behavior can be classified into three dimensions: interpersonal communication, leisure and entertainment, and information searches. Table 3 lists the significant independent variables in the first dimension of smartphone behavior based on parental control and students' self-control. Among the students using their smartphones for the purpose of communication, the proportion of parents enforcing no control over smartphone use was significantly higher than the proportions of parents enforcing strict or conditional control ( F  = 11.828, p  < 0.001). This indicates that the lack of parental control over smartphone use leads to the participants spending more time using their smartphones for interpersonal communication.

For the independent variable of self-control, regardless of whether students had proactive high self-control, proactive low self-control or reactive low self-control, significantly higher levels of interpersonal communication than reactive high self-control were reported ( F  = 18.88, p  < 0.001). This means that students effectively able to control themselves, who had not asked their parents to buy them smartphones, spent less time using their smartphones for interpersonal communication. However, students with high self-control but who had asked their parents to buy them smartphones, would spend more time on interpersonal communication (meaning that while they may not spend a lot of time on their smartphones each day, the time spent on interpersonal communication is no different than for the other groups). Those without effective self-control, regardless of whether they had actively asked their parents to buy them a smartphone or not, would spend more time using their smartphones for interpersonal communication.

Table 4 displays the independent variables (parental control and students' self-control) significant in the dimension of leisure and entertainment. Among the students using their smartphones for this purpose, the proportion of parents enforcing no control over smartphone use was significantly higher than the proportions of parents enforcing strict or conditional control ( F  = 8.539, p  < 0.001). This indicates that the lack of parental control over smartphone use leads to the participants spending more time using their smartphones for leisure and entertainment.

For the independent variable of self-control, students with proactive low self-control and reactive low self-control reported significantly higher use of smartphones for leisure and entertainment than did students with proactive high self-control and reactive high self-control ( F  = 8.77, p  < 0.001). This means that students who cannot control themselves, whether proactive or passive in terms of asking their parents to buy them a smartphone, will spend more time using their smartphones for leisure and entertainment.

Table 5 presents the significant independent variables in the dimension of information searching. Significant differences were observed only for gender, with a significantly higher proportion of girls using their smartphones to search for information ( t  =  − 3.979, p  < 0.001). Parental control and students' self-control had no significance in the dimension of information searching. This means that the parents' attitudes towards control did not affect the students' use of smartphones for information searches. This is conceivable, as Asian parents generally discourage their children from using their smartphones for non-study related activities (such as entertainment or making friends), but not for learning-related activities. It is also worth noting that student self-control was not significant in relation to searching for information. This means that it makes no difference whether or not students have self-control in their search for learning-related information.

Four notable results are presented as follows.

First, a significantly higher proportion of girls used their smartphones to search for information. Second, if smartphone use was not subject to parental control, the participants spent more time using their smartphones for interpersonal communication and for leisure and entertainment rather than for information searches. This means that if parents make the effort to control their children's smartphone use, this will reduce their children's use of smartphones for interpersonal communication and entertainment. Third, student self-control affects smartphone use behavior for interpersonal communication and entertainment (but not searching for information). This does not mean that they spend more time on their smartphones in their daily lives, it means that they spend the most time interacting with people while using their smartphones (For example, they may only spend 2–3 h a day using their smartphone. During those 2–3 h, they spend more than 90% of their time interacting with people and only 10% doing other things), which is the fourth result.

These results support hypotheses 1 and 2.

4.3 Pearson’s correlation analysis of smartphone behavior and academic performance

Table 6 presents the results of Pearson’s correlation analysis of smartphone behavior and academic performance. Except for information searches and learning attitudes, all variables exhibited significant and positively correlations. In short, there was a positive correlation between smartphone behavior and academic performance. Thus, hypothesis 3 is supported.

4.4 Analysis of differences in the academic performance of students with different smartphone behaviors

Differences in smartphone behavior and its impacts on learning effectiveness with regard to academic performance were examined through. In step 1, cluster analysis was conducted to convert continuous variables into discrete variables. In step 2, a one-way MANOVA was performed to analyze differences in the academic performance of students with varying smartphone behavior. Regarding the cluster analysis results (Table 7 ), the value of the change in the Bayesian information criterion in the second cluster was − 271.954, indicating that it would be appropriate to group the data. Specifically, we assigned the participants into either the high smartphone use group or the low smartphone use group, comprised of 230 and 269 participants (46.1% and 53.9%), respectively.

The MANOVA was preceded by the Levene test for the equality of variance, which revealed nonsignificant results, F (6, 167,784.219) = 1.285, p  > 0.05. Thus, we proceeded to use MANOVA to examine differences in the academic performance of students with differing smartphone behaviors (Table 8 ). Between-group differences in academic performance were significant, F (3, 495) = 44.083, p  < 0.001, Λ = 0.789, η 2  = 0.211, power = 0.999. Subsequently, because academic performance consists of three dimensions, we performed univariate tests and an a posteriori comparison.

Table 9 presents the results of the univariate tests. Between-group differences in learning activities were significant, ( F [1, 497] = 40.8, p  < 0.001, η 2  = 0.076, power = 0.999). Between-group differences in learning applications were also significant ( F [1, 497] = 117.98, p  < 0.001, η 2  = 0.192, power = 0.999). Finally, differences between the groups in learning attitudes were significant ( F [1, 497] = 23.22, p  < 0.001, η 2  = 0.045, power = 0.998). The a posteriori comparison demonstrated that the high smartphone use group significantly outperformed the low smartphone use group in all dependent variables with regard to academic performance. Thus, hypothesis 4 is supported.

4.5 Smartphone behavior as the mediating variable impacting academic performance

As suggested by Baron and Kenny ( 1986 ), smartphone behavior is a mediating variable affecting academic performance. We examined the impact through the following four-step process:

Step 1. The independent variable (parental control and students' self-control) must have a significant effect on the dependent variable (academic performance), as in model 1 (please see Fig.  1 ).

Step 2. The independent variable (parental control and students' self-control) must have a significant effect on the mediating variable (smartphone behaviors), as in model 2 (please see Fig.  2 ).

Step 3. When both the independent variable (parental control and student self-control) and the mediator (smartphone behavior) are used as predictors, the mediating variable (smartphone behavior) must have a significant effect on the dependent variable (academic performance), as in model 3 (please see Fig.  3 ).

Step 4. In model 3, the regression coefficient of the independent variables (parental control and student self-control) on the dependent variables must be less than in mode 1 or become insignificant.

Model 2: Model to test the impact of parental control and students’ self-control on smartphone behavior

Model 3: Both independent variables (parental control and student self-control) and mediators (smartphone behavior) were used as predictors to predict dependent variables

As can be seen in Fig.  1 , parental control and student self-control are observed variables, and smartphone behavior is a latent variable. "Strict" is set to 0, which means "Conditional", with "None" compared to "Strict". “Proactive high self-control” is also set to 0. From Fig.  1 we find that the independent variables have a significant effect on the dependent variable. The regression coefficient of parental control is 0.176, t = 3.45 ( p  < 0.01); the regression coefficient of students’ self-control is 0.218, t = 4.12 ( p  < 0.001), proving the fit of the model (Chi Square = 13.96**, df = 4, GFI = 0.989, AGFI = 0.959, CFI = 0.996, TLI = 0.915, RMSEA = 0.051, SRMR = 0.031). Therefore, the test results for Model 1 are in line with the recommendations of Baron and Kenny ( 1986 ).

As can be seen in Fig.  2 , the independent variables have a significant effect on smartphone behaviors. The regression coefficient of parental control is 0.166, t = 3.11 ( p  < 0.01); the regression coefficient of students’ self-control is 0.149, t = 2.85 ( p  < 0.01). The coefficients of the model fit are: Chi Square = 15.10**, df = 4, GFI = 0.988, AGFI = 0.954, CFI = 0.973, TLI = 0.932, RMSEA = 0.052, SRMR = 0.039. Therefore, the results of the test of Model 2 are in line with the recommendations of Baron and Kenny ( 1986 ).

As can be seen in Fig.  3 , smartphone behaviors have a significant effect on the dependent variable. The regression coefficient is 0.664, t = 10.2 ( p  < 0.001). The coefficients of the model fit are: Chi Square = 91.04**, df = 16, GFI = 0.958, AGFI = 0.905, CFI = 0.918, TLI = 0.900, RMSEA = 0.077, SRMR = 0.063. Therefore, the results of the test of Model 3 are in line with the recommendations of Baron and Kenny ( 1986 ).

As can be seen in Fig.  4 , the regression coefficient of the independent variables (parental control and student self-control) on the dependent variables is less than in model 1, and the parental control variable becomes insignificant. The regression coefficient of parental control is 0.013, t = 0.226 ( p  > 0.05); the path coefficient of students’ self-control is 0.155, t = 3.07 ( p  < 0.01).

Model 4: Model three’s regression coefficient of the independent variables (parental control and student self-control) on the dependent variables

To sum up, we prove that smartphone behavior is the mediating variable to impact the academic performance. Thus, hypothesis 5 is supported.

5 Discussion

This study investigated differences in the smartphone behavior of fifth and sixth graders in Taiwan with different background variables (focus on parental control and students’ self-control) and their effects on academic performance. The correlation between smartphone behavior and academic performance was also examined. Although smartphones are being used in elementary school learning activities, relatively few studies have explored their effects on academic performance. In this study, the proportion of girls who used smartphones to search for information was significantly higher than that of boys. Past studies have been inconclusive about gender differences in smartphone use. Lee and Kim ( 2018 ) observed no gender differences in smartphone use, but did note that boys engaged in more smartphone use if their parents set fewer restrictions. Kim et al. ( 2019 ) found that boys exhibited higher levels of smartphone dependency than girls. By contrast, Kim ( 2017 ) reported that girls had higher levels of smartphone dependency than boys did. Most relevant studies have focused on smartphone dependency; comparatively little attention has been devoted to smartphone behavior. The present study contributes to the literature in this regard.

Notably, this study found that parental control affected smartphone use. If the participants’ parents imposed no restrictions, students spent more time on leisure and entertainment and on interpersonal communication rather than on information searches. This is conceivable, as Asian parents generally discourage their children from using their smartphones for non-study related activities (such as entertainment or making friends) but not for learning-related activities. If Asian parents believe that using a smartphone can improve their child's academic performance, they will encourage their child to use it. Parents in Taiwan attach great importance to their children's academic performance (Lee et al., 2016 ). A considerable amount of research has been conducted on parental attitudes or control in this context. Hwang and Jeong ( 2015 ) suggested that parental attitudes mediated their children’s smartphone use. Similarly, Chang et al. ( 2019 ) observed that parental attitudes mediated the smartphone use of children in Taiwan. Our results are consistent with extant evidence in this regard. Lee and Ogbolu ( 2018 ) demonstrated that the stronger children’s perception was of parental control over their smartphone use, the more frequently they used their smartphones. The study did not further explain the activities the children engaged in on their smartphones after they increased their frequency of use. In the present study, the participants spent more time on their smartphones for leisure and entertainment and for interpersonal communication than for information searches.

Notably, this study also found that students’ self-control affected smartphone use.

Regarding the Pearson’s correlation analysis of smartphone behavior and academic performance, except for information searches and learning attitudes, all the variables were significantly positively correlated. In other words, there was a positive correlation between smartphone behavior and academic performance. In their systematic review, Amez and Beart ( 2020 ) determined that most empirical results provided evidence of a negative correlation between smartphone behavior and academic performance, playing a more considerable role in that relationship than the theoretical mechanisms or empirical methods in the studies they examined. The discrepancy between our results and theirs can be explained by the between-study variations in the definitions of learning achievement or performance.

Regarding the present results on the differences in the academic performance of students with varying smartphone behaviors, we carried out a cluster analysis, dividing the participants into a high smartphone use group and a low smartphone use group. Subsequent MANOVA revealed that the high smartphone use group academically outperformed the low smartphone use group; significant differences were noted in the academic performance of students with different smartphone behaviors. Given the observed correlation between smartphone behavior and academic performance, this result is not unexpected. The findings on the relationship between smartphone behavior and academic performance can be applied to smartphone use in the context of education.

Finally, in a discussion of whether smartphone behavior is a mediator of academic performance, it is proved that smartphone behavior is the mediating variable impacting academic performance. Our findings show that parental control and students’ self-control can affect academic performance. However, the role of the mediating variable (smartphone use behavior) means that changes in parental control have no effect on academic achievement at all. This means that smartphone use behaviors have a full mediating effect on parental control. It is also found that students’ self-control has a partial mediating effect. Our findings suggest that parental attitudes towards the control of smartphone use and students' self-control do affect academic performance, but smartphone use behavior has a significant mediating effect on this. In other words, it is more important to understand the children's smartphone behavior than to control their smartphone usage. There have been many studies in the past exploring the mediator variables for smartphone use addiction and academic performance. For instance, Ahmed et al. ( 2020 ) found that the mediating variables of electronic word of mouth (eWOM) and attitude have a significant and positive influence in the relationship between smartphone functions. Cho and Lee ( 2017 ) found that parental attitude is the mediating variable for smartphone use addiction. Cho et al. ( 2017 ) indicated that stress had a significant influence on smartphone addiction, while self-control mediates that influence. In conclusion, the outcomes demonstrate that parental control and students’ self-control do influence student academic performance in primary school. Previous studies have offered mixed results as to whether smartphone usage has an adverse or affirmative influence on student academic performance. This study points out a new direction, thinking of smartphone use behavior as a mediator.

In brief, the participants spent more smartphone time on leisure and entertainment and interpersonal communication, but the academic performance of the high smartphone use group surpassed that of the low smartphone use group. This result may clarify the role of students’ communication skills in their smartphone use. As Kang and Jung ( 2014 ) noted, conventional communication methods have been largely replaced by mobile technologies. This suggests that students’ conventional communication skills are also shifting to accommodate smartphone use. Elementary students are relatively confident in communicating with others through smartphones; thus, they likely have greater self‐efficacy in this regard and in turn may be better able to improve their academic performance by leveraging mobile technologies. This premise requires verification through further research. Notably, high smartphone use suggests the greater availability of time and opportunity in this regard. Conversely, low smartphone use suggests the relative lack of such time and opportunity. The finding that the high smartphone use group academically outperformed the low smartphone use group also indicates that smartphone accessibility constitutes a potential inequality in the learning opportunities of elementary school students. Therefore, elementary school teachers must be aware of this issue, especially in view of the shift to online learning triggered by the COVID-19 pandemic, when many students are dependent on smartphones and computers for online learning.

6 Conclusions and implications

This study examined the relationship between smartphone behavior and academic performance for fifth and sixth graders in Taiwan. Various background variables (parental control and students’ self-control) were also considered. The findings provide new insights into student attitudes toward smartphone use and into the impacts of smartphone use on academic performance. Smartphone behavior and academic performance were correlated. The students in the high smartphone use group academically outperformed the low smartphone use group. This result indicates that smartphone use constitutes a potential inequality in elementary school students’ learning opportunities. This can be explained as follows: high smartphone use suggests that the participants had sufficient time and opportunity to access and use smartphones. Conversely, low smartphone use suggests that the participants did not have sufficient time and opportunity for this purpose. Students’ academic performance may be adversely affected by fewer opportunities for access. Disparities between their performance and that of their peers with ready access to smartphones may widen amid the prevalent class suspension and school closure during the ongoing COVID-19 pandemic.

This study has laid down the basic foundations for future studies concerning the influence of smartphones on student academic performance in primary school as the outcome variable. This model can be replicated and applied to other social science variables which can influence the academic performance of primary school students as the outcome variable. Moreover, the outcomes of this study can also provide guidelines to teachers, parents, and policymakers on how smartphones can be most effectively used to derive the maximum benefits in relation to academic performance in primary school as the outcome variable. Finally, the discussion of the mediating variable can also be used as the basis for the future projects.

7 Limitations and areas of future research

This research is significant in the field of smartphone functions and the student academic performance for primary school students. However, certain limitations remain. The small number of students sampled is the main problem in this study. For more generalized results, the sample data may be taken across countries within the region and increased in number (rather than limited to certain cities and countries). For more robust results, data might also be obtained from both rural and urban centers. In this study, only one mediating variable was incorporated, but in future studies, several other psychological and behavioral variables might be included for more comprehensive outcomes. We used the SEM-based multivariate approach which does not address the cause and effect between the variables, therefore, in future work, more robust models could be employed for cause-and-effect investigation amongst the variables.

Data availability

The datasets generated during and/or analysed during the current study are available from the corresponding author upon request.

Ahmed, R. R., Salman, F., Malik, S. A., Streimikiene, D., Soomro, R. H., & Pahi, M. H. (2020). Smartphone Use and Academic Performance of University Students: A Mediation and Moderation Analysis.  Sustainability, 12 (1), 439. MDPI AG. Retrieved from https://doi.org/10.3390/su12010439

Amez, S., & Beart, S. (2020). Smartphone use and academic performance: A literature review. International Journal of Educational Research, 103 , 101618. https://doi.org/10.1016/j.ijer.2020.101618

Article   Google Scholar  

Anshari, M., Almunawar, M. N., Shahrill, M., Wicaksono, D. K., & Huda, M. (2017). Smartphones usage in the classrooms: Learning aid or interference? Education and Information Technologies, 22 , 3063–3079. https://doi.org/10.1007/s10639-017-9572-7

Bae, S. M. (2015). The relationships between perceived parenting style, learning motivation, friendship satisfaction, and the addictive use of smartphones with elementary school students of South Korea: Using multivariate latent growth modeling. School Psychology International, 36 (5), 513–531. https://doi.org/10.1177/0143034315604017

Baron, R. M., & Kenny, D. A. (1986). The moderator–mediator variable distinction in social psychological research: Conceptual, strategic, and statistical considerations. Journal of Personality and Social Psychology, 51 (6), 1173–1182. https://doi.org/10.1037/0022-3514.51.6.1173

Bluestein, S. A., & Kim, T. (2017). Expectations and fulfillment of course engagement, gained skills, and non-academic usage of college students utilizing tablets in an undergraduate skills course. Education and Information Technologies, 22 (4), 1757–1770. https://doi.org/10.1007/s10639-016-9515-8

Carrillo, C., & Flores, M. A. (2020). COVID-19 and teacher education: A literature review of online teaching and learning practices. European Journal of Teacher Education, 43 (4), 466–487. https://doi.org/10.1080/02619768.2020.1821184

Chang, F. C., Chiu, C. H., Chen, P. H., Chiang, J. T., Miao, N. F., Chuang, H. T., & Liu, S. (2019). Children’s use of mobile devices, smartphone addiction and parental mediation in Taiwan. Computers in Human Behavior, 93 , 25–32. https://doi.org/10.1016/j.chb.2018.11.048

Chen, R. S., & Ji, C. H. (2015). Investigating the relationship between thinking style and personal electronic device use and its implications for academic performance. Computers in Human Behavior, 52 , 177–183. https://doi.org/10.1016/j.chb.2015.05.042

Chen, C., Chen, S., Wen, P., & Snow, C. E. (2020). Are screen devices soothing children or soothing parents?Investigating the relationships among children’s exposure to different types of screen media, parental efficacy and home literacy practices. Computers in Human Behavior, 112 , 106462. https://doi.org/10.1016/j.chb.2020.106462

Cheng, Y. M., Kuo, S. H., Lou, S. J., & Shih, R. C. (2016). The development and implementation of u-msg for college students’ English learning. International Journal of Distance Education Technologies, 14 (2), 17–29. https://doi.org/10.4018/IJDET.2016040102

Cho, K. S., & Lee, J. M. (2017). Influence of smartphone addiction proneness of young children on problematic behaviors and emotional intelligence: Mediating self-assessment effects of parents using smartphones. Computers in Human Behavior, 66 , 303–311. https://doi.org/10.1016/j.chb.2016.09.063

Cho, H.-Y., Kim, K. J., & Park, J. W. (2017). Stress and adult smartphone addiction: Mediation by self-control, neuroticism, and extraversion. Stress and Heath, 33 , 624–630. https://doi.org/10.1002/smi.2749

Clayton, K., & Murphy, A. (2016). Smartphone apps in education: Students create videos to teach smartphone use as tool for learning. Journal of Media Literacy Education , 8 , 99–109. Retrieved October 13, 2021. Review from  https://files.eric.ed.gov/fulltext/EJ1125609.pdf

Daems, K., Pelsmacker, P. D., & Moons, I. (2019). The effect of ad integration and interactivity on young teenagers’ memory, brand attitude and personal data sharing. Computers in Human Behavior, 99 , 245–259. https://doi.org/10.1016/j.chb.2019.05.031

Deci, E. L., & Ryan, R. M. (2008). Facilitating optimal motivation and psychological well-being across life’s domains. Canadian Psychology, 49 (1), 14–23. https://doi.org/10.1037/0708-5591.49.1.14

Deci, E. L., Koestner, R., & Ryan, R. M. (1999). A meta-analytic review of experiments examining the effects of extrinsic rewards on intrinsic motivation. Psychological Bulletin, 125 , 627–668. https://doi.org/10.1037//0033-2909.125.6.627

Dong, C., Cao, S., & Li, H. (2020). Young children’s online learning during COVID-19 pandemic: Chinese parents’ beliefs and attitudes. Children and Youth Services Review, 118 , 105440. https://doi.org/10.1016/j.childyouth.2020.105440

Du, J., van Koningsbruggen, G. M., & Kerkhof, P. (2018). A brief measure of social media self-control failure. Computers in Human Behavior, 84 , 68–75. https://doi.org/10.1016/j.chb.2018.02.002

Firmansyah, R. O., Hamdani, R. A., & Kuswardhana, D. (2020). IOP conference series: Materials science and engineering. The use of smartphone on learning activities: Systematic review . In International Symposium on Materials and Electrical Engineering 2019 (ISMEE 2019), Bandung, Indonesia. https://doi.org/10.1088/1757-899X/850/1/012006

Garbe, A., Ogurlu, U., Logan, N., & Cook, P. (2020). COVID-19 and remote learning: Experiences of parents with children during the pandemic. American Journal of Qualitative Research, 4 (3), 45–65. https://doi.org/10.29333/ajqr/8471

George, D., & Mallery, P. (2010). SPSS for windows step by step: A simple guide and reference. 17.0 update (10th ed.). Pearson.

Google Scholar  

Grant, M., & Hsu, Y. C. (2014). Making personal and professional learning mobile: Blending mobile devices, social media, social networks, and mobile apps to support PLEs, PLNs, & ProLNs. Advances in Communications and Media Research Series, 10 , 27–46.

Grolnick, W. S., & Pomerantz, E. M. (2009). Issues and challenges in studying parental control: Toward a new conceptualization. Child Development Perspectives, 3 , 165–170. https://doi.org/10.1111/j.1750-8606.2009.00099.x

Guo, Z., Lu, X., Li, Y., & Li, Y. (2011). A framework of students’ reasons for using CMC media in learning contexts: A structural approach. Journal of the Association for Information Science and Technology, 62 (11), 2182–2200. https://doi.org/10.1002/asi.21631

Hadad, S., Meishar-Tal, H., & Blau, I. (2020). The parents’ tale: Why parents resist the educational use of smartphones at schools? Computers & Education, 157 , 103984. https://doi.org/10.1016/j.compedu.2020.103984

Hau, K.-T., & Ho, I. T. (2010). Chinese students’ motivation and achievement. In M. H. Bond (Ed.), Oxford handbook of Chinese psychology (pp. 187–204). Oxford University Press.

Hawi, N. S., & Samaha, M. (2016). To excel or not to excel: Strong evidence on the adverse effect of smartphone addiction on academic performance. Computers & Education, 98 , 81–89. https://doi.org/10.1016/j.compedu.2016.03.007

Heo, Y. J., & Lee, K. (2021). Smartphone addiction and school life adjustment among high school students: The mediating effect of self-control. Journal of Psychosocial Nursing and Mental Health Services, 56 (11), 28–36. https://doi.org/10.3928/02793695-20180503-06

Hofmann, W., Friese, M., & Strack, F. (2009). Impulse and self-control from a dual-systems perspective. Perspectives on Psychological Science, 4 (2), 162–176. https://doi.org/10.1111/j.1745-6924.2009.01116.x

Hsieh, C. Y. (2020). Predictive analysis of instruction in science to students’ declining interest in science-An analysis of gifted students of sixth - and seventh-grade in Taiwan. International Journal of Engineering Education, 2 (1), 33–51. https://doi.org/10.14710/ijee.2.1.33-51

Hsieh, C. Y., Lin, C. H. (2021). Other important issues. Meta-analysis: the relationship between smartphone addiction and college students’ academic performance . 2021 TERA International Conference on Education. IN National Sun Yat-sen University (NSYSU), Kaohsiung.

Hwang, Y., & Jeong, S. H. (2015). Predictors of parental mediation regarding children’s smartphone use. Cyberpsychology, Behavior, and Social Networking, 18 (12), 737–743. https://doi.org/10.1089/cyber.2015.0286

Jeong, S.-H., Kim, H. J., Yum, J.-Y., & Hwang, Y. (2016). What type of content are smartphone users addicted to? SNS vs. games. Computers in Human Behavior, 54 , 10–17. https://doi.org/10.1016/j.chb.2015.07.035

Judd, T. (2014). Making sense of multitasking: The role of facebook. Computers & Education, 70 , 194–202. https://doi.org/10.1016/j.compedu.2013.08.013

Junco, R., & Cotton, S. R. (2012). No A 4 U: The relationship between multitasking and academic performance. Computers & Education, 59 , 505–514. https://doi.org/10.1016/j.compedu.2011.12.023

Kang, S., & Jung, J. (2014). Mobile communication for human needs: A comparison of smartphone use between the US and Korea. Computers in Human Behavior, 35 , 376–387. https://doi.org/10.1016/j.chb.2014.03.024

Karikoski, J., & Soikkeli, T. (2013). Contextual usage patterns in smartphone communication services. Personal and Ubiquitous Computing, 17 (3), 491–502. https://doi.org/10.1007/s00779-011-0503-0

Karpinski, A. C., Kirschner, P. A., Ozer, I., Mellott, J. A., & Ochwo, P. (2013). An exploration of social networking site use, multitasking, and academic performance among United States and European university students. Computers in Human Behavior, 29 , 1182–1192. https://doi.org/10.1016/j.chb.2012.10.011

Kates, A. W., Wu, H., & Coryn, C. L. S. (2018). The effects of mobile phone use on academic performance: A meta-analysis. Computers & Education, 127 , 107–112. https://doi.org/10.1016/j.compedu.2018.08.012

Kim, K. (2017). Smartphone addiction and the current status of smartphone usage among Korean adolescents. Studies in Humanities and Social Sciences, 2017 (56), 115–142. https://doi.org/10.17939/hushss.2017.56.006

Kim, D., Chun, H., & Lee, H. (2014). Determining the factors that influence college students’ adoption of smartphones. Journal of the Association for Information Science and Technology, 65 (3), 578–588. https://doi.org/10.1002/asi.22987

Kim, B., Jahng, K. E., & Oh, H. (2019). The moderating effect of elementary school students’ perception of open communication with their parents in the relationship between smartphone dependency and school adjustment. Korean Journal of Childcare and Education, 15 (1), 54–73. https://doi.org/10.14698/jkcce.2019.15.01.057

Lee, J., & Cho, B. (2015). Effects of self-control and school adjustment on smartphone addiction among elementary school students. Korea Science, 11 (3), 1–6. https://doi.org/10.5392/IJoC.2015.11.3.001

Lee, E. J., & Kim, H. S. (2018). Gender differences in smartphone addiction behaviors associated with parent-child bonding, parent-child communication, and parental mediation among Korean elementary school students. Journal of Addictions Nursing, 29 (4), 244–254. https://doi.org/10.1097/JAN.0000000000000254

Lee, S. J., & Moon, H. J. (2013). Effects of self-Control, parent- adolescent communication, and school life satisfaction on smart-phone addiction for middle school students. Korean Journal of Human Ecology, 22 (6), 87–598. https://doi.org/10.5934/kjhe.2013.22.6.587

Lee, E. J., & Ogbolu, Y. (2018). Does parental control work with smartphone addiction? Journal of Addictions Nursing, 29 (2), 128–138. https://doi.org/10.1097/JAN.0000000000000222

Lee, J., Cho, B., Kim, Y., & Noh, J. (2015). Smartphone addiction in university students and its implication for learning. In G. Chen, V. K. Kinshuk, R. Huang, & S. C. Kong (Eds.), Emerging issues in smart learning (pp. 297–305). Springer.

Chapter   Google Scholar  

Lee, S., Lee, K., Yi, S. H., Park, H. J., Hong, Y. J., & Cho, H. (2016). Effects of Parental Psychological Control on Child’s School Life: Mobile Phone Dependency as Mediator. Journal of Child and Family Studies, 25 , 407–418. https://doi.org/10.1007/s10826-015-0251-2

Lepp, A., Barkley, J. E., & Karpinski, A. C. (2014). The relationship between cell phone use, academic performance, anxiety, and satisfaction with life in college students. Computers in Human Behavior, 31 , 343–350. https://doi.org/10.1016/j.chb.2013.10.049

Lin, Y. Q., Liu, Y., Fan, W. J., Tuunainen, V. K., & Deng, S. G. (2021). Revisiting the relationship between smartphone use and academic performance: A large-scale study. Computers in Human Behavior, 122 , 106835. https://doi.org/10.1016/j.chb.2021.106835

Looi, C. K., Lim, K. F., Pang, J., Koh, A. L. H., Seow, P., Sun, D., Boticki, I., Norris, C., & Soloway, E. (2016). Bridging formal and informal learning with the use of mobile technology. In C. S. Chai, C. P. Lim, & C. M. Tan (Eds.), Future learning in primary schools (pp. 79–96). Springer.

Martin, F., & Ertzberger, J. (2013). Here and now mobile learning: An experimental study on the use of mobile technology. Computers & Education, 68 , 76–85. https://doi.org/10.1016/j.compedu.2013.04.021

Meier, A. (2017). Neither pleasurable nor virtuous: Procrastination links smartphone habits and messenger checking behavior to decreased hedonic as well as eudaimonic well-being . Paper presented at the 67th Annual Conference of the International Communication Association (ICA), San Diego, CA.

Morgan, H. (2020). Best practices for implementing remote learning during a pandemic. The Clearing House: A Journal of Educational Strategies, Issues and Ideas, 93 (3), 135–141. https://doi.org/10.1080/00098655.2020.1751480

Park, J. H. (2020). Smartphone use patterns of smartphone-dependent children. Korean Academy of Child Health Nursing, 26 (1), 47–54. https://doi.org/10.4094/chnr.2020.26.1.47

Park, N., & Lee, H. (2012). Social implications of smartphone use: Korean college students’ smartphone use and psychological well-being. Cyberpsychology, Behavior, and Social Networking, 15 (9), 491–497. https://doi.org/10.1089/cyber.2011.0580

Park, E., Logan, H., Zhang, L., Kamigaichi, N., & Kulapichitr, U. (2021). Responses to coronavirus pandemic in early childhood services across five countries in the Asia-Pacific region: OMEP Policy Forum. International Journal of Early Childhood, 2021 , 1–18. https://doi.org/10.1007/s13158-020-00278-0

Peng, Y., Zhou, H., Zhang, B., Mao, H., Hu, R., & Jiang, H. (2022). Perceived stress and mobile phone addiction among college students during the 2019 coronavirus disease: The mediating roles of rumination and the moderating role of self-control. Personality and Individual Differences, 185 , 111222. https://doi.org/10.1016/j.paid.2021.111222

Reinecke, L., Aufenanger, S., Beutel, M. E., Dreier, M., Quiring, O., Stark, B., & Müller, K. W. (2017). Digital stress over the life span: The effects of communication load and Internet multitasking on perceived stress and psychological health impairments in a German probability sample. Media Psychology, 20 (1), 90–115. https://doi.org/10.1080/15213269.2015.1121832

Ryan, R. M., & Deci, E. L. (2000). Self-determination theory and the facilitation of intrinsic motivation, social development, and well-being. American Psychologist, 55 (1), 68–78. https://doi.org/10.1037//0003-066x.55.1.68

Ryan, R. M., & Deci, E. L. (2006). Self-regulation and the problem of human autonomy: Does psychology need choice, self-determination, and will? Journal of Personality, 74 , 1557–1585. https://doi.org/10.1111/j.1467-6494.2006.00420.x

Salvation, M. D. (2017). The relationship between smartphone applications usage and students’ academic performance. Computational Methods in Social Sciences , 5 (2), 26–39. Retrieved October 13, 2021. Review from http://cmss.univnt.ro/wp-content/uploads/vol/split/vol_V_issue_2/CMSS_vol_V_issue_2_art.003.pdf

Sarker, I. H., Kayes, A. S. M., & Watters, P. (2019). Effectiveness analysis of machine learning classification models for predicting personalized context-aware smartphone usage. Journal of Big Data, 6 , 57. https://doi.org/10.1186/s40537-019-0219-y

Sarwar, M., & Soomro, T. R. (2013). Impact of smartphone’s on society. European Journal of Scientific Research, 98 (2), 219–226.

Sepulveda-Escobar, P., & Morrison, A. (2020). Online teaching placement during the COVID-19 pandemic in Chile: Challenges and opportunities. European Journal of Teacher Education, 43 (4), 587–607. https://doi.org/10.1080/02619768.2020.1820981

Sklar, A., Rim, S. Y. & Fujita, K. (2017). Proactive and reactive self-control. In D., de Ridder, M., Adriaanse, & K. Fujita (Eds). The Routledge International Handbook of Self-Control in Health and Well-Being (p. 11). Routledge. https://doi.org/10.4324/9781315648576

Steinberg, L., Lamborn, S. D., Dornbusch, S. M., & Darling, N. (1992). Impact of parenting practices on adolescent achievement: Authoritative parenting, school involvement, and encouragement to succeed. Child Development, 63 , 1266–1281. https://doi.org/10.1111/j.1467-8624.1992.tb01694.x

Stice, E., & Barrera, M. (1995). A longitudinal examination of the reciprocal relations between perceived parenting and adolescents’ substance use and externalizing behaviors. Developmental Psychology, 31 , 322–334. https://doi.org/10.1037/0012-1649.31.2.322

Stolz, H. E., Barber, B. K., & Olsen, J. A. (2005). Toward disentangling fathering and mothering: An assessment of relative importance. Journal of Marriage and Family, 67 , 1076–1092. https://doi.org/10.1111/j.1741-3737.2005.00195.x

Tang, T., Abuhmaid, A. M., Olaimat, M., Oudat, D. M., Aldhaeebi, M., & Bamanger, E. (2020). Efficiency of flipped classroom with online-based teaching under COVID-19. Interactive Learning Environments . https://doi.org/10.1080/10494820.2020.1817761

Troll, E. S., Friese, M., & Loschelder, D. D. (2021). How students’ self-control and smartphone-use explain their academic performance. Computers in Human Behavior, 117 , 106624. https://doi.org/10.1016/j.chb.2020.106624

UNESCO. (2021). UNESCO figures show two thirds of an academic year lost on average worldwide due to Covid-19 school closures . Retrieved July 23, 2021. Retrieved, from  https://en.unesco.org/news/unesco-figures-show-two-thirds-academic-year-lost-average-worldwide-due-covid-19-school

Vanderloo, L. M. (2014). Screen-viewing among preschoolers in childcare: A systematic review. BMC Pediatric, 14 , 205. https://doi.org/10.1186/1471-2431-14-205

Wang, T. H., & Cheng, H. Y. (2019). Problematic Internet use among elementary school students: Prevalence and risk factors. Information, Communication & Society, 24 (2), 108–134. https://doi.org/10.1080/1369118X.2019.1645192

Wang, C. J., Chang, F. C., & Chiu, C. H. (2017). Smartphone addiction and related factors among elementary school students in New Taipei City. Research of Educational Communications and Technology, 117 , 67–87. https://doi.org/10.6137/RECT.201712_117.0005

Yi, Y. J., You, S., & Bae, B. J. (2016). The influence of smartphones on academic performance: The development of the technology-to-performance chain model. Library Hi Tech, 34 (3), 480–499. https://doi.org/10.1108/LHT-04-2016-0038

Zhang, M. W. B., Ho, C. S. H., & Ho, C. M. (2014). Methodology of development and students’ perceptions of a psychiatry educational smartphone application. Technology and Health Care, 22 , 847–855. https://doi.org/10.3233/THC-140861

Download references

Acknowledgements

The authors would like to express their gratitude to the school participants in the study.

The work done for this study was financially supported by the Ministry of Science and Technology of Taiwan under project No. MOST 109–2511-H-017–005.

Author information

Authors and affiliations.

Department of Industry Technology Education, National Kaohsiung Normal University, 62, Shenjhong Rd., Yanchao District, Kaohsiung, 82446, Taiwan

Jen Chun Wang & Shih-Hao Kung

Department of Early Childhood Education, National PingTung University, No.4-18, Minsheng Rd., Pingtung City, Pingtung County, 900391, Taiwan

Chia-Yen Hsieh

You can also search for this author in PubMed   Google Scholar

Contributions

Kung and Wang conceived of the presented idea. Kung, Wang and Hsieh developed the theory and performed the computations. Kung and Hsieh verified the analytical methods. Wang encouraged Kung and Hsieh to verify the numerical checklist and supervised the findings of this work. All authors discussed the results and contributed to the final manuscript.

Corresponding author

Correspondence to Chia-Yen Hsieh .

Ethics declarations

Conflict of interest.

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Additional information

Publisher's note.

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Appendix 1 Factor analysis results

Rights and permissions.

Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.

Reprints and permissions

About this article

Wang, J.C., Hsieh, CY. & Kung, SH. The impact of smartphone use on learning effectiveness: A case study of primary school students. Educ Inf Technol 28 , 6287–6320 (2023). https://doi.org/10.1007/s10639-022-11430-9

Download citation

Received : 21 February 2022

Accepted : 24 October 2022

Published : 11 November 2022

Issue Date : June 2023

DOI : https://doi.org/10.1007/s10639-022-11430-9

Share this article

Anyone you share the following link with will be able to read this content:

Sorry, a shareable link is not currently available for this article.

Provided by the Springer Nature SharedIt content-sharing initiative

• Smartphone use
• Learning effectiveness
• Human–computer interface
• Media in education
• Elementary education
• Find a journal
• Publish with us
• Track your research

An official website of the United States government

The .gov means it’s official. Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

The site is secure. The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

• Publications
• Account settings

Preview improvements coming to the PMC website in October 2024. Learn More or Try it out now .

• Advanced Search
• Journal List
• Front Psychol

Smartphones and Cognition: A Review of Research Exploring the Links between Mobile Technology Habits and Cognitive Functioning

While smartphones and related mobile technologies are recognized as flexible and powerful tools that, when used prudently, can augment human cognition, there is also a growing perception that habitual involvement with these devices may have a negative and lasting impact on users’ ability to think, remember, pay attention, and regulate emotion. The present review considers an intensifying, though still limited, area of research exploring the potential cognitive impacts of smartphone-related habits, and seeks to determine in which domains of functioning there is accruing evidence of a significant relationship between smartphone technology and cognitive performance, and in which domains the scientific literature is not yet mature enough to endorse any firm conclusions. We focus our review primarily on three facets of cognition that are clearly implicated in public discourse regarding the impacts of mobile technology – attention, memory, and delay of gratification – and then consider evidence regarding the broader relationships between smartphone habits and everyday cognitive functioning. Along the way, we highlight compelling findings, discuss limitations with respect to empirical methodology and interpretation, and offer suggestions for how the field might progress toward a more coherent and robust area of scientific inquiry.

Introduction

As portable media devices, such as smartphones, have become an increasingly pervasive part of our lives, they have also become increasingly capable of supplementing, or even supplanting, various mental functions. With the capacity to be used as phonebooks, appointment calendars, internet portals, tip calculators, maps, gaming devices, and much more, smartphones seem capable of performing an almost limitless range of cognitive activities for us, and of satisfying many of our affective urges. However, sensationalist articles with titles such as, “Are Smartphones Making Us Dumber?” ( Ellison, 2012 ) and, “Is Your Smartphone Making You Fat and Lazy?” ( Morin, 2013 ) encourage the conclusion that reliance on smartphones and related technologies is not aiding mental functioning, but rather, is having a negative impact on our ability to think, remember, pay attention, and regulate emotion. Some have even made the claim that modern connectedness is “rewiring our brains” to constantly crave instant gratification, and that this threat to our society is “almost as important as climate change” ( Greenfield, 2013 ). Are these simply examples of an older generation once again thinking its “progeny yet more corrupt?” (Horace, 20BC) or is there some evidential legitimacy to these fears?

For all the media attention that this subject garners, the supporting scientific literature is still in its nascent stages. The present paper aims to consolidate and integrate some of the key empirical evidence that has emerged regarding the association between smartphone technology and cognitive and affective functioning. We examine the extant corpus of studies in terms of the specific claims put forth by the researchers who conducted them, and where relevant, offer a consideration of factors that might qualify or limit the generalizability of the findings. As we proceed, we evaluate the domains in which there is reason to be concerned about the growing presence of smart technology in our culture, domains in which smartphone technology may enhance cognitive skills, and domains in which the scientific literature is not mature enough to substantiate such claims. In this discussion, we examine evidence relating to both the acute consequences of media technology use on the performance of ongoing cognitive tasks, as well as the more lasting relationships that may exist between technology usage habits and cognitive abilities. As a snapshot of the current literature related to this topic, we also hope this paper can serve as a resource for those conducting further research in this area.

Challenges and Limitations in Scope

The 21st century has already provided us with a vast array of technological advances that markedly shape the ways by which we interact with the world. In this paper we could not hope to investigate every type of emerging technology, nor would we endeavor to review every psychological implication of the technologies in question. For instance, much has already been written about the impact of violent television and video games on children ( Hartmann et al., 2014 ), and this is one of many topics that fall outside of the scope of the present review. Likewise, this review will not venture into the growing body of research exploring problematic usage of mobile phones and the addiction-like symptoms of overuse ( Bianchi and Phillips, 2005 ; Billieux et al., 2008 ; Kwon et al., 2013 ; Lee et al., 2014 ). Nor will it consider studies exploring the possible effects of radio frequency electromagnetic fields emitted from cellular devices on the human brain and its functioning ( Zubko et al., 2016 ). There is also a growing body of work exploring how technology-related habits may be affecting the development of individuals’ social competencies and emotion reading, and this is yet another topic that has been tackled elsewhere ( Brown, 2014 ; Misra et al., 2014 ; Uhls et al., 2014 ; George and Odgers, 2015 ; Mills, 2016 ) and to which we give little consideration.

To give the present review some focus, we begin with the premise that smartphones are an especially impactful technological development, due to their flexibility of function, portability, and increasing proliferation. Accordingly, we limit the scope of our examination to work that is directly relevant to smartphone-related impacts. Moreover, rather than concentrating on “problem” behavior related to smartphone technology (see e.g., Bianchi and Phillips, 2005 ; Hadlington, 2015 ), we mainly explore evidence regarding the consequences of typical everyday smartphone use. Finally, while a wide array of mental functions might be influenced by smartphone habits, we home in on the impacts in the three domains that are most widely discussed in the lay media and that have garnered some consideration in empirical work: attention, memory, and delay of gratification (reward processing). We then give brief consideration to some emerging work exploring links between smartphone habits, executive functioning, and academic performance.

Some representative studies exploring the relationship between smartphone (and related) habits and cognitive functioning are summarized in Table ​ Table1 1 . Researchers interested in this area of study are faced with many difficulties when developing an empirical approach, and these challenges necessarily pervade our attempt to review the extant literature. To begin, smartphones have become so ubiquitous that it is nearly impossible to employ true experimental methods with random assignment into different technology exposure/access groups. Even when it is possible to find technology-naïve participants, contrasting them with experienced technology users is likely to be a confounded approach, due to disparities in SES, age, resources, and social expectations among groups who differ in their habits. As a result, much of the literature consists of quasi-experimental and correlational studies, from which strong inferences regarding causality cannot be drawn. The few truly experimental studies that have been performed on this topic typically investigate only momentary effects of smartphone use or deprivation on cognition, rather than long-term impacts.

Representative publications exploring associations between technology usage and cognitive domains.

The majority of studies in this field also employ self-report questionnaires that provide only a narrow window into the relevant behaviors, and that may in some cases provide unreliable indices of the target behavior ( Baumgartner et al., 2016 ). Indeed, the limited evidence we have regarding the compatibility between subjective and objective usage measures indicates that self-report estimates of usage are likely to be of limited reliability, and only modestly correlated (if at all) with actual usage ( Andrews et al., 2015 ). Further, because the landscape of technology usage opportunities is ever-evolving, many of the questionnaires that researchers develop turn out to have a limited “half life,” sometimes becoming dated (or obsolete) before they can be applied more broadly across research labs, or used to establish meaningful longitudinal trends in key behaviors ( Roberts et al., 2005 ; Rideout et al., 2010 ). Relatedly, the fact that smartphones are a relatively recent development precludes the existence of any broadly generalizable longitudinal evidence. Thus, even when connections between technology and cognition are established, we do not know the extent to which these impacts are lasting. Another crucial challenge is that it can be difficult to assess technology usage habits without intruding on participants’ natural behavior. Attempts to assess smartphone-related habits (questionnaires, diaries, etc.) can draw the participants’ attention to their patterns of use, which could alter their naturalistic behaviors and affect the way in which participants approach laboratory tasks that are meant to assess the cognitive impacts of such habits. In spite of these many challenges, some foundational research has been conducted, and some intriguing patterns are beginning to emerge. In the following sections, we discuss recent research in the areas of attention, memory and knowledge, delay of gratification, and conclude with a consideration of studies investigating more general effects on academic performance and other domains.

Mobile Technology Use and Attention

A concern that pre-dates smartphone technology is the rising incidence in the diagnosis of attentional difficulties, most specifically ADHD, in children and adolescents (e.g., Visser et al., 2014 ). Considered together with the rise in the prevalence of multimedia devices, this correlation may be perceived by the public to be evidence of a causative relationship. Opportunities and motives to interact with digital media technologies are especially compelling for today’s adolescents, for whom many social interactions take place online. Such trends have spurred the fear that regular engagement with these devices can lead to diminished attentional capacity – producing shorter attention spans and “scatter-brained” tendencies among those who are most invested with the devices (e.g., Egan, 2016 ). One specific manifestation of this concern is that the current generation of children and adolescents are developing increasingly shorter attention spans due to their increased contact with smartphone technology, and use onset at younger ages ( Nikken and Schols, 2015 ).

Here we consider the empirical research concerning the potential impacts of smartphone-related technologies on divided attention and focused attention. Focused attention refers to the capacity to attend to only one source of information while ignoring other incoming stimuli. Focused attention also encompasses sustained attention – the ability to maintain a directed attentional focus over an extended period of time. Conversely, divided attention typically refers to the ability to perform two or more functions simultaneously, otherwise known as multitasking.

Perhaps the most recognizable, and obvious, impact of smartphone technology in our everyday lives is the way in which it can acutely interfere with, or interrupt, ongoing mental and physical tasks. It may be useful to think of smartphone-related interruptions as coming in two forms: endogenous or exogenous. Endogenous interruptions occur when the user’s own thoughts drift toward a smartphone-related activity, and thereby evince an otherwise unsolicited drive to begin interacting with the device. These endogenously driven drifts of attention might arise from a desire for more immediate gratification when ongoing goal-directed activities are not perceived as rewarding ( Melcher, 2013 ), a point to which we return below. Once attention has been shifted to the smartphone for one purpose (e.g., by virtue of a specific notification source), users often then engage in a chain of subsequent task-unrelated acts on the smartphone, thereby extending the period of disruption. Studies exploring these ‘within-phone’ interruptions have found that task completion in one app can be delayed by up to 400% by an unintended interruption from another app ( Leiva et al., 2012 ). And, some evidence suggests that the more “rich” (e.g., including a visual image rather than just text) the information encountered during an interruption, the more detrimental the distraction is likely to be with respect to primary task completion ( Levy et al., 2016 ).

Exogenous interruptions occur when some environmental cue captures the user’s attention. This often involves an alert coming directly from the smartphone itself, but can also involve some other external event that triggers subsequent smartphone use, such as noticing someone else interacting with his or her phone, or being reminded during a live conversation (either explicitly or implicitly) about an activity that can be accomplished on one’s smartphone (email, information search, etc.). Importantly, smartphones are capable of interfering with focused attention even when the user attempts to ignore them. In one recently published study, for instance, researchers demonstrated that exposure to smartphone notifications significantly decreased performance on a concurrent attention-based task, even when the participant did not take the time to view the notification ( Stothart et al., 2015 ). Simply hearing the sound or feeling the vibration that signified the alert was enough to distract the participants and decrease their ability to focus attention on the primary task. The researchers posited that that the notifications prompted task-irrelevant thoughts, which manifested themselves in poorer performance on the primary task.

Further evidence suggests that even the mere awareness of the physical presence of a cell phone may impact cognitive performance. Thornton et al. (2014) conducted a study in which participants were asked to complete two neuropsychological tasks designed to measure executive function and attention, a digit cancelation task and a trail-making task. Each task involved two levels of difficulty. At the start of the experiment, the experimenter “accidentally” left either her cell phone or a notebook on the participant’s desk. Participants in the cell phone condition performed significantly worse on the more difficult parts of the digit cancelation and trail-making task than participants in the notebook condition, but performance on the easier parts of the tasks was similar. The researchers replicated these findings in a follow-up study for which half of the participants were asked to place their own cell phones on their desks. The researchers concluded that the mere presence of a phone is sufficiently distracting to affect cognitive functioning, but only during demanding tasks.

Deleterious effects of smartphones on attention are particularly concerning in situations where attention is crucial for safety, such as in the case of distracted driving. A substantial body of work over the past 12 years has considered the effects of texting on driving abilities using driving simulators or closed tracks. Caird et al. (2014) performed a meta-analysis on this literature and concluded that the act of writing text messages impacts nearly every studied measure of dangerous driving. They reported that texting consistently led to decreased attention to the road, slower response time to hazards, greater lateral variance across the lane, and more crashes. Reading text messages without responding resulted in similar findings, albeit with smaller effect sizes. These findings are particularly troubling given that 31% of adults surveyed in 2011, and 42% of teen drivers surveyed in 2015, reported that they had read or sent text messages while driving in the past 30 days ( Centers for Disease Control and Prevention, 2011 , 2016 ).

Research investigating the direct impacts that interruptions can have on performance is complemented by research on “resumption errors” – errors that arise in task performance that is resumed following an interruption or task-switch ( Monk, 2004 ; Cades et al., 2007 ; Brumby et al., 2013 ). The tendency to commit resumption errors increases steeply when the interruption duration exceeds 15 s ( Monk et al., 2008 ). Smartphone interruptions frequently exceed this 15 s threshold ( Leiva et al., 2012 ), and therefore may be especially deleterious to the resumption of ongoing tasks.

The acute and short-term consequences of having one’s attention distracted away from ongoing tasks is an obvious locus of concern in relation to smartphone habits, but there is also growing fear that the increasingly regular interactions we have with smartphones might also have a more lasting impact on the basic capacity for focused and sustained attention. At this point, very limited empirical evidence lends backing to this concern. Given the lack of longitudinal research in this domain, the best data available are derived from correlational studies. However, findings from those studies are somewhat mixed with respect to the claim that smartphone usage is linked to a diminished attentional capacity beyond the time in which an individual is actively engaged with the device.

One study intimating that smartphone habits diminish sustained attentional abilities was conducted by Lee et al. (2015) . The specific focus of their work was on the connection between an individual’s degree of “addiction” to a smartphone and the ability to achieve “flow.” A flow state relates to sustained attention in that it is “a state of concentration so focused that it amounts to absolute absorption in an activity” ( Csikszentmihalyi et al., 2014 ). Lee et al. (2015) investigated whether one’s specific pattern of smartphone usage could have long-term effects on the ability to achieve a state of flow. The researchers administered three questionnaires to a large sample of university students, measuring level of smartphone addiction, tendency for self-regulated learning, and capacity for learning flow. The results showed that the individuals who scored highest on the smartphone addiction scale scored significantly lower on the self-regulated learning and learning flow scales. The authors suggest that the smartphone addiction causes a reduced ability to achieve flow and to be self-regulated learners. Of course, it is equally possible that individuals who are able to be self-regulated learners and more easily achieve flow are also more capable of controlling their impulses with respect to smartphone usage, and thus scored lower on the smartphone addiction questionnaire, or that smartphone use and learning flow exert bidirectional influences on one another. Given the correlational nature of the data, we cannot infer any directionality for the relationship, but the data at least hint that excessive smartphone usage could have a negative impact on the ability to maintain the form of sustained focused attention assessed by the flow index.

Prior research on the relationship between smartphone technology and cognitive abilities has also explored a form of media-related divided attention, “media multitasking,” which involves the simultaneous use of more than one media technology, often via a smartphone. Despite the obvious link to work on divided attention, studies exploring media-multitasking are generally not focused on the acute impacts of media engagement on concurrent cognitive activities (e.g., how being on one’s smartphones might affect attentiveness to work activities). Rather, media-multitasking studies mostly explore the associations that exist between one’s basic cognitive skills and one’s tendency to engage in simultaneous media-related habits. In a seminal experiment on this behavior, Ophir et al. (2009) developed and validated the Media Multitasking Index (MMI), a rating determined by responses to a self-report questionnaire (the Media Use Questionnaire) that expressly assesses an individual’s media multitasking habits. They then used computer-based behavioral tasks to measure participants’ attentional functioning. The data revealed that those who reported engaging in more media multitasking were also less able to filter environmental distractions (task stimuli that were inessential to the primary task). Additionally, frequent media multitaskers exhibited higher switch-costs in a task-switching paradigm, indicating that they were less able to suppress the activation of task set representations that were no longer relevant to performance ( Monsell, 2003 ). These data suggest that frequent multitasking of this sort may be associated with a tendency toward allowing bottom-up (environmental) inputs to capture attention (and conversely, a stronger tendency toward exploratory information gathering). Some subsequent studies have replicated and extended aspects of this influential paper. For instance, using a shorter form of the Media Use Questionnaire, Moisala et al. (2016) showed that everyday media multitasking is associated with poorer control over attention. Specifically, the participants who had higher MMI scores made significantly more errors on a task measuring their ability to ignore distractors that interfered with task completion. Moreover, Cain and Mitroff (2011) found that the link between distractibility and media multitasking habits was associated specifically with individual differences in the scope of attention [and not differences in working memory; see also Yap and Lim (2013) for related results].

Brain imaging studies exploring potential neural correlates of habitual media multitasking behavior have demonstrated that the associated attentional deficit may be directly manifest in the functioning of the brain’s attentional control circuitry. For instance, concurrent with the behavioral deficit they observed in performance of a focused attention task, Moisala et al. (2016) showed that individuals with higher MMI scores also exhibited relatively increased activity in right prefrontal areas. The authors interpreted this result as evidence that increased daily multitasking leads individuals to experience greater difficulty in recruiting cognitive control resources. Relatedly, Loh and Kanai (2015) found reduced gray matter in the anterior cingulate cortex of frequent media multitaskers, indicating that this habit may have a direct impact on the structural properties of an important locus of attentional control in the brain (though it should be noted that other functions have also been ascribed to this region; Shenhav et al., 2016 ).

While these behavioral and neuroimaging findings are intriguing, some research using MMI scores has failed to reproduce the originally observed associations ( Minear et al., 2013 ; Ralph et al., 2013 , 2015 ). Indeed, some evidence suggests the opposite pattern of relationship – that high MMI scores correlate with better performance on certain attentionally demanding tasks. For instance, Lui and Wong (2012) created a task that required participants to integrate incoming information from multiple sensory modalities (vision and audition). Their findings revealed that individuals who reported heavier multitasking outperformed light multitaskers in their ability to integrate the information arriving from multiple modalities. Findings suggesting an attentional benefit associated with heavier media multitasking are also compatible with studies demonstrating positive and transferable impacts of training, through repetitive task practice, in divided attention tasks ( Dux et al., 2009 ; Karbach and Kray, 2009 ).

Perhaps because the Media Multiuse Questionnaire was the first questionnaire of its kind to be employed in a study published in a major scientific journal, the measure has been widely adopted as an assessment of media-related behavior, and as such, is the basis of many additional empirical studies. In just the few years since its conception, dozens of studies have used MMI scores to investigate the cognitive and psychological impacts of media multitasking (see Table ​ Table1 1 ).

The use of this questionnaire across laboratories and to explore different dimensions of functioning has provided the field some much needed grounding. The Media Multiuse Questionnaire does, however, have some limitations that might constrain the generalizability of these studies. One potential issue is that the MMI is calculated by submitting subject responses into a formula that applies the same weight to each of 132 potential forms of multitasking (the crossing of 12 different media-related behaviors with any of the 11 remaining behaviors). Thus, one’s multitasking score increases by the same amount regardless of the type of multitasking indicated, and regardless of the relative attentional demands of different media activities (or of combining certain activities with others). For instance, the questionnaire treats the tendency to “Play video games” and to “Listen to music” as equivalent, despite the fact that the former typically requires active attentional engagement and the latter is often a passive pursuit. Likewise, specifying a frequent tendency to “Play video games” while “Reading print media” (books) – a challenging pairing – increases one’s MMI score by the same magnitude as “Listening to music” while “Instant messaging” – a less challenging pairing (in general, this measure may give disproportionately high MMI scores to individuals who frequently listen to music). Relatedly, because the measure includes different listings for “Instant messaging” and “Mobile phone text messaging,” individuals who frequently engage in these activities will have disproportionately high scores because their score is doubly weighted by these now functionally equivalent activities (the consequence of an ever-changing technology landscape). Placing the same mathematical weight on all forms of multitasking included in this index likely muddies the outcomes, making it difficult to distinguish those media multitaskers who engage in the types of difficult pairings [like those used as the basis of training in studies showing beneficial effects of practice with divided attention; e.g., Dux et al. (2009) , Karbach and Kray (2009) ] from those who are just prone to distracting themselves with secondary sources of input (like music). The limited specificity of the MMI might also account for the recent observation that individuals who fall somewhere in the middle of the media multitasking spectrum may perform better on attentionally demanding tasks than either high or low media multitasking participants ( Cardoso-Leite et al., 2014 ).

While media multitasking appears, at least under certain circumstances, to be negatively correlated with the ability to task-switch and filter distractions, one form of media included on the questionnaire has been associated with improvements in multitasking: action video games. As Cardoso-Leite et al. (2015) note, it may seem paradoxical that media multitasking is related to poorer multitasking performance whereas the single task of playing a video game leads to improved multitasking performance. Nonetheless, positive associations between gaming and skills like selective attention, sustained attention, task-switching, and visual short-term memory have been demonstrated in numerous correlational and experimental studies (for a review, see Green and Bavelier, 2012 ). These associations appear to be specific to the genre known as “action video games” (e.g., first-person shooters), rather than strategy games or role-playing games. Action games require high cognitive and perceptual loads, divided visual processing, and feedback learning with a complex reward schedule, and seem to specifically improve pattern recognition and the metacognitive process of “learning-to-learn” ( Green and Bavelier, 2012 ). The specificity of this relationship highlights another limitation of the MMI: it does not distinguish between different types of video games. Of note, action video games are typically played on computers or gaming consoles, whereas many popular smartphone games (e.g., Candy Crush, Words with Friends) are strategy games that seem to be less likely to confer similar cognitive advantages.

Attention: Summary

The research reviewed above provides some limited empirical support for claims about the effect of smartphone technology on our attentional capacities. While there is clear evidence that engagement with smart devices can have an acute impact on ongoing cognitive tasks, the evidence on any long-term impacts of smartphone-related habits on attentional functioning is quite thin, and somewhat equivocal. Generally, the evidence does point to a negative relationship between smartphone usage and attention, but correlational and self-report data dominate the literature. Where more controlled assessment of attentional performance has been deployed, such as with media multitasking, the results are mixed, with some studies even yielding a positive relationship with the ability to filter distractions. The limitations of current methods used to measure media-related behavior and wide variation in the specific tasks used to assess attentional performance may account for some mixed results in the literature.

Mobile Technology Use, Memory, and Knowledge

Smartphones provide constant access to an endless and ever-improving database of collective knowledge. Having this access enables people to search for, locate, and learn seemingly any fact that they desire. Prior to the advent of the World Wide Web, the closest available approximation of this sort of resource was a multi-volume encyclopedia, the cost and limited portability of which precluded ubiquitous use. Internet search engines enable anyone on a connected device to have access to an unfathomably large amount of information, often at very low cost. Moreover, smartphone technology allows people to take this information wherever they wish, and access it within a matter of seconds.

Though it may seem as if constant access to a limitless database of knowledge should improve cognition, much has been written about how the rapidly changing landscape of technology is negatively affecting how we remember our own lives, the places we have been, and those with whom we have interacted (e.g., Kuhn, 2010 ; Humphreys and Liao, 2011 ; Pentzold and Sommer, 2011 ; Frith and Kalin, 2015 ; Özkul and Humphreys, 2015 ). However, as with attentional impact, the body of empirical evidence demonstrating tangible effects of mobile media devices on memory and knowledge is limited.

One topic that has been investigated is the oft-cited claim that modern technology is leading us to depend upon our devices to store information for us. In a highly influential and informative study, Sparrow et al. (2011) asked participants to type a series of newly learned trivia facts into a computer. Half of the participants were told that the computer would store their typed information for them and that they would be able to access it later, whereas the other half believed that the information would soon be erased. The individuals who believed they would maintain access to the typed information performed more poorly on a later recall task. Importantly, an explicit instruction to remember the facts vs. not being told to remember had no impact on participants’ rates of recall. This finding, dubbed by the authors as the “Google Effect,” and later referred to by other researchers as “digital amnesia” ( Kaspersky Lab, 2015 ) demonstrates that the expectation of having later access to information can make us less inclined to encode and store that information in long-term memory.

Sparrow et al. (2011) further argued that we are becoming symbiotic with our technology; remembering less actual information and instead committing to memory where such information can be found. To further investigate this theory, the researchers conducted an additional experiment using a design similar to that described above, but with three within-subject conditions. For one third of the questions, participants were simply told that the information they entered was saved. Another third of the questions resulted in the participants being told that the information was saved into one of six pre-determined folders (named FACTS, DATA, INFO, NAMES, ITEMS, and POINTS). The remaining third of the questions were followed by a prompt that informed the participants that the information they typed was immediately deleted. The results of this experiment indicated that participants were better able to recall the name of the folder in which the relevant information was located than the information itself. The authors use this finding to claim that, “the processes of human memory are adapting to the advent of new computing and communication technology” ( Sparrow et al., 2011 , p. 778).

A potential experimental confound that Sparrow et al. do not discuss is the amount of “information” represented by the trivia fact vs. the name of the folder. The authors provide an example fact, “The space shuttle Columbia disintegrated during re-entry over Texas in February 2003.” The complexity of the fact may make it more difficult to memorize than the name of the folder in which the information is stored (i.e., FACTS). Future research should attempt to create more balance between the trivia statements and the folder names.

Barr et al. (2015) recently reported findings from a further exploration of internet access via smartphones and knowledge representation. In keeping with the notion that humans are generally “cognitive misers” ( Kahneman, 2011 ), these authors posited that the tendency to rely on simple heuristics and mental shortcuts extends to the habitual use of internet search engines as a substitute for deep cognitive analysis. In their experiment, Barr et al. (2015) gave participants a series of cognitively demanding questions, including syllogisms, base-rate problems, and a “heuristics and biases” battery. They also assessed participants’ knowledge in different cognitive domains through administration of a numeracy test and a verbal intelligence test. Finally, participants were also asked to provide an estimation of how much time per day they spend on their smartphones overall, as well as an estimation of how much time they spend specifically using internet search engines on their smartphones. The results showed that individuals who reported being heavy users of smartphones also exhibited less analytical “cognitive styles” and poorer performance on the knowledge measures. Moreover, individuals who indicated that they spend a large amount of time using the search engine function on their smartphones scored most poorly on these cognitive measures. Of course, since these results are derived from self-reported data, it is conceivable that participants who highly weight their desire for knowledge may also inflate their memory for (and estimates of) the time they devote to using search engines. Further, given the correlational nature of the research, the results cannot resolve whether, as claimed, frequent search engine use can actually “supplant thinking,” or whether individuals who already have a weaker tendency to engage cognitive analytic strategies also tend to use search engines more frequently [see also Small et al. (2009) and Xavier et al. (2014) for somewhat conflicting findings in older adults].

Interpreted in a different light, Barr et al.’s (2015) results seem counter-intuitive. After all, the tendency to go out of one’s way to seek information and knowledge [e.g., Need for Cognition, ( Cacioppo et al., 1984 )] has been shown to be positively correlated with fluid intelligence ( Fleischhauer et al., 2010 ). Reinterpreted in this way, individuals with higher cognitive scores might have more semantic knowledge already accessible to them, and thus would not need to resort to using their smartphones as often. Moreover, it is possible that those with higher cognitive scores are able to conduct searches more efficiently. Accordingly, they might use their smartphone’s search engine functions just as frequently as those with low scores, but for a shorter duration each time.

Another recent study provides complementary empirical evidence regarding the potential impact of digital media on memories for personally experienced events ( Henkel, 2013 ). In this study, participants were given digital cameras and taken on a tour of an art museum. Though the research was concerned specifically with digital cameras, the fact that nearly all modern smartphones include a digital camera function makes it relevant to the present discussion. Throughout the tour, the participants were told to take pictures of specific objects, and were asked to observe other objects without taking a picture. One day later, the participants were tested on their ability to distinguish objects they had seen during the tour from brand new objects. The results showed that taking photographs diminished memory for observed objects. Specifically, the participants’ who used the camera during their tour showed a poorer ability to recognize objects as having been previously viewed. A further experiment presented in the same paper showed that this effect was mitigated by asking the participants to zoom in on specific features of the objects that they were viewing before taking the picture. Interestingly, zooming in on a specific area did not increase recall accuracy for details specific to that area vs. the work as a whole, but did improve overall memory for the object, suggesting that the improvement was due to a more rich interaction with the object. Additional empirical support for this phenomenon comes from Zauberman et al. (2015) who found that while visual memory is improved by taking photographs, auditory memory of photographed events is impaired. The practice of taking pictures and videos of trivial occurrences in one’s life (and uploading them to a social media site) is increasingly common due to the proliferation of smartphone ownership and the popularity of photo- and video-sharing social apps like Instagram and Snapchat. If taking pictures can lead to weaker encoding of representations in memory, then this is an important facet of the cognitive impact of ubiquitous smartphone usage. Recent qualitative research provides first-hand accounts that one’s interactions with smartphones and the ‘check-in’ capability of some social media apps as well as photos taken with one’s phone help establish a topographical memory that can both supplant and augment one’s memory of their surroundings and experiences ( Özkul and Humphreys, 2015 ).

Studies investigating the relation between digital photography and memory have assumed that photographs are stored or shared in a semi-permanent matter. Thus, while the act of taking photographs may change memory encoding during an event, the photographs provide an opportunity to review and recollect the experience at a later time. However, recent trends in social media use have prioritized ephemeral photo-sharing. For example, Snapchat – a tool rapidly rising in popularity, especially among youth ( Lenhart, 2015 ) – allows user to send and post pictures and videos that can only be viewed a limited number of times or for a finite period (Instagram recently debuted a similar feature). Users may therefore experience the same effects on memory in the moment, without the added opportunity to refer back to the photograph or video as an external source of information/memory. Little is yet known about the specific effects of ephemeral photo-sharing tools on memory for events (which may act on memory in a way that is akin to the soon-to-be-erased files in Sparrow et al., 2011 ).

Another common concern regarding the “offloading” of our semantic memory into a modern technological device regards the impact of GPS mapping systems on our ability to navigate the world. Crafting an accurate cognitive representation of our spatial surroundings is crucial for us to effectively and efficiently get from one place to another. It has been posited that constant reliance on GPS navigation systems, which are now integrated into smartphone devices, interferes with our natural tendency to develop cognitive spatial representations. Media headlines insist that these car technologies are “creating stupid drivers” ( Moskvitch, 2014 ) and there are many compelling instances in which a driver blindly followed an inaccurate GPS direction into peril ( Hansen, 2013 ). As GPS navigation devices pre-exist smartphone technology, so too does the related scientific literature.

In a study published a decade ago, researchers sought to identify the consequences of overreliance on GPS navigational devices ( Burnett and Lee, 2005 ). Specifically, the authors wanted to know whether use of GPS navigational devices impacted their participants’ tendency to create cognitive maps when maneuvering through a novel environment. To do this, Burnett and Lee recruited experienced drivers to navigate around a 3D digitally rendered virtual environment. The virtual environment resembled a medium-sized neighborhood, and included many buildings and other landmarks such as trees, signs, and people. The between-subjects design required half of the participants to study a map of the environment for as long as they wished before hitting the road in an attempt to reach their destination using the most direct route possible. Conversely, the other half of participants were allowed to study the map for only 20 s, and then commenced their journey, which was accompanied with turn-by-turn voice guidance to the destination. After the participants completed the route, their spatial knowledge of the environment was tested according to three facets of spatial representation: Landmark-, Route-, and Survey-level representations. Participants were presented with screen shots of scenes, including some from the virtual environment and some that were similar, but not actually on the route that the participants took. The participants were required to identify which screenshots they recognized as part of the route they took (Landmark) and the order in which they occurred (Route). To assess Survey knowledge of the spatial environment, participants were asked to sketch a map of their overall route as best they could on a blank sheet of paper, and to include as many landmarks as they could remember. The results from this study showed that the participants in the voice navigation group performed significantly worse in Landmark and Route knowledge of the environment. Further, those in the voice navigation group drew significantly simpler and more fragmented maps in the assessment of Survey knowledge.

Some recent research has focused on identifying ways in which the detriments of navigation devices on spatial memory can be mitigated. It has been shown, for example, that spatial knowledge can be improved by allowing users to request that their position be indicated at any given time during the navigation episode ( Parush et al., 2007 ). Further, spatial knowledge can be improved if users are forced to perform mental rotations of on-screen images, as opposed to observing automated rotations ( Boari et al., 2012 ). This knowledge can be applied by encouraging users to keep their navigation devices set such that North is always facing up, rather than moving around the compass as they turn.

Finally, research extending the Ophir et al. (2009) findings on media multitasking also implicates this behavior in memory functioning. Most recently, Uncapher et al. (2015) showed that frequent media multitaskers differed from light users with respect to their working memory capacity, and also exhibited diminished long-term memory functioning. In their study, frequency of media multitasking specifically predicted how participants encoded information, with higher rates of media multitasking leading to less precise representations of goal-relevant information and more task-irrelevant information filling the space. Further, the reduced precision of information in working memory observed in heavy media multitaskers was associated with diminished long-term memory performance, as measured by a surprise recognition test for tested items [with a significant association between heavy media multitasking and memory for target items in the earlier working memory task, as well as a trend level association for memory of distractor items; see also Frein et al. (2013) for related findings].

Memory and Knowledge: Summary

Research investigating the relationships between smartphone technology habits and one’s memory and knowledge capabilities is still scant, but available findings indicate that, as some have worried, smartphone-related habits can in some cases be detrimental to mnemonic functioning. Though there are some important limitations in the experimental designs that have been discussed, the work conducted to date does give us reason to be cautious about how we use new technologies. The available evidence suggests that when we turn to these devices, we generally learn and remember less from our experiences. While the research discussed in this section represents an important step toward investigating the impact of smartphone technology on memory, it is equally important to bear in mind that the sort of “memory externalization” that these articles focus on is by no means a new issue. The same concerns could, for instance, be made regarding a Rolodex. Invented in the 1950s, this ‘rolling index’ provided a system to organize one’s contacts into an easy to access alphabetized structure. It allowed its users to remember where an individual’s contact was located, rather than needing to memorize the full contact information. Determining whether externalizing cognitive processes via smartphone is necessarily worse than externalizing cognitive processes via older methods will be an important avenue for future research.

Mobile Technology Use, Delay of Gratification, and Reward Processing

In addition to their effects on memory and attention, smartphones and related media are often implicated as the cause of a perceived cultural shift toward a necessity for immediate gratification ( Alsop, 2014 ). Indeed, there is a common belief that the current generation of children and teenagers are less capable of waiting for rewards, due in part to the omnipresence of various types of multimedia in their lives ( Richtel, 2010b ). As with the previous sections, the empirical work exploring this claim is still in its nascent stages. In this section, we outline some studies that inform our understanding of the potential impacts that smartphones can have on individuals’ tendencies to choose smaller, more immediate, rewards over larger rewards after a delay, and then offer a summary on the status of the claim.

Some work in this realm has begun by exploring the motivations that drive individuals to engage with media in the first place. In one such study, Wang and Tchernev (2012) investigated media multitasking in terms of the Uses and Gratifications theory ( Katz et al., 1973 ). Based on this theory, “Needs” could be defined as “the combined product of psychological dispositions, sociological factors, and environmental conditions that motivate media consumption” and “Gratifications” as the “perceived fulfillment” of those needs, in this case as a result of media use or exposure (p. 495). In their experiment, Wang and Tchernev (2012) collected self-reported data over a period of 4 weeks. Participants were asked to submit three reports daily, in which they indicated the types of media that they had used in the time that had passed since the previous report, and whether they performed any of these activities simultaneously (i.e., multitasking). The participants were also asked to indicate the specific “motivation” (emotional, cognitive, social, or habitual) that drove them to engage in each media interaction, and the strength of that motivation on a 1–10 scale. The participants indicated the degree to which each “need” was satisfied on a 1–4 scale, and this data was aggregated into “gratification” measures used in data analysis. By comparing the various types and strengths of motivations and gratifications across time points, the experimenters were able to draw interesting conclusions regarding the short-term causes and effects of multimedia interaction. Specifically, participants most often reported that “cognitive” motivations drove their interactions with media devices. However, subjective reports indicated that the ensuing interaction with a media device rarely satisfied the cognitive needs. Instead, participants experienced an emotional gratification that they did not report pursuing in the first place. Ultimately, these emotional gratifications may be driving subsequent media interactions at an unconscious level [for related findings, see Zhang and Zhang (2012) ].

In a study performed in our own lab ( Wilmer and Chein, 2016 ), we used a measure of self-reported mobile technology usage in an attempt to mine the potential relationship with delay of gratification. We observed a significant negative correlation between participants’ mobile technology usage and their “indifference point” and discounting rate in a delay discounting paradigm. Specifically, individuals who were heavier users of mobile technology were also more apt to accept a smaller, more immediate reward than to wait for a more substantial but delayed reward. These findings fit with the popular conception that having constant access to these devices could generate a need for instant gratification. In our study we further observed that the correlation between technology habits and delay of gratification was mediated by individual differences in impulsivity, but not in reward/sensation seeking. This finding partially replicated earlier investigations of the relationship between media use and impulsivity ( Minear et al., 2013 ; Sanbonmatsu et al., 2013 ; Shih, 2013 ). Since the results from all of these studies are entirely correlational, they could simply reveal that people who naturally tend toward more immediate gratification and who give in to impulses more easily also tend to use their mobile devices more often (i.e., there may not be a causal relationship from media use to discounting behavior).

Still, habituating oneself to constant immediate gratification could have significant and lasting cognitive consequences. In one of the few truly experimental studies in the field, researchers sought to determine whether non-users of smartphones would exhibit a change in their reward processing capacity after being provided with a smartphone for the first time ( Hadar et al., 2015 ). The aim of the study was to investigate the cognitive, behavioral, and neural consequences of smartphone usage, with a specific emphasis on delay discounting. Participants were divided into three groups: heavy smartphone users, smartphone non-users, and a third group which were smartphone non-users who were given a smartphone for the first time (the latter two groups were assigned randomly). The heavy smartphone users showed higher scores for impulsivity and hyperactivity on a questionnaire that was administered at the beginning of the experiment. Even more interestingly, after a 3-month exposure to smartphones, the non-users who were given a smartphone were found to have become more immediacy oriented in the delay discounting measure, whereas non-users’ orientation did not change. The data from Hadar et al. (2015) suggest that heavy smartphone usage can causally reduce an individual’s capacity (or at least tendency) to delay gratification in favor of a greater reward in the future. These findings are strengthened by the study’s experimental design.

Evidence from neuroimaging research suggests that that neural circuitry implicated in reward processing also plays a role in activities performed on mobile phones, particularly social media. For example, Sherman et al. (2016) found that receiving many “Likes” on one’s social media photographs is related to increased activation in the brain’s reward circuitry, including areas in the dorsal and ventral striatum and ventral tegmental area. The ventral striatum has also been implicated in the experience of sharing information about oneself with peers, a popular activity on social media ( Tamir and Mitchell, 2012 ), and level of response in this brain region has been shown to correlate with level of social media use ( Meshi et al., 2013 ).

Delay of Gratification and Reward: Summary

As with the research highlighted in the previous sections of this paper, the data is still too sparse to support firm conclusions regarding the impacts of smartphone use on reward processing and delay of gratification. Lurid claims that smart devices are “rewiring our brains” ( Greenfield, 2013 ) into being addicted to instant gratification suffer from a lack of any longitudinal evidence, and still very limited empirical support of any kind. Future research could use neuroimaging techniques to investigate whether any “rewiring” is actually occurring, and some relevant work is currently being conducted. At present, neuroimaging research has been limited to cross-sectional studies mapping the neural correlates of engaging in popular activities on mobile phones. These studies cannot shed light on the ways that mobile phones may be leading to functional or structural changes in the brain. By conducting brain scans before and after a long-term intense exposure to electronic immediate gratification, neuroscientists could analyze whether any connectivity changes occurred.

Mobile Technology Use and Everyday Cognitive Functioning

Given the pattern of findings in attention, memory, and the ability to regulate reward-related processing in the context of delay of gratification, it follows that we might expect to see links to more generalized measures of cognitive functioning. One way in which such links have been studied is by exploring the relationship between technology habits and general academic performance. Studies on this front generally support the conclusion that poor academic performance (generally assessed by GPA) can be predicted by higher levels of smartphone use ( Beland and Murphy, 2014 ; Lepp et al., 2014 ), instant messaging ( Levine et al., 2007 ; Fox et al., 2009 ), social networking ( Kirschner and Karpinski, 2010 ; Junco, 2012b ; Karpinski et al., 2012 ; Paul et al., 2012 ), media multitasking ( Junco, 2012a ; Rosen et al., 2013 ; Sana et al., 2013 ), and general electronic media usage ( Jacobsen and Forste, 2011 ; Junco and Cotten, 2012 ).

Researchers have also directly investigated the relationship between mobile technology/media multitasking habits and executive functions that are thought to be essential to academic performance ( Abramson et al., 2009 ; Alloway and Alloway, 2012 ; Alzahabi and Becker, 2013 ; Lepp et al., 2014 ; Barr et al., 2015 ). In one relevant study ( Baumgartner et al., 2014 ), participants were required to complete a self-report questionnaire and computerized tasks that assessed executive functions in three subcategories: working memory, inhibition, and shifting. Participants who reported being high multitaskers, based on MMI score, also self-reported lower levels of “executive function in everyday life” on the questionnaire. The correlation was significant for all three subcategories of the executive functioning questionnaire. Though the results from the self-report measures were not corroborated by any of the performance-based measures of executive functioning used in that study, very recently published work conducted by Cain et al. (2016) does provide evidence for such links. These authors found that higher media-multitasking among a large adolescent sample was associated with poorer performance on one laboratory measure of executive function, the n-back working memory task, and also with lower scores on a standardized test of academic achievement in the classroom. Taken together, this body of work suggests that the degree to which one can exert executive control over behavior and maintain goal-related representations (in working memory) may explain individual differences in vulnerability to the “real life” consequences of mobile device habits.

Interestingly, there is also some evidence suggesting that one’s susceptibility to cognitive disruption from mobile technology use, and the consequent impacts on academic success, might depend on the individual’s existing cognitive skill set; especially their ability to exert self-regulatory control over behavior. Research indicates, for instance, that how closely an individual monitors and plans for interruptions, via executive control, mediates the relationship between multimedia interruptions and resultant stress ( Tams et al., 2015 ), and that differences in working memory capacity (which is closely linked to executive functioning) is a predictor of the speed of task resumption following an interruption ( Werner et al., 2011 ).

As a further point, it should be acknowledged that some of the cognitive and affective consequences of smartphone/technology habits may come from indirect impacts, such as through influences on sleep and mood. Quality of sleep has been shown to have a serious effect on cognitive performance ( Lim and Dinges, 2008 ), and considerable evidence implicates smartphone technology as a source of sleep disturbances ( Cain and Gradisar, 2010 for a review), with a compound effect on cognitive functioning and work engagement the following day ( Lanaj et al., 2014 ). An observation that predates the emergence of smartphone technology is that using electronic devices with a brightly lit screen immediately before bed, such as a television or a computer, can negatively impact one’s ability to fall asleep. Smartphones potentially exacerbate this problem because people frequently keep and charge smartphones at their bedside, often using them as an alarm clock. Based on a recent survey, over 70% of Americans follow this behavioral pattern ( Trends in Consumer Mobility Report, 2015 ). Moreover, in addition to the bright light, it has been proposed that specific activities, such as social interactions and games, occurring via one’s smartphone can lead to psychological arousal and stimulation that could further disrupt subsequent sleep ( Cain and Gradisar, 2010 ). Though most studies in this domain have had child and adolescent participants, recent research has affirmed that this effect can be seen in older adults as well ( Exelmans and Van den Bulck, 2016 ). Future research should investigate a direct relationship between habitual smartphone usage before bedtime and cognitive abilities. Furthermore, future research might attempt to identify if particular smartphone activities (e.g., gaming, passive or active social media use) are especially deleterious to sleep quality, and how notification settings may impact sleep disruption, and also consider how sleep-tracking apps (e.g., the recently introduced “Bedtime” feature on the iPhone operating system) might improve quantity or consistency of sleep.

Extending this work on sleep, Lemola et al. (2014) used self-report questionnaires to explore how sleep and smartphone habits might also impact mood; specifically depressive symptoms. They found that difficulty sleeping was a significant mediator in the relationship between electronic media use and depressive symptoms. While psychopathological symptoms are not the focus of this paper, it is noteworthy that depression is often comorbid with cognitive disorders ( American Psychiatric Association, 2013 ), and that sleep quality is inversely related with cognitive performance ( Lim and Dinges, 2008 ).

Like depression, anxiety is known to have significant negative effects on several aspects of cognitive functioning ( American Psychiatric Association, 2013 ). In early work linking anxiety symptoms, technology habits, and cognitive functioning, Mark et al. (2012) found that limiting individuals’ access to email reduced anxiety and improved later focus on work related tasks. A commonly repeated assertion regarding today’s digital world is that people feel a ‘need’ for access to their phones. Researchers have gone so far as to refer to this phenomenon as a “phantom limb” ( Turkle, 2011 ). Similarly, “phantom vibration syndrome” describes a commonly experienced phenomenon in which people perceive a vibration in their pocket, when no such vibration occurred ( Rosen, 2013 ), and even when their phone is not in their pocket (for a review, see Deb, 2014 ). In acknowledging the strength of individuals’ attachment to their smartphones, researchers have begun to investigate the degree to which separation from one’s smart device can cause symptoms of anxiety. In one study ( Cheever et al., 2014 ), participants were randomly assigned to one of two groups. One group kept their phones with them for the entirety of the study with the ringer silenced and vibration turned off, whereas the second group had their phones removed from them for the duration of the study. The participants then completed a series of three anxiety assessments at 20-min intervals, followed by a wireless media device usage questionnaire. The researchers posited that the group that had their phones removed from them would experience significantly more anxiety than those who were allowed to keep their phones with them. Although this main effect was not observed, the researchers found that the group of participants who did not have their phones with them scored higher on each successive anxiety test, showing that their anxiety increased as a function of time without their phones. Additionally, the study found that individuals who scored higher on the wireless media device usage questionnaire had higher rates of increased anxiety for the later tests, regardless of whether their phone was taken from them or with them but silenced. These findings led the researchers to conclude that one’s regular mobile device usage predicts the levels of anxiety that results from being separated from their device. However, it is also important to bear in mind that the directionality of the effect remains ambiguous, with research also suggesting that life-stress is predictive of mobile device usage, driven by the social support one can attain through using one’s device ( Chiu, 2014 ).

Clayton et al. (2015) also investigated the impact of brief separation from a mobile device, but additionally assessed the potential impacts on cognition. The researchers focused exclusively on iPhone users, based on the ease with which one can toggle the iPhone’s ringer. Upon arrival, participants were randomly assigned into one of two groups: one group completed a task that is sometimes used to measure sustained attention (a word-search task), first with their phones in their possession and then with their phones given to the experimenters, whereas these conditions were reversed in the second group. Physiological and self-report measures were used to track anxiety levels throughout the experiment. In this experiment, the researchers did more than simply separate the phone from its user; the phone was placed in an adjacent cubicle, and the experimenters placed a call to the phone so that it emitted a ring that the participants’ presumably recognized as their own. The results showed that participants’ anxiety levels were highest when they were separated from their ringing phones and lowest when their phones were in their possession. Moreover, the participants’ performance on the word-search puzzles was significantly poorer when they were separated from their ringing phone.

The data provided by such experiments offer evidence of the psychological sway our digital lives can hold over us. Yet, there is nothing to indicate whether the resultant anxiety is specific to separation from one’s smartphone, or whether the same effect might emerge when participants are separated from something else of subjective value, such as a wallet or personally cherished item. Moreover, the potential implications with respect to cognitive functioning are still limited in that a link between anxiety and cognition was established only via a word-search puzzle, a task that is somewhat idiosyncratic relative to tasks used more typically in cognitive research. The design also does not allow for a determination of whether the effect on word search performance was caused by the absence of the participants’ phones or simply by the distraction of the ring.

Smartphones (and related mobile technologies) have the potential to affect a wide range of cognitive domains, but empirical research on the cognitive impacts of smartphone technology is still quite limited. This is understandable, given that the relevant technology itself is still young and constantly evolving. However, with each passing year, smartphones become more omnipresent in our lives. Rather than applying to only a niche group of individuals, the research conducted in this domain will soon be relevant to the majority of the world’s population ( eMarketer, 2014 ). Therefore, it is crucial to understand how smartphone technology affects us so that we can take the steps necessary to mitigate the potential negative consequences.

Although the research concerning the potential cognitive impacts of smartphone technology is growing, the results remain contradictory and inconclusive. The at times contradictory findings suggest that not all smartphone use is created equal; certain apps, approaches to multitasking, or notification settings may moderate the relation between overall smartphone use and various cognitive skills. Despite the inconclusive nature of the literature, media headlines encourage a public perception that the findings are conclusive and that smartphones have a definite and negative impact on cognitive functioning. A common view, that smartphones are stifling our creativity by depriving our brains of downtime ( Richtel, 2010a ), even led to a radio challenge, in which thousands of people reduced their smartphone usage in an attempt to increase their creativity ( Zomorodi, 2015 ). However, there is no extant research to validate the basic concern that motivated the challenge. Investigating the cognitive impacts of filling the small breaks in our day with inputs from smartphone engagement is perhaps another endeavor worth pursuing, but not one that is yet represented in the peer reviewed literature.

As discussed earlier in our review, there are many limitations to the literature that forms the basis for this paper. Chief among these is that there is very little longitudinal evidence on the long-term consequences of frequent smartphone usage. Now is the time to begin gathering the data for such studies. A particularly important topic that requires longitudinal data is the effect of smartphone ownership on young children. Despite widely publicized recommendations ( AAP Council on Communications and Media, 2016a , b ), we know very little about the most appropriate age for a child to begin using a smartphone, and we know equally little about the consequences of using one too early in life. A longitudinal study with a large sample size should be developed in which children are assessed on a variety of cognitive (and affective) outcome measures at multiple time points. In a study such as this, data could also be gathered to ascertain the degree to which children with smartphones or other portable sources of immediate gratification, such as portable video game systems, are influenced by these devices. Analysis of group differences in rates of maturity of certain cognitive processes could also provide information about how smartphone technology can affect the brain during periods of heightened developmental plasticity. It is possible, but untested, that frequent smartphone usage could be less harmful to adults, whereas children may experience more negative consequences as a result of their increased neural plasticity.

If emerging research does suggest that there are serious consequences of smartphone usage, we need to investigate potential practical approaches that could mitigate these effects. Finally, the majority of the literature only speaks broadly about “smartphone usage.” Future research should distinguish between specific types of smartphone usage, each of which are likely to have differential effects on the user. In particular, it seems likely that social activities such as text messaging, email, and social media use will have different impacts than gaming or browsing the web, yet very little is known about the specific concerns related to these seemingly disparate patterns of use.

As smartphones have worked their way into the pockets of over 70% of American adults, and nearly 50% of adults worldwide, there is also a great opportunity to use them as a tool for research ( Poushter, 2016 ). Scientists have already begun to suggest that smartphones could present a more convenient and more naturalistic method of gathering empirical data for cognitive and social psychology experiments ( Raento et al., 2009 ; Dufau et al., 2011 ; Miller, 2012 ). Moreover, as smartphones become increasingly interlaced with our cognitive functioning, it will be important to continue to gather detailed usage metrics to understand how these interactions are affecting us, and how are lives are accordingly shaped.

The research outlined in this paper lays a foundation on which a seemingly endless number of “next steps” can be imagined. There is an immense opportunity for additional research to be performed with the aim of giving psychologists and the world-at-large a better understanding the short-term and long-term effects of smartphone technology.

Author Contributions

HW and JC conceived and developed the project. HW conducted primary literature review with additional input from LS, both working under the supervision and guidance of JC. HW wrote the initial draft manuscript. JC provided primary editorial feedback, and both JC and LS contributed additional written sections to the manuscript. All authors were involved in final preparation of the manuscript.

Conflict of Interest Statement

The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Funding. LS contributions to this work were supported in part by an National Science Foundation SBE-IBSS postdoctoral research award.

• AAP Council on Communications and Media (2016a). Media and Young Minds. Pediatrics Vol. 138. Available at: http://pediatrics.aappublications.org/content/early/2016/10/19/peds.2016-2591 . [ PubMed ] [ Google Scholar ]
• AAP Council on Communications and Media (2016b). Media Use in School-Aged Children and Adolescents Vol. 138. Available at: http://pediatrics.aappublications.org/cgi . [ PubMed ] [ Google Scholar ]
• Abramson M. J., Benke G. P., Dimitriadis C., Inyang I. O., Sim M. R., Wolfe R. S., et al. (2009). Mobile telephone use is associated with changes in cognitive function in young adolescents. Bioelectromagnetics 30 678–686. 10.1002/bem.20534 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Alloway T. P., Alloway R. G. (2012). The impact of engagement with social networking sites (SNSs) on cognitive skills. Comput. Hum. Behav. 28 1748–1754. 10.1016/j.chb.2012.04.015 [ CrossRef ] [ Google Scholar ]
• Alsop R. (2014). Instant Gratification & Its Dark Side. Available at: http://www.bucknell.edu/communications/bucknell-magazine/instant-gratification-and-its-dark-side.html [accessed November 25, 2016]. [ Google Scholar ]
• Alzahabi R., Becker M. W. (2013). The association between media multitasking, task-switching, and dual-task performance. J. Exp. Psychol. Hum. Percept. Perform. 39 1485–1495. 10.1037/a0031208 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• American Psychiatric Association (2013). Diagnostic and Statistical Manual of Mental Disorders 5th Edn. Washington, DC: American Psychiatric Association. 10.1176/appi.books.9780890425596 [ CrossRef ] [ Google Scholar ]
• Andrews S., Ellis D. A., Shaw H., Piwek L. (2015). Beyond self-report: tools to compare estimated and real-world smartphone use. PLoS ONE 10 : e0139004 10.1371/journal.pone.0139004 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Barr N., Pennycook G., Stolz J. A., Fugelsang J. A. (2015). The brain in your pocket: evidence that smartphones are used to supplant thinking. Comput. Hum. Behav. 48 473–480. 10.1016/j.chb.2015.02.029 [ CrossRef ] [ Google Scholar ]
• Baumgartner S. E., Lemmens J. S., Weeda W. D., Huizinga M. (2016). Measuring media multitasking: development of a short measure of media multitasking for adolescents. J. Media Psychol. 10.1027/1864-1105/a000167 [ CrossRef ] [ Google Scholar ]
• Baumgartner S. E., Weeda W. D., van der Heijden L. L., Huizinga M. (2014). The relationship between media multitasking and executive function in early adolescents. J. Early Adolesc. 34 1120–1144. 10.1177/0272431614523133 [ CrossRef ] [ Google Scholar ]
• Beland L., Murphy R. J. (2014). Ill Communication: Mobile Phones & Student Performance. London: London School of Economics and Political Science. [ Google Scholar ]
• Bianchi A., Phillips J. G. (2005). Psychological predictors of problem mobile phone use. Cyberpsychol. Behav. 8 39–51. 10.1089/cpb.2005.8.39 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Billieux J., Van Der Linden M., Rochat L. (2008). The role of impulsivity in actual and problematic use of the mobile phone. Appl. Cogn. Psychol. 22 1195–1210. 10.1002/acp.1429 [ CrossRef ] [ Google Scholar ]
• Boari D., Fraser M., Stanton Fraser D., Cater K. (2012). “Augmenting spatial skills with mobile devices,” in Proceedings of the ACM SIGCHI Conference on Human Factors in Computing Systems (CHI2012): Mobile Computing and Interaction (New York, NY: Association for Computing Machinery; ) 1611–1620. 10.1145/2207676.2208284 [ CrossRef ] [ Google Scholar ]
• Brown G. (2014). The Effects of Values and the Presence of a Mobile Phone on Friendship Interactions. Masters thesis, Western Washington University; Bellingham, WA. [ Google Scholar ]
• Brumby D., Cox A., Back J. (2013). Recovering from an interruption: investigating speed-accuracy tradeoffs in task resumption strategy. J. Exp. Psychol. Appl. 19 95–107. 10.1037/a0032696 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Burnett G., Lee K. (2005). The effect of vehicle navigation systems on the formation of cognitive maps. Int. J. Psychol. 40 27–35. 10.1016/b978-008044379-9/50188-6 [ CrossRef ] [ Google Scholar ]
• Cacioppo J., Petty R., Kao C. (1984). The efficient assessment of need for cognition. J. Pers. Assess. 48 306–307. 10.1207/s15327752jpa4803 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cades D. M., Davis D. A. B., Trafton J. G., Monk C. A. (2007). “Does the difficulty of an interruption affect our ability to resume?,” in Proceedings of the Human Factors and Ergonomics Society 51st Annual Meeting Santa Monica, CA: 234–238. 10.1177/154193120705100419 [ CrossRef ] [ Google Scholar ]
• Cain M. S., Leonard J. A., Gabrieli J. D. E., Finn A. S. (2016). Media multitasking in adolescence. Psychon. Bull. Rev. 23 1932–1941. 10.3758/s13423-016-1036-3 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cain M. S., Mitroff S. R. (2011). Distractor filtering in media multitaskers. Perception 40 1183–1192. 10.1068/p6939 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cain N., Gradisar M. (2010). Electronic media use and sleep in school-aged children and adolescents: a review. Sleep Med. 11 735–742. 10.1016/j.sleep.2010.02.006 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Caird J. K., Johnston K. A., Willness C. R., Asbridge M., Steel P. (2014). A meta-analysis of the effects of texting on driving. Accid. Anal. Prev. 71 311–318. 10.1016/j.aap.2014.06.005 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Cardoso-Leite P., Green C. S., Bavelier D. (2014). On the impact of new technologies on multitasking. Dev. Rev. 35 98–112. 10.1016/j.dr.2014.12.001 [ CrossRef ] [ Google Scholar ]
• Cardoso-Leite P., Kludt R., Ma W. J. C., Shawn G., Daphne B. (2015). Technology consumption and cognitive control: contrasting action video game experience with media multitasking. Atten. Percept. Psychophys. 78 218–241. 10.3758/s13414-015-0988-0 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Centers for Disease Control and Prevention (2011). Mobile Device Use While Driving- United States and Seven European Countries Vol. 62 Atlanta, GA: Centers for Disease Control and Prevention [ Google Scholar ]
• Centers for Disease Control and Prevention (2016). Youth Risk Behavior Surveillance–United States, 2015. Morbidity and Mortality Weekly Report Vol. 65 Atlanta, GA: Centers for Disease Control and Prevention [ PubMed ] [ Google Scholar ]
• Cheever N. A., Rosen L. D., Carrier L. M., Chavez A. (2014). Out of sight is not out of mind: the impact of restricting wireless mobile device use on anxiety levels among low, moderate and high users. Comput. Hum. Behav. 37 290–297. 10.1016/j.chb.2014.05.002 [ CrossRef ] [ Google Scholar ]
• Chiu S. I. (2014). The relationship between life stress and smartphone addiction on taiwanese university student: a mediation model of learning self-Efficacy and social self-Efficacy. Comput. Hum. Behav. 34 49–57. 10.1016/j.chb.2014.01.024 [ CrossRef ] [ Google Scholar ]
• Clayton R. B., Leshner G., Almond A. (2015). The extended iSelf: the impact of iPhone separation on cognition, emotion, and physiology. J. Comput. Med. Commun. 20 119–135. 10.1111/jcc4.12109 [ CrossRef ] [ Google Scholar ]
• Csikszentmihalyi M., Abuhamdeh S., Nakamura J. (2014). Flow and the Foundations of Positive Psychology. Dordrecht: Springer; 227–238. 10.1007/978-94-017-9088-8 [ CrossRef ] [ Google Scholar ]
• Deb A. (2014). Phantom vibration and phantom ringing among mobile phone users: a systematic review of literature. Asia Pac. Psychiatry 7 231–239. 10.1111/appy.12164 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Dufau S., Duñabeitia J. A., Moret-Tatay C., McGonigal A., Peeters D., Alario F. X., et al. (2011). Smart phone, smart science: how the use of smartphones can revolutionize research in cognitive science. PLoS ONE 6 : e24974 10.1371/journal.pone.0024974 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Dux P. E., Tombu M. N., Harrison S., Rogers B. P., Tong F., Marois R. (2009). Training improves multitasking performance by increasing the speed of information processing in human prefrontal cortex. Neuron 63 127–138. 10.1016/j.neuron.2009.06.005 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Egan T. (2016). The Eight-Second Attention Span. New York City, NY: The New York Times. [ Google Scholar ]
• Ellison K. (2012). Are Smartphones Making Us Dumber?. Available at: http://www.forbes.com/sites/netapp/2012/09/12/is-an-digital-data-overload-shortening-our-attentions-spans-and-making-us-dumber/ [accessed April 16, 2015]. [ Google Scholar ]
• eMarketer (2014). 2 Billion Consumers Worldwide to Get Smart(phones) by 2016. Available at: http://www.emarketer.com/Article/2-Billion-Consumers-Worldwide-Smartphones-by-2016/1011694 [accessed April 16, 2015]. [ Google Scholar ]
• Exelmans L., Van den Bulck J. (2016). Bedtime mobile phone use and sleep in adults. Soc. Sci. Med. 148 93–101. 10.1016/j.socscimed.2015.11.037 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Fleischhauer M., Enge S., Brocke B., Ullrich J., Strobel A., Strobel A. (2010). Same or different? Clarifying the relationship of need for cognition to personality and intelligence. Pers. Soc. Psychol. Bull. 36 82–96. 10.1177/0146167209351886 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Fox A. B., Rosen J., Crawford M. (2009). Distractions, distractions: does instant messaging affect college students’ performance on a concurrent reading comprehension task? Cyberpsychol. Behav. 12 51–53. 10.1089/cpb.2008.0107 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Frein S. T., Jones S. L., Gerow J. E. (2013). When it comes to Facebook there may be more to bad memory than just multitasking. Comput. Hum. Behav. 29 2179–2182. 10.1016/j.chb.2013.04.031 [ CrossRef ] [ Google Scholar ]
• Frith J., Kalin J. (2015). Here, I used to be: mobile media and practices of place-based digital memory. Space Cult. 19 43–55. 10.1177/1206331215595730 [ CrossRef ] [ Google Scholar ]
• George M. J., Odgers C. L. (2015). Seven fears and the science of how mobile technologies may be influencing adolescents in the digital age. Perspect. Psychol. Sci. 10 832–851. 10.1177/1745691615596788 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Green C. S., Bavelier D. (2012). Learning, attentional control, and action video games. Curr. Biol. 22 R197–R206. 10.1016/j.cub.2012.02.012 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Greenfield S. (2013). Screen Technologies. Available at: http://www.susangreenfield.com/science/screen-technologies/ [accessed April 16, 2015]. [ Google Scholar ]
• Hadar A. A., Eliraz D., Lazarovits A., Alyagon U., Zangen A. (2015). Using longitudinal exposure to causally link smartphone usage to changes in behavior, cognition and right prefrontal neural activity. Brain Stimul. 8 318 10.1016/j.brs.2015.01.032 [ CrossRef ] [ Google Scholar ]
• Hadlington L. J. (2015). Cognitive failures in daily life: exploring the link with Internet addiction and problematic mobile phone use. Comput. Hum. Behav. 51 75–81. 10.1016/j.chb.2015.04.036 [ CrossRef ] [ Google Scholar ]
• Hansen L. (2013). 8 Drivers who Blindly Followed their GPS Into Disaster. Available at: http://theweek.com/articles/464674/8-drivers-who-blindly-followed-gps-into-disaster [accessed April 16, 2015]. [ Google Scholar ]
• Hartmann T., Krakowiak K. M., Tsay-Vogel M. (2014). How violent video games communicate violence: a literature review and content analysis of moral disengagement factors. Commun. Monogr. 7751 1–23. 10.1080/03637751.2014.922206 [ CrossRef ] [ Google Scholar ]
• Henkel L. A. (2013). Point-and-shoot memories: the influence of taking photos on memory for a museum tour. Psychol. Sci. 25 396–402. 10.1177/0956797613504438 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Humphreys L., Liao T. (2011). Mobile geotagging: reexamining our interactions with urban space. J. Comput. Mediat. Commun. 16 407–423. 10.1111/j.1083-6101.2011.01548.x [ CrossRef ] [ Google Scholar ]
• Jacobsen W. C., Forste R. (2011). The wired generation: academic and social outcomes of electronic media use among university students. Cyberpsychol. Behav. Soc. Netw. 14 275–280. 10.1089/cyber.2010.0135 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Junco R. (2012a). In-class multitasking and academic performance. Comput. Hum. Behav. 28 2236–2243. 10.1016/j.chb.2012.06.031 [ CrossRef ] [ Google Scholar ]
• Junco R. (2012b). Too much face and not enough books: the relationship between multiple indices of Facebook use and academic performance. Comput. Hum. Behav. 28 187–198. 10.1016/j.chb.2011.08.026 [ CrossRef ] [ Google Scholar ]
• Junco R., Cotten S. R. (2012). No A 4 U: the relationship between multitasking and academic performance. Comput. Educ. 59 505–514. 10.1016/j.compedu.2011.12.023 [ CrossRef ] [ Google Scholar ]
• Kahneman D. (2011). Thinking, Fast and Slow. Macmillan. Available at: https://books-google-com.libproxy.temple.edu/books?hl=en&lr=&id=SHvzzuCnuv8C&oi=fnd&pg=PP2&ots=NRqmNH-jJC&sig=aZUMjzdgcaWja5iIsY0kXJoRy7I#v=onepage&q&f=false . [ Google Scholar ]
• Karbach J., Kray J. (2009). How useful is executive control training? Age differences in near and far transfer of task-switching training. Dev. Sci. 12 978–990. 10.1111/j.1467-7687.2009.00846.x [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Karpinski A. C., Kirschner P. A., Ozer I., Mellott J. A., Ochwo P. (2012). An exploration of social networking site use, multitasking, and academic performance among United States and European university students. Comput. Hum. Behav. 29 1182–1192. 10.1016/j.chb.2012.10.011 [ CrossRef ] [ Google Scholar ]
• Kaspersky Lab (2015). The Rise, and Impact of Digital Amnesia: Why we Need to Protect what we no Longer Remember. Moscow: Kaspersky Lab. [ Google Scholar ]
• Katz E., Blumler J. G., Gurevitch M. (1973). Uses and gratifications research. Public Opin. Q. 37 509–523. 10.1086/268109 [ CrossRef ] [ Google Scholar ]
• Kirschner P. A., Karpinski A. C. (2010). Facebook ® and academic performance. Comput. Hum. Behav. 26 1237–1245. 10.1016/j.chb.2010.03.024 [ CrossRef ] [ Google Scholar ]
• Kuhn A. (2010). Memory texts and memory work: performances of memory in and with visual media. Mem. Stud. 3 298–313. 10.1177/1750698010370034 [ CrossRef ] [ Google Scholar ]
• Kwon M., Lee J. Y., Won W. Y., Park J. W., Min J. A., Hahn C., et al. (2013). Development and validation of a Smartphone Addiction Scale (SAS). PLoS ONE 8 : e56936 10.1371/journal.pone.0056936 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lanaj K., Johnson R. E., Barnes C. M. (2014). Beginning the workday yet already depleted? Consequences of late-night smartphone use and sleep. Organ. Behav. Hum. Decis. Process. 124 11–23. 10.1016/j.obhdp.2014.01.001 [ CrossRef ] [ Google Scholar ]
• Lee J., Cho B., Kim Y., Noh J. (2015). Emerging Issues in Smart Learning. Berlin: Springer; 297–305. 10.1007/978-3-662-44188-6 [ CrossRef ] [ Google Scholar ]
• Lee Y. K., Chang C. T., Lin Y., Cheng Z. H. (2014). The dark side of smartphone usage: psychological traits, compulsive behavior and technostress. Comput. Hum. Behav. 31 373–383. 10.1016/j.chb.2013.10.047 [ CrossRef ] [ Google Scholar ]
• Leiva L., Böhmer M., Gehring S., Krüger A. (2012). “Back to the app: the costs of mobile appication interruptions,” in Proceedings of the 14th International Conference on Human-Computer Interaction with Mobile Devices and Services–Mobile HCI Vol. 12 San Francisco, CA: 291–294. 10.1145/2371574.2371617 [ CrossRef ] [ Google Scholar ]
• Lemola S., Perkinson-Gloor N., Brand S., Dewald-Kaufmann J. F., Grob A. (2014). Adolescents’ electronic media use at night, sleep disturbance, and depressive symptoms in the smartphone age. J. Youth Adolesc. 44 405–418. 10.1007/s10964-014-0176-x [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lenhart A. (2015). Teens, Social Media and Technology Overview 2015: Smartphones Facilitate Shifts in Communication Landscape for Teens. Washington, DC: Pew Research Center; 1–47. 10.1016/j.chb.2015.08.026 [ CrossRef ] [ Google Scholar ]
• Lepp A., Barkley J. E., Karpinski A. C. (2014). The relationship between cell phone use, academic performance, anxiety, and satisfaction with life in college students. Comput. Hum. Behav. 31 343–350. 10.1016/j.chb.2013.10.049 [ CrossRef ] [ Google Scholar ]
• Levine L. E., Waite B. M., Bowman L. L. (2007). Electronic media use, reading, and academic distractibility in college youth. Cyberpsychol. Behav. 10 560–566. 10.1089/cpb.2007.9990 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Levy E. C., Rafaeli S., Ariel Y. (2016). The effect of online interruptions on the quality of cognitive performance. Telemat. Inform. 33 1014–1021. 10.1016/j.tele.2016.03.003 [ CrossRef ] [ Google Scholar ]
• Lim J., Dinges D. F. (2008). Sleep deprivation and vigilant attention. Ann. N. Y. Acad. Sci. 1129 305–322. 10.1196/annals.1417.002 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Loh K. K., Kanai R. (2015). How has the internet reshaped human cognition? Neuroscientist 22 506–520. 10.1177/1073858415595005 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Lui K. F. H., Wong A. C.-N. (2012). Does media multitasking always hurt? A positive correlation between multitasking and multisensory integration. Psychon. Bull. Rev. 19 647–653. 10.3758/s13423-012-0245-7 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Mark G., Voida S., Cardello A. (2012). “A pace not dictated by electrons”: an empirical study of work without email,” in Proceedings of the 2012 ACM Annual Conference on Human Factors in Computing Systems - CHI Vol. 12 Austin, TX: 555 10.1145/2207676.2207754 [ CrossRef ] [ Google Scholar ]
• Melcher M. (2013). Mindfulness vs. Smartphones. Available at: http://www.huffingtonpost.com/michael-melcher/mindfulness-vs-smartphone_b_2856462.html [accessed April 16, 2015]. [ Google Scholar ]
• Meshi D., Morawetz C., Heekeren H. R. (2013). Nucleus accumbens response to gains in reputation for the self relative to gains for others predicts social media use. Front. Hum. Neurosci. 7 : 439 10.3389/fnhum.2013.00439 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Miller G. (2012). The smartphone psychology manifesto. Perspect. Psychol. Sci. 7 221–237. 10.1177/1745691612441215 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Mills K. L. (2016). Possible effects of internet use on cognitive development in adolescence. Med. Commun. 4 4 10.17645/mac.v4i3.516 [ CrossRef ] [ Google Scholar ]
• Minear M., Brasher F., McCurdy M., Lewis J., Younggren A. (2013). Working memory, fluid intelligence, and impulsiveness in heavy media multitaskers. Psychon. Bull. Rev. 20 1274–1281. 10.3758/s13423-013-0456-6 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Misra S., Cheng L., Genevie J., Yuan M. (2014). The iPhone effect: the quality of in-person social interactions in the presence of mobile devices. Environ. Behav. 48 275–298. 10.1177/0013916514539755 [ CrossRef ] [ Google Scholar ]
• Moisala M., Salmela V., Hietajärvi L., Salo E., Carlson S., Salonen O., et al. (2016). Media multitasking is associated with distractibility and increased prefrontal activity in adolescents and young adults. Neuroimage 134 113–121. 10.1016/j.neuroimage.2016.04.011 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Monk C. A. (2004). “The effect of frequent versus infrequent interruptions on primary task resumption,” in Proceedings of the Human Factors and Ergonomics Society 48th Annual Meeting Santa Monica, CA: 295–299. 10.1177/154193120404800304 [ CrossRef ] [ Google Scholar ]
• Monk C. A., Trafton J. G., Boehm-Davis D. A. (2008). The effect of interruption duration and demand on resuming suspended goals. J. Exp. Psychol. Appl. 14 299–313. 10.1037/a0014402 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Monsell S. (2003). Task switching. Trends Cogn. Sci. 7 134–140. 10.1016/S1364-6613(03)00028-7 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Morin M. (2013). Is Your Smartphone Making you Fat and Lazy?. Los Angeles, CA: Los Angeles Times. [ Google Scholar ]
• Moskvitch K. (2014). BBC–Future–Is car Technology Creating Stupid Drivers?. Available at: http://www.bbc.com/future/story/20141119-is-tech-creating-stupid-drivers [accessed April 16, 2015]. [ Google Scholar ]
• Nikken P., Schols M. (2015). How and why parents guide the media use of young children. J. Child Fam. Stud. 24 3423–3435. 10.1007/s10826-015-0144-4 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ophir E., Nass C., Wagner A. D. (2009). Cognitive control in media multitaskers. Proc. Natl. Acad. Sci. U.S.A. 106 15583–15587. 10.1073/pnas.0903620106 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Özkul D., Humphreys L. (2015). Record and remember: memory and meaning-making practices through mobile media. Mob. Med. Commun. 3 351–365. 10.1177/2050157914565846 [ CrossRef ] [ Google Scholar ]
• Parush A., Ahuvia S., Erev I. (2007). “Degradation in spatial knowledge acquisition when using automatic navigation systems,” in Spatial Information Theory eds Winter S., Duckham M., Kulik L., Kuipers B. (Berlin: Springer; ) 238–254. [ Google Scholar ]
• Paul J. A., Baker H. M., Cochran J. D. (2012). Effect of online social networking on student academic performance. Comput. Hum. Behav. 28 2117–2127. 10.1016/j.chb.2012.06.016 [ CrossRef ] [ Google Scholar ]
• Pentzold C., Sommer V. (2011). Digital Networked Media and Social Memory. Theoretical Foundations and Implications. Aurora: Revista de Arte, Mídia e Política; 72–85. [ Google Scholar ]
• Poushter J. (2016). Smartphone Ownership and Internet Usage Continues to Climb in Emerging Economies. Available at: http://www.pewglobal.org/2016/02/22/smartphone-ownership-and-internet-usage-continues-to-climb-in-emerging-economies/ . [ Google Scholar ]
• Raento M., Oulasvirta A., Eagle N. (2009). Smartphones: an emerging tool for social scientists. Sociol. Methods Res. 37 426–454. 10.1177/0049124108330005 [ CrossRef ] [ Google Scholar ]
• Ralph B. C. W., Thomson D. R., Cheyne J. A., Smilek D. (2013). Media multitasking and failures of attention in everyday life. Psychol. Res. 78 661–669. 10.1007/s00426-013-0523-7 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Ralph B. C. W., Thomson D. R., Seli P., Carriere J. S. A., Smilek D. (2015). Media multitasking and behavioral measures of sustained attention. Atten. Percept. Psychophys. 77 390–401. 10.3758/s13414-014-0771-7 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Richtel M. (2010a). Digital Devices Deprive Brain of Needed Downtime. Available at: http://www.nytimes.com/2010/08/25/technology/25brain.html [accessed April 16, 2015]. [ Google Scholar ]
• Richtel M. (2010b). Growing Up Digital, Wired for Distraction. Available at: http://www.nytimes.com/2010/11/21/technology/21brain.html?pagewanted=all&_r=0 [accessed April 16, 2015]. [ Google Scholar ]
• Rideout V. J., Foehr U. G., Roberts D. F., Kimberly M., Deepa G., Board E., et al. (2010). Generation M2: Media in the Lives of 8- to 18-Year-Olds. Menlo Park, CA: Henry J. Kaiser Family Foundation. [ Google Scholar ]
• Roberts D. F., Foehr U. G., Rideout V. J. (2005). Generation M: Media in the Lives of 8-18 Year-Olds. Menlo Park, CA: Henry J. Kaiser Family Foundation. [ Google Scholar ]
• Rosen L. D. (2013). Phantom Pocket Vibration Syndrome. Available at: https://www.psychologytoday.com/blog/rewired-the-psychology-technology/201305/phantom-pocket-vibration-syndrome [accessed April 16, 2015]. [ Google Scholar ]
• Rosen L. D., Mark Carrier L., Cheever N. A. (2013). Facebook and texting made me do it: media-induced task-switching while studying. Comput. Hum. Behav. 29 948–958. 10.1016/j.chb.2012.12.001 [ CrossRef ] [ Google Scholar ]
• Sana F., Weston T., Cepeda N. J. (2013). Laptop multitasking hinders classroom learning for both users and nearby peers. Comput. Educ. 62 24–31. 10.1016/j.compedu.2012.10.003 [ CrossRef ] [ Google Scholar ]
• Sanbonmatsu D. M., Strayer D. L., Medeiros-Ward N., Watson J. M. (2013). Who multi-tasks and why? multi-tasking ability, perceived multi-tasking ability, impulsivity, and sensation seeking. PLoS ONE 8 : e54402 10.1371/journal.pone.0054402 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Shenhav A., Cohen J. D., Botvinick M. M. (2016). Dorsal anterior cingulate cortex and the value of control. Nat. Neurosci. 19 1286–1291. 10.1038/nn.4382 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Sherman L. E., Payton A. A., Hernandez L. M., Greenfield P. M., Dapretto M. (2016). The power of the like in adolescence: effects of peer influence on neural and behavioral responses to social media. Psychol. Sci. 27 1027–1035. 10.1177/0956797616645673 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Shih S. I. (2013). A null relationship between media multitasking and well-being. PLoS ONE 8 : e64508 10.1371/journal.pone.0064508 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Small G. W., Moody T. D., Siddarth P., Bookheimer S. Y. (2009). Your brain on google: patterns of cerebral activation during internet searching. Am. J. Geriatr. Psychiatry 17 116–126. 10.1097/JGP.0b013e3181953a02 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Sparrow B., Liu J., Wegner D. M. (2011). Google effects on memory: cognitive consequences of having information at our fingertips. Science 333 776–778. 10.1126/science.1207745 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Stothart C., Mitchum A., Yehnert C. (2015). The attentional cost of receiving a cell phone notification. J. Exp. Psychol. 41 893–897. 10.1037/xhp0000100 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Tamir D. I., Mitchell J. P. (2012). Disclosing information about the self is intrinsically rewarding. Proc. Natl. Acad. Sci. U.S.A. 109 8038–8043. 10.1073/pnas.1202129109 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Tams S., Thatcher J., Grover V., Pak R. (2015). Selective attention as a protagonist in contemporary workplace stress: implications for the interruption age. Anxiety Stress Coping 28 663–686. 10.1080/10615806.2015.1011141 [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Thornton B., Faires A., Robbins M., Rollins E. (2014). The mere presence of a cell phone may be distracting implications for attention and task performance. Soc. Psychol. 45 479–488. 10.1027/1864-9335/a000216 [ CrossRef ] [ Google Scholar ]
• Trends in Consumer Mobility Report (2015). Available at. http://newsroom.bankofamerica.com/files/doc_library/additional/2015_BAC_Trends_in_Consumer_Mobility_Report.pdf [accessed April 27, 2016]. [ Google Scholar ]
• Turkle S. (2011). Alone Together: Why We Expect More from Technology and Less from Each Other. New York City, NY: Basic Books. [ Google Scholar ]
• Uhls Y. T., Michikyan M., Morris J., Garcia D., Small G. W., Zgourou E., et al. (2014). Five days at outdoor education camp without screens improves preteen skills with nonverbal emotion cues. Comput. Hum. Behav. 39 387–392. 10.1016/j.chb.2014.05.036 [ CrossRef ] [ Google Scholar ]
• Uncapher M. R. K., Thieu M., Wagner A. D. (2015). Media multitasking and memory: differences in working memory and long-term memory. Psychon. Bull. Rev. 23 483–490. 10.3758/s13423-015-0907-3 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Visser S. N., Danielson M. L., Bitsko R. H., Holbrook J. R., Kogan M. D., Ghandour R. M., et al. (2014). Trends in the parent-report of health care provider-diagnosed and medicated attention-deficit/hyperactivity disorder: United States, 2003-2011. J. Am. Acad. Child Adolesc. Psychiatry 53 34–46.e2. 10.1016/j.jaac.2013.09.001 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Wang Z., Tchernev J. M. (2012). The “Myth” of media multitasking: reciprocal dynamics of media multitasking, Personal Needs, and Gratifications. J. Commun. 62 493–513. 10.1111/j.1460-2466.2012.01641.x [ CrossRef ] [ Google Scholar ]
• Werner N. E., Cades D. M., Boehm-Davis D. A., Chang J., Khan H., Thi G. (2011). “What makes us resilient to interruptions? understanding the role of individual differences in resumption,” in Proceedings of the Human Factors and Ergonomics Society Annual Meeting Vol. 55 Thousand Oaks, CA: 296–300. 10.1177/1071181311551062 [ CrossRef ] [ Google Scholar ]
• Wilmer H. H., Chein J. M. (2016). Mobile technology habits: patterns of association among device usage, intertemporal preference, impulse control, and reward sensitivity. Psychon. Bull. Rev. 23 1607–1614. 10.3758/s13423-016-1011-z [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Xavier A. J., D’Orsi E., de Oliveira C. M., Orrell M., Demakakos P., Biddulph J. P., et al. (2014). English longitudinal study of aging: can Internet/E-mail use reduce cognitive decline? J. Gerontol. A Biol. Sci. Med. Sci. 69 1117–1121. 10.1093/gerona/glu105 [ PMC free article ] [ PubMed ] [ CrossRef ] [ Google Scholar ]
• Yap J. Y., Lim S. W. H. (2013). Media multitasking predicts unitary versus splitting visual focal attention. J. Cogn. Psychol. 25 889–902. 10.1080/20445911.2013.835315 [ CrossRef ] [ Google Scholar ]
• Zauberman G., Silverman J., Diehl K., Barasch A. (2015). Photographic memory: the effects of photo-taking on memory for auditory and visual information. NA Adv. Consum. Res. 43 218–223. [ Google Scholar ]
• Zhang W., Zhang L. (2012). Explicating multitasking with computers: gratifications and situations. Comput. Hum. Behav. 28 1883–1891. 10.1016/j.chb.2012.05.006 [ CrossRef ] [ Google Scholar ]
• Zomorodi M. (2015). New Tech City: Bored and Brilliant by the Numbers - WNYC. Available at: http://www.wnyc.org/story/bored-and-brilliant-challenge-tracker/ [accessed April 16, 2015]. [ Google Scholar ]
• Zubko O., Gould R. L., Gay H. C., Cox H. J., Coulson M. C., Howard R. J. (2016). Effects of electromagnetic fields emitted by GSM phones on working memory: a meta-analysis. Int. J. Geriatr. Psychiatry 32 125–135. 10.1002/gps.4581 [ PubMed ] [ CrossRef ] [ Google Scholar ]

Presentations made painless

• Get Premium

103 Smartphone Essay Topic Ideas & Examples

Inside This Article

In today's digital age, smartphones have become an essential part of our daily lives. From communication to entertainment, these devices have revolutionized the way we interact with the world around us. With so many different aspects of smartphones to explore, there are countless essay topics that can be written about this versatile technology. Here are 103 smartphone essay topic ideas and examples to get you started:

• The impact of smartphones on society
• The evolution of smartphone technology
• The role of smartphones in education
• The effects of smartphone addiction
• How smartphones have changed the way we communicate
• The benefits of using smartphones for productivity
• The dangers of distracted driving due to smartphone use
• Smartphone privacy concerns and data security
• The influence of smartphones on mental health
• The future of smartphone technology
• The importance of smartphone accessibility for people with disabilities
• The use of smartphones in healthcare
• The role of smartphones in disaster response and recovery
• The impact of smartphone use on relationships
• The rise of mobile gaming on smartphones
• The environmental impact of smartphone production and disposal
• The influence of smartphone advertising on consumer behavior
• Smartphone tracking and surveillance
• The globalization of smartphone technology
• The role of smartphones in social activism
• The effects of smartphone use on sleep patterns
• The impact of smartphones on children's development
• The use of smartphones in journalism and citizen reporting
• The influence of smartphones on fashion and design
• The benefits of using smartphones for travel and navigation
• The role of smartphones in political campaigns
• The effects of smartphone use on memory and cognitive function
• The use of smartphones in emergency response situations
• The influence of smartphones on language and communication skills
• The ethical implications of smartphone technology
• The impact of smartphones on the music industry
• The benefits of using smartphones for remote work
• The effects of smartphone use on physical health
• The role of smartphones in e-commerce and online shopping
• The influence of smartphones on cultural trends
• The use of smartphones in disaster preparedness and response
• The impact of smartphones on the environment
• The benefits of using smartphones for fitness and health tracking
• The effects of smartphone use on socialization and relationships
• The role of smartphones in urban planning and development
• The influence of smartphones on fashion and beauty trends
• The use of smartphones in public transportation and urban mobility
• The benefits of using smartphones for language learning
• The effects of smartphone use on attention span and focus
• The role of smartphones in virtual reality and augmented reality experiences
• The influence of smartphones on social media and online communities
• The use of smartphones in disaster response and recovery efforts
• The impact of smartphones on job creation and economic development
• The benefits of using smartphones for remote learning and education
• The effects of smartphone use on creativity and innovation
• The role of smartphones in political activism and advocacy
• The influence of smartphones on cultural diversity and inclusion
• The use of smartphones in wildlife conservation and environmental protection
• The impact of smartphones on journalism and media reporting
• The benefits of using smartphones for financial management and budgeting
• The effects of smartphone use on mental health and well-being
• The role of smartphones in public safety and emergency response
• The influence of smartphones on art and creative expression
• The impact of smartphones on travel and tourism industries
• The benefits of using smartphones for language translation
• The effects of smartphone use on social interactions and relationships
• The role of smartphones in healthcare and telemedicine
• The influence of smartphones on political participation and civic engagement
• The use of smartphones in wildlife conservation and environmental protection efforts
• The impact of smartphones on global trade and commerce
• The benefits of using smartphones for environmental monitoring and research
• The effects of smartphone use on physical health and well-being
• The role of smartphones in disaster response and recovery efforts

As you can see, there are a wide variety of essay topics that can be explored when it comes to smartphones. Whether you're interested in the social, economic, environmental, or technological aspects of this technology, there is sure to be a topic that piques your interest. So go ahead and start writing about the fascinating world of smartphones!

Want to create a presentation now?

Instantly Create A Deck

Let PitchGrade do this for me

Hassle Free

We will create your text and designs for you. Sit back and relax while we do the work.

Explore More Content

• Privacy Policy
• Terms of Service

© 2023 Pitchgrade

Are Smartphones Good or Bad? (Essay Guide)

With the rapid advancement in technology, smartphones have become a part of our everyday lives.

We use them to communicate, to work, to stay informed, and even to entertain ourselves.

But are smartphones really good for us, or are they bad? This essay guide will explore the advantages and disadvantages of smartphones, their impact on social interactions, mental health, education, productivity, and privacy.

We will delve into the different ways smartphones can be both good and bad, giving readers the tools to decide how to best use smartphones to their advantage.

Table of Contents

Short Answer

The answer to this question really depends on how smartphones are used.

Smartphones can be both good and bad, depending on the circumstances.

On the one hand, smartphones can offer many convenient features such as allowing people to stay connected with their friends and family, access to the internet, and the ability to perform a variety of tasks, such as online shopping or banking.

On the other hand, smartphones can be a source of distraction and can lead to an unhealthy dependence on technology.

Ultimately, it is important to be aware of the potential risks and benefits of using smartphones and to use them mindfully and responsibly.

Advantages of Smartphones

Smartphones offer a wide range of advantages that have made them an essential tool in our daily lives.

First and foremost, smartphones provide us with access to a wealth of information.

With a few clicks, we can search for information on virtually any topic, quickly and conveniently.

We can also access the internet to read the news, watch videos, and stay up to date with the latest developments in the world.

Smartphones also make it easy to stay connected with friends, family, and loved ones no matter where they are in the world.

This means that people can stay in contact with their loved ones even when they are separated by distance.

We can also use our smartphones to share photos, videos, and other media with our contacts, allowing us to stay updated on each others lives.

Smartphones also provide us with an array of entertainment options.

We can watch movies, listen to music, and play games on our phones.

This means that we can have access to entertainment no matter where we are.

We can also use our phones to access streaming services such as Netflix, Hulu, and Amazon Prime Video, making it easier than ever to watch our favorite movies and shows.

Lastly, smartphones have revolutionized the way we shop.

We can now purchase products and services from the comfort of our own homes, eliminating the need to travel to a store.

We can also compare prices and read reviews to ensure that we are getting the best deal.

Smartphones have also made it easier to keep track of our finances, as we can access our bank accounts and pay bills with just a few taps.

In summary, smartphones offer a range of advantages that make them an essential tool in our daily lives.

They provide us with access to a wealth of information, allow us to stay connected with people from all over the world, and offer us an array of entertainment options.

They also make it easier to shop and manage our finances.

Disadvantages of Smartphones

Smartphones can be a distraction from face-to-face interactions and can lead to further disconnection from those around us.

Smartphones can lead to an over-reliance on technology, reducing our ability to think critically or problem solve independently.

They can also be a source of anxiety, as we are always connected and can easily become overwhelmed by the amount of information and notifications that come our way.

Additionally, smartphones can be an addiction, with people becoming dependent on their devices and losing track of time.

This can lead to a lack of productivity, as well as mental and physical health problems.

Finally, smartphones can be a source of cyberbullying and can be used to spread false information, which can have serious consequences.

The Impact of Smartphones on Social Interactions

Smartphones have had a profound impact on the way we interact with each other.

With the ability to communicate and stay connected with anyone, anytime, anywhere, the concept of socializing has been drastically altered.

People can now stay in touch with friends and family, even when they are miles apart, and can access a wealth of information from around the world.

On the one hand, this has had a positive impact on our social lives.

People can now easily stay connected with their loved ones and have access to a wealth of information that was previously unavailable.

However, there is also a downside to smartphones: they can be a distraction from face-to-face interactions.

People may become so engrossed in their phones that they forget to interact with the people around them.

Furthermore, there is the issue of cyberbullying, which has become increasingly prevalent due to the rise of smartphones.

With the ability to send messages anonymously, people can be cruel and hurtful to others without facing any consequences.

This can lead to psychological distress and can even lead to suicide in extreme cases.

Overall, it is clear that smartphones have both positive and negative effects on social interactions.

While they can provide access to a wealth of information and allow people to stay connected with their loved ones, they can also be a distraction from face-to-face interactions and can lead to cyberbullying.

As such, it is important to be mindful of how we use our smartphones and remember to take time to interact with people around us.

Smartphones and Mental Health

Smartphones have become an integral part of our lives, and with that comes the potential for both positive and negative effects on our mental health.

On the one hand, smartphones provide us with access to a wealth of information and allow us to stay connected with people from all over the world.

This can be beneficial in terms of building relationships, reducing feelings of loneliness, and providing access to mental health resources.

On the other hand, smartphones can also be a distraction from face-to-face interactions, which can lead to feelings of alienation and even depression.

Additionally, spending too much time on smartphones can lead to feelings of anxiety or stress, as well as an increased risk of developing physical health problems.

At the same time, smartphones can also be used to help manage mental health conditions.

For example, there are a number of apps and websites available that can help people manage anxiety, depression, and even PTSD.

Additionally, apps like Calm, Headspace, and Happify can be used to help manage stress and improve overall wellbeing.

Ultimately, smartphones can have both positive and negative effects on mental health.

It is important to be mindful of how much time you are spending on your smartphone and to ensure that you are taking regular breaks from it to engage in other activities.

Additionally, if you need help managing mental health conditions, there are a variety of apps and websites available that can help.

Smartphones and Education

Smartphones are becoming increasingly prevalent in the classroom, and many students are using them to access educational resources.

Smartphones can provide students with a wealth of information and resources, including online textbooks, lecture slides, and educational videos.

Furthermore, they can be used to access online forums and discussion groups, where students can ask questions and get help from other students and experts.

Smartphones also allow students to access information quickly and easily, which can be particularly helpful for those studying for exams.

Additionally, students can use smartphone apps to keep track of their assignments, grades, and other important information.

All of these features make smartphones invaluable tools for students.

Smartphones and Productivity

Smartphones can be powerful tools when it comes to productivity.

They offer a wealth of applications designed to improve productivity, from note-taking apps to task managers.

Smartphones also provide access to the internet, which can be a great resource for research, creative projects, and more.

Additionally, they allow us to stay connected with our colleagues, clients, and peers, making it easier to collaborate and share ideas.

However, smartphones can also be a major distraction.

Notifications, email, and social media can pull our attention away from the task at hand and can be difficult to ignore.

Additionally, some research suggests that the blue light emitted by our devices can disrupt our circadian rhythms and make it difficult to focus on tasks.

Ultimately, smartphones can be a great asset when it comes to productivity.

However, its important to be mindful of how we use our devices and make sure to limit distractions when necessary.

With the right approach and discipline, smartphones can be powerful tools for getting work done.

Smartphones and Privacy

When it comes to smartphones, it is important to consider the implications of privacy.

Smartphones are incredibly powerful tools, and with that comes the potential for misuse.

Smartphones are able to store and access large amounts of personal data, and if that data falls into the wrong hands, it could be used for malicious purposes.

This is why it is essential to ensure that security measures are in place to protect your data.

It is also important to be mindful of the privacy settings on individual apps and websites, as well as the security settings on your phone.

By taking the necessary precautions, you can ensure that your data remains safe while still enjoying the benefits of your smartphone.

Final Thoughts

Ultimately, smartphones are a powerful tool that can be used for good or ill.

They can provide us with access to a wealth of knowledge, keep us connected with people from all over the world, and help to increase our productivity.

However, it is important to be mindful of the potential negative impacts of smartphones, such as their ability to distract us and interfere with our social interactions.

To make the most of the advantages that smartphones offer, it is important to use them responsibly and in moderation.

With a mindful approach to using smartphones, we can make the most of all the benefits that come with them, while mitigating the potential risks.

James Miller

He believes that technology should be fun and easy to use. That’s why he wants to make sure that everyone has access to the information they need to get the most out of their devices.

Recent Posts

Is OnePlus Available in Stores? (FIND OUT HERE)

Yes, OnePlus smartphones are available in various stores, including authorized OnePlus retail outlets, electronic stores, and online retailers like Amazon and Best Buy. You can check the official...

Is OnePlus a Good Brand? Discover the Truth here

Yes, OnePlus is a reputable brand known for delivering high-quality smartphones with impressive features at competitive prices. Their devices often receive positive reviews for their performance,...

• PRO Courses Guides New Tech Help Pro Expert Videos About wikiHow Pro Upgrade Sign In
• EDIT Edit this Article
• EXPLORE Tech Help Pro About Us Random Article Quizzes Request a New Article Community Dashboard This Or That Game Popular Categories Arts and Entertainment Artwork Books Movies Computers and Electronics Computers Phone Skills Technology Hacks Health Men's Health Mental Health Women's Health Relationships Dating Love Relationship Issues Hobbies and Crafts Crafts Drawing Games Education & Communication Communication Skills Personal Development Studying Personal Care and Style Fashion Hair Care Personal Hygiene Youth Personal Care School Stuff Dating All Categories Arts and Entertainment Finance and Business Home and Garden Relationship Quizzes Cars & Other Vehicles Food and Entertaining Personal Care and Style Sports and Fitness Computers and Electronics Health Pets and Animals Travel Education & Communication Hobbies and Crafts Philosophy and Religion Work World Family Life Holidays and Traditions Relationships Youth
• Browse Articles
• Learn Something New
• Quizzes Hot
• This Or That Game
• Train Your Brain
• Explore More
• Support wikiHow
• About wikiHow
• Log in / Sign up
• Education and Communications
• Critical Reviews

How to Write a Smartphone Review

Last Updated: May 13, 2024 Fact Checked

This article was co-authored by Marissa Levis . Marissa Levis is an English Teacher in the Morris County Vocational School District. She previously worked as an English director at a tutoring center that caters to students in elementary and middle school. She is an expert in creating a curriculum that helps students advance their skills in secondary-level English, focusing on MLA formatting, reading comprehension, writing skills, editing and proofreading, literary analysis, standardized test preparation, and journalism topics. Marissa received her Master of Arts in Teaching from Fairleigh Dickinson University. This article has been fact-checked, ensuring the accuracy of any cited facts and confirming the authority of its sources. This article has been viewed 96,024 times.

In an age where consumers are increasingly relying on each other for honest and straightforward recommendations of new products, writing or recording a smartphone review is a great way to help potential buyers get a feel for a new phone. Technology blogs and video sharing sites like YouTube are all the rage for smartphone reviews and adding reviews to your blog on a constant basis can create a trusting following of readers who come to rely on your reliable advice. You can write or film your own smartphone review with help from the following tips.

• If you don't already own the smartphone in question (for example, if you're reviewing a range of phones for your blog), visit one of the smartphone carrier's retail store and play around with the phone, or ask a friend who owns the device if you can use it for a day. Learn about the phone's features, design, pros, cons, and the price.

• Be sure to include any relevant hyperlinks to help customers find the information you're referring to, perhaps even purchase locations.

• Talk about what you like about the smartphone you're reviewing, how easy it is to use, what features in particular are of worthy note.

• Look at the advertising blurb accompanying the smartphone. Consider whether you think the smartphone in question lives up to the hype or whether there are aspects that you don't agree with. It will be important to compare your experience with the advertised promises.
• Even summarizing the review with the best "pro" and the most annoying or disabling "con" can be a good overall summary for readers. For example, "Pros: This smartphone has a battery that outlasts any of its competitors in the same niche. Cons: Its price and the limited color range."

• Post your review on a blog, on a review site, or anywhere else relevant and be sure to respond to comments made to you. Sometimes developers will also respond if your review has struck a chord, so it pays to monitor your review actively for a good period of time.

Expert Q&A

• If you have spotted anything unusual about the smartphone that hasn't necessarily being promoted as a feature, such as an ability to use it somewhere that your previous phones have never worked (for example, if doing field work, etc.), be sure to point this out. Your personal experiences can influence other people who might be in the same situation as you. Thanks Helpful 0 Not Helpful 0
• Be cautious about anything that might be a service provider [AT&T, Rogers, Bell etc.] issue. Not everybody uses the same provider, so if you're having reception problems, gather more data before you include it in the review. Thanks Helpful 0 Not Helpful 0
• You might like to extend your reviewing skills to apps that are supported by the smartphone in question. In this way, you can build a large repository of useful information related to the topic of smartphones. Thanks Helpful 0 Not Helpful 0

• Be objective. If you love Android, don't just simply hate the iPhone. Be objective, and try to explain any issues you find with some clear and concise words. Don't try to mimic a paid publication on the take from a manufacturer like Apple. Thanks Helpful 0 Not Helpful 0
• A review is not a rant. If your smartphone turned out to be a complete lemon, contact the retailer and manufacturer before letting loose your frustration online. You may have been unlucky enough to get a faulty phone, or you may not be using it correctly, or the company may find a problem thanks to your complaint and do its best to rectify it with you. Thanks Helpful 5 Not Helpful 3

Things You'll Need

• Online access
• Access to smartphones

You Might Also Like

Expert Interview

Thanks for reading our article! If you’d like to learn more about writing, check out our in-depth interview with Marissa Levis .

• ↑ https://www.youtube.com/watch?v=0i4z5tqRzPg

About This Article

• Send fan mail to authors

Did this article help you?

Featured Articles

Trending Articles

Watch Articles

• Terms of Use
• Privacy Policy
• Do Not Sell or Share My Info
• Not Selling Info

Get all the best how-tos!

Sign up for wikiHow's weekly email newsletter

Essay on Smartphone

Students are often asked to write an essay on Smartphone in their schools and colleges. And if you’re also looking for the same, we have created 100-word, 250-word, and 500-word essays on the topic.

Let’s take a look…

100 Words Essay on Smartphone

Introduction.

Smartphones are handheld devices that combine the functions of a mobile phone and a computer. They are an essential part of modern life.

Smartphones have many features like calling, texting, internet browsing, and camera. They also have apps for education, entertainment, and social media.

Smartphones make communication easy. They help us in learning, provide entertainment, and keep us updated with the world.

Disadvantages

However, excessive use of smartphones can lead to addiction, poor eyesight, and can distract students from studies.

Smartphones are useful tools. But they should be used wisely to avoid negative effects.

250 Words Essay on Smartphone

Smartphones, the ubiquitous devices that have transformed our lives, are more than just communication tools. They are complex, multi-functional devices that have revolutionized the way we interact with the world.

The Evolution of Smartphones

The genesis of smartphones can be traced back to the early 1990s, but it was the launch of Apple’s iPhone in 2007 that truly marked the dawn of the smartphone era. Since then, technological advancements have led to the creation of devices that are increasingly powerful, versatile, and user-friendly.

Functionality and Applications

Today, smartphones serve many purposes. They are used for communication, entertainment, education, shopping, and even health monitoring. With the rise of mobile applications, smartphones have become a platform for innovation, enabling developers to create solutions that cater to diverse needs.

Impact on Society

The impact of smartphones on society is profound. They have facilitated instant communication, provided access to vast amounts of information, and democratized technology. However, they have also raised concerns about privacy, mental health, and social interaction.

Future Perspectives

The future of smartphones is exciting. With advancements in artificial intelligence, virtual reality, and 5G technology, smartphones are poised to become even more integral to our lives. However, it is crucial to address the challenges they pose to ensure their benefits are maximized.

In conclusion, smartphones are transformative devices that have reshaped our world. As we navigate the future, it is essential to harness their potential responsibly and innovatively.

500 Words Essay on Smartphone

Introduction to the smartphone era.

The advent of smartphones has significantly revolutionized the way we communicate, interact, and perceive the world. As multifunctional devices, smartphones have become indispensable tools that facilitate not only communication but also education, entertainment, and business operations.

The journey of smartphones began with IBM’s Simon Personal Communicator in 1992, which was the first device to combine telephony with computing abilities. However, the real transformation occurred with the launch of Apple’s iPhone in 2007. This event marked the onset of the era of touch-screen, app-based smartphones. Today, smartphones are equipped with advanced features like high-resolution cameras, GPS, and a multitude of apps catering to diverse needs.

Impact on Communication and Information

Smartphones have redefined communication, making it instant and borderless. With apps like WhatsApp, FaceTime, and Skype, we can connect with anyone around the globe within seconds. Furthermore, smartphones have democratized information. Internet connectivity has made vast amounts of data accessible at our fingertips, enabling us to stay updated and informed.

The Role of Smartphones in Education

Smartphones have also found a significant place in the realm of education. They serve as portable knowledge hubs, with numerous educational apps and e-books available. During the COVID-19 pandemic, smartphones played a crucial role in facilitating remote learning, thus ensuring the continuity of education.

Smartphones and Entertainment

Entertainment has been another domain profoundly impacted by smartphones. With apps like Netflix, Spotify, and YouTube, smartphones have become personalized entertainment centers. They also host a myriad of games, catering to different age groups and interests.

Smartphones in Business

In the business world, smartphones have enabled the growth of e-commerce and digital marketing. They also facilitate remote work, making businesses more flexible and resilient. Furthermore, smartphones have given rise to app-based businesses, creating new economic opportunities.

The Dark Side of Smartphones

Despite their benefits, smartphones have a darker side. Overdependence can lead to addiction, impacting mental health and social relationships. Additionally, smartphones pose privacy risks, as they collect vast amounts of personal data, which can be misused.

Conclusion: The Future of Smartphones

As we move forward, the role of smartphones in our lives is likely to grow. With advancements in technologies like Artificial Intelligence and Augmented Reality, the future smartphones will become even more integrated into our daily lives. However, it is crucial to balance the benefits of smartphones with their potential risks, to ensure they serve as tools for empowerment rather than sources of harm.

That’s it! I hope the essay helped you.

If you’re looking for more, here are essays on other interesting topics:

• Essay on Impact of Smartphone
• Essay on Advantage of Smartphone
• Essay on Travelling as a Part of Education

Apart from these, you can look at all the essays by clicking here .

Happy studying!

Leave a Reply Cancel reply

Your email address will not be published. Required fields are marked *

Save my name, email, and website in this browser for the next time I comment.

• Share full article

Advertisement

Subscriber-only Newsletter

David Wallace-Wells

Are smartphones driving our teens to depression.

By David Wallace-Wells

Opinion Writer

Here is a story. In 2007, Apple released the iPhone, initiating the smartphone revolution that would quickly transform the world. In 2010, it added a front-facing camera, helping shift the social-media landscape toward images, especially selfies. Partly as a result, in the five years that followed, the nature of childhood and especially adolescence was fundamentally changed — a “great rewiring,” in the words of the social psychologist Jonathan Haidt — such that between 2010 and 2015 mental health and well-being plummeted and suffering and despair exploded, particularly among teenage girls.

For young women, rates of hospitalization for nonfatal self-harm in the United States, which had bottomed out in 2009, started to rise again, according to data reported to the C.D.C., taking a leap beginning in 2012 and another beginning in 2016, and producing , over about a decade, an alarming 48 percent increase in such emergency room visits among American girls ages 15 to 19 and a shocking 188 percent increase among girls ages 10 to14.

Here is another story. In 2011, as part of the rollout of the Affordable Care Act, the Department of Health and Human Services issued a new set of guidelines that recommended that teenage girls should be screened annually for depression by their primary care physicians and that same year required that insurance providers cover such screenings in full. In 2015, H.H.S. finally mandated a coding change, proposed by the World Health Organization almost two decades before, that required hospitals to record whether an injury was self-inflicted or accidental — and which seemingly overnight nearly doubled rates for self-harm across all demographic groups. Soon thereafter, the coding of suicidal ideation was also updated. The effect of these bureaucratic changes on hospitalization data presumably varied from place to place. But in one place where it has been studied systematically, New Jersey, where 90 percent of children had health coverage even before the A.C.A., researchers have found that the changes explain nearly all of the state’s apparent upward trend in suicide-related hospital visits, turning what were “essentially flat” trendlines into something that looked like a youth mental health “crisis.”

Could both of these stories be partially true? Of course: Emotional distress among teenagers may be genuinely growing while simultaneous bureaucratic and cultural changes — more focus on mental health, destigmatization, growing comfort with therapy and medication — exaggerate the underlying trends. (This is what Adriana Corredor-Waldron, a co-author of the New Jersey study, believes — that suicidal behavior is distressingly high among teenagers in the United States and that many of our conventional measures are not very reliable to assess changes in suicidal behavior over time.) But over the past several years, Americans worrying over the well-being of teenagers have heard much less about that second story, which emphasizes changes in the broader culture of mental illness, screening guidelines and treatment, than the first one, which suggests smartphones and social-media use explain a whole raft of concerns about the well-being of the country’s youth.

When the smartphone thesis first came to prominence more than six years ago, advanced by Haidt’s sometime collaborator Jean Twenge, there was a fair amount of skepticism from scientists and social scientists and other commentators: Were teenagers really suffering that much? they asked. How much in this messy world could you pin on one piece of technology anyway? But some things have changed since then, including the conventional liberal perspective on the virtues of Big Tech, and, in the past few years, as more data has rolled in and more red flags have been raised about American teenagers — about the culture of college campuses, about the political hopelessness or neuroticism or radicalism or fatalism of teenagers, about a growing political gender divide, about how often they socialize or drink or have sex — a two-part conventional wisdom has taken hold across the pundit class. First, that American teenagers are experiencing a mental health crisis; second, that it is the fault of phones.

“Smartphones and social media are destroying children’s mental health,” the Financial Times declared last spring. This spring, Haidt’s new book on the subject, The Anxious Generation: How the Great Rewiring of Childhood Is Causing an Epidemic of Mental Illness, debuted at the top of the New York Times best-seller list. In its review of the book, The Guardian described the smartphone as “a pocket full of poison,” and in an essay , The New Yorker accepted as a given that Gen Z was in the midst of a “mental health emergency” and that “social media is bad for young people.” “Parents could see their phone-obsessed children changing and succumbing to distress,” The Wall Street Journal reflected . “Now we know the true horror of what happened.”

But, well, do we? Over the past five years, “Is it the phones?” has become “It’s probably the phones,” particularly among an anxious older generation processing bleak-looking charts of teenage mental health on social media as they are scrolling on their own phones. But however much we may think we know about how corrosive screen time is to mental health, the data looks murkier and more ambiguous than the headlines suggest — or than our own private anxieties, as parents and smartphone addicts, seem to tell us.

What do we really know about the state of mental health among teenagers today? Suicide offers the most concrete measure of emotional distress, and rates among American teenagers ages 15 to 19 have indeed risen over the past decade or so, to about 11.8 deaths per 100,000 in 2021 from about 7.5 deaths per 100,000 in 2009. But the American suicide epidemic is not confined to teenagers. In 2022, the rate had increased roughly as much since 2000 for the country as a whole, suggesting a national story both broader and more complicated than one focused on the emotional vulnerabilities of teenagers to Instagram. And among the teenagers of other rich countries, there is essentially no sign of a similar pattern. As Max Roser of Our World in Data recently documented , suicide rates among older teenagers and young adults have held roughly steady or declined over the same time period in France, Spain, Italy, Austria, Germany, Greece, Poland, Norway and Belgium. In Sweden there were only very small increases.

Is there a stronger distress signal in the data for young women? Yes, somewhat. According to an international analysis by The Economist, suicide rates among young women in 17 wealthy countries have grown since 2003, by about 17 percent, to a 2020 rate of 3.5 suicides per 100,000 people. The rate among young women has always been low, compared with other groups, and among the countries in the Economist data set, the rate among male teenagers, which has hardly grown at all, remains almost twice as high. Among men in their 50s, the rate is more than seven times as high.

In some countries, we see concerning signs of convergence by gender and age, with suicide rates among young women growing closer to other demographic groups. But the pattern, across countries, is quite varied. In Denmark, where smartphone penetration was the highest in the world in 2017, rates of hospitalization for self-harm among 10- to 19-year-olds fell by more than 40 percent between 2008 and 2016. In Germany, there are today barely one-quarter as many suicides among women between 15 and 20 as there were in the early 1980s, and the number has been remarkably flat for more than two decades. In the United States, suicide rates for young men are still three and a half times as high as for young women, the recent increases have been larger in absolute terms among young men than among young women, and suicide rates for all teenagers have been gradually declining since 2018. In 2022, the latest year for which C.D.C. data is available, suicide declined by 18 percent for Americans ages 10 to 14 and 9 percent for those ages 15 to 24.

None of this is to say that everything is fine — that the kids are perfectly all right, that there is no sign at all of worsening mental health among teenagers, or that there isn’t something significant and even potentially damaging about smartphone use and social media. Phones have changed us, and are still changing us, as anyone using one or observing the world through them knows well. But are they generating an obvious mental health crisis?

The picture that emerges from the suicide data is mixed and complicated to parse. Suicide is the hardest-to-dispute measure of despair, but not the most capacious. But while rates of depression and anxiety have grown strikingly for teenagers in certain parts of the world, including the U.S., it’s tricky to disentangle those increases from growing mental-health awareness and destigmatization, and attempts to measure the phenomenon in different ways can yield very different results.

According to data Haidt uses, from the U.S. National Survey on Drug Use and Health, conducted by the Substance Abuse and Mental Health Services Administration, the percent of teenage girls reporting major depressive episodes in the last year grew by about 50 percent between 2005 and 2017, for instance, during which time the share of teenage boys reporting the same grew by roughly 75 percent from a lower level. But in a biannual C.D.C. survey of teenage mental health, the share of teenagers reporting that they had been persistently sad for a period of at least two weeks in the past year grew from only 28.5 percent in 2005 to 31.5 percent in 2017. Two different surveys tracked exactly the same period, and one showed an enormous increase in depression while the other showed almost no change at all.

And if the rise of mood disorders were a straightforward effect of the smartphone, you’d expect to see it everywhere smartphones were, and, as with suicide, you don’t. In Britain, the share of young people who reported “feeling down” or experiencing depression grew from 31 percent in 2012 to 38 percent on the eve of the pandemic and to 41 percent in 2021. That is significant, though by other measures British teenagers appear, if more depressed than they were in the 2000s, not much more depressed than they were in the 1990s.

Overall, when you dig into the country-by-country data, many places seem to be registering increases in depression among teenagers, particularly among the countries of Western Europe and North America. But the trends are hard to disentangle from changes in diagnostic patterns and the medicalization of sadness, as Lucy Foulkes has argued , and the picture varies considerably from country to country. In Canada , for instance, surveys of teenagers’ well-being show a significant decline between 2015 and 2021, particularly among young women; in South Korea rates of depressive episodes among teenagers fell by 35 percent between 2006 and 2018.

Because much of our sense of teenage well-being comes from self-reported surveys, when you ask questions in different ways, the answers vary enormously. Haidt likes to cite data collected as part of an international standardized test program called PISA, which adds a few questions about loneliness at school to its sections covering progress in math, science and reading, and has found a pattern of increasing loneliness over the past decade. But according to the World Happiness Report , life satisfaction among those ages 15 to 24 around the world has been improving pretty steadily since 2013, with more significant gains among women, as the smartphone completed its global takeover, with a slight dip during the first two years of the pandemic. An international review published in 2020, examining more than 900,000 adolescents in 36 countries, showed no change in life satisfaction between 2002 and 2018.

“It doesn’t look like there’s one big uniform thing happening to people’s mental health,” said Andrew Przybylski, a professor at Oxford. “In some particular places, there are some measures moving in the wrong direction. But if I had to describe the global trend over the last decade, I would say there is no uniform trend showing a global crisis, and, where things are getting worse for teenagers, no evidence that it is the result of the spread of technology.”

If Haidt is the public face of worry about teenagers and phones, Przybylski is probably the most prominent skeptic of the thesis. Others include Amy Orben, at the University of Cambridge, who in January told The Guardian, “I think the concern about phones as a singular entity are overblown”; Chris Ferguson, at Stetson University, who is about to publish a new meta-analysis showing no relationship between smartphone use and well-being; and Candice Odgers, of the University of California, Irvine, who published a much-debated review of Haidt in Nature, in which she declared “the book’s repeated suggestion that digital technologies are rewiring our children’s brains and causing an epidemic of mental illness is not supported by science.”

Does that overstate the case? In a technical sense, I think, no: There may be some concerning changes in the underlying incidence of certain mood disorders among American teenagers over the past couple of decades, but they are hard to separate from changing methods of measuring and addressing mental health and mental illness. There isn’t great data on international trends in teenage suicide — but in those places with good reporting, the rates are generally not worsening — and the trends around anxiety, depression and well-being are ambiguous elsewhere in the world. And the association of those local increases with the rise of the smartphone, while now almost conventional wisdom among people like me, is, among specialists, very much a contested claim. Indeed, even Haidt, who has also emphasized broader changes to the culture of childhood , estimated that social media use is responsible for only about 10 percent to 15 percent of the variation in teenage well-being — which would be a significant correlation, given the complexities of adolescent life and of social science, but is also a much more measured estimate than you tend to see in headlines trumpeting the connection. And many others have arrived at much smaller estimates still.

But this all also raises the complicated question of what exactly we mean by “science,” in the context of social phenomena like these, and what standard of evidence we should be applying when asking whether something qualifies as a “crisis” or “emergency” and what we know about what may have caused it. There is a reason we rarely reduce broad social changes to monocausal explanations, whether we’re talking about the rapid decline of teenage pregnancy in the 2000s, or the spike in youth suicide in the late ’80s and early 1990s, or the rise in crime that began in the 1960s: Lives are far too complex to easily reduce to the influence of single factors, whether the factor is a recession or political conditions or, for that matter, climate breakdown.

To me, the number of places where rates of depression among teenagers are markedly on the rise is a legitimate cause for concern. But it is also worth remembering that, for instance, between the mid-1990s and the mid-2000s, diagnoses of American youth for bipolar disorder grew about 40-fold , and it is hard to find anyone who believes that change was a true reflection of underlying incidence. And when we find ourselves panicking over charts showing rapid increases in, say, the number of British girls who say they’re often unhappy or feel they are a failure, it’s worth keeping in mind that the charts were probably zoomed in to emphasize the spike, and the increase is only from about 5 percent of teenagers to about 10 percent in the first case, or from about 15 percent to about 20 percent in the second. It may also be the case, as Orben has emphasized , that smartphones and social media may be problematic for some teenagers without doing emotional damage to a majority of them. That’s not to say that in taking in the full scope of the problem, there is nothing there. But overall it is probably less than meets the eye.

If you are having thoughts of suicide, call or text 988 to reach the 988 Suicide and Crisis Lifeline or go to SpeakingOfSuicide.com/resources for a list of additional resources.

Further reading (and listening):

On Jonathan Haidt’s After Babel Substack , a series of admirable responses to critics of “The Anxious Generation” and the smartphone thesis by Haidt, his lead researcher Zach Rausch, and his sometime collaborator Jean Twenge.

In Vox, Eric Levitz weighs the body of evidence for and against the thesis.

Tom Chivers and Stuart Ritchie deliver a useful overview of the evidence and its limitations on the Studies Show podcast.

Five experts review the evidence for the smartphone hypothesis in The Guardian.

A Substack survey of “diagnostic inflation” and teenage mental health.

138 Smartphone Essay Topic Ideas & Examples

🏆 best smartphone topic ideas & essay examples, 💡 most interesting smartphone topics to write about, 🥇 good research topics about smartphone, 📌 list of topics to write about smartphone, ❓ smartphone related questions.

• Smartphone Addiction Problem Statement Uncontrolled use of smartphone requires users to review the need to respond to smartphone alerts, deactivate the alerts, and consult their colleagues rather than the phone because such actions can reduce anxiety. Smartphone addiction is […]
• Apple Versus Samsung Smartphones With the introduction of the Samsung Galaxy S series smartphones, Samsung competes with Apple’s iPhone. The screen, look and feel of Samsung smartphones is strikingly similar to that of the iPhone.
• Using Smartphones in Learning The other purpose of the study is to understand the recent developments that have been made to the smartphones and how people are able to adopt the changes.
• The Impact of Smartphones on Young People’s Social Life On the one hand, a cellphone enables young people to call their parents when they are in trouble and need help.
• Apple’s Competition in Chinese Smartphone Market The analysis will examine the following forces that affect the company: industry competition, the bargaining power of suppliers, the bargaining power of consumers, the threat of new entrants, and the threat of substitute products.
• Smartphone Market’s Economic Analysis Thus, the purpose of the current exploration is associated with the analysis of the smartphone market, with the emphasis placed on Samsung and Apple, which are considered significant rivals in the industry.
• Smartphones Role in Lifestyles Changes The fast and quick connectivity of smartphones to the internet provides a wide spectrum of understanding issues that individuals face in their professional and social lives. Creativity and innovation that smartphones facilitate lead to enhanced […]
• Smartphone-Related Cognitive and Ethical Issues The remarkable rise of smartphones and the rapid adoption of mobile computing are two of the most important developments in contemporary information and communication technology.
• Social Issues: Smartphones’ Positive Impacts In the past, it is expensive to make calls. In the past, it is not convenient to make calls using payphones.
• New Product Feasibility: Tecno N7 Series Android Smartphone According to Abou-Moghli and Abdallah, product feasibility is one of the essential business marketing elements in the market analysis process, which is deemed as a scientific tool of obtaining relevant information pertinent to the provision […]
• Smartphones Affect and Change Modern Life Now, it is time to manage technological addiction and enhance the benefits of using smartphones for new creative designs and high-quality data exchange.
• Nokia Pure View Smartphone Marketing Strategy In order to achieve the above objectives, other key issues such as the geographical environment where the Nokia targets to market the phone, the target population and the competitors in the market will also have […]
• Smartphones and Information Technology Systems Management Smartphones such as BlackBerry have applications that increase the accessibility of information, which is critical in enhancing the organisations’ effectiveness particularly in the management of tasks and projects.
• Blackberry Smartphone Consumer Behavior Description of Internal Variables Consumer Personality: the personality of the consumer especially that of the middle class has a significant influence on the purchase decisions. With the brand and the specific outlet in mind, a […]
• Factors Affecting Youth’s Behaviors Towards Purchasing a Smartphone Objectives Understand the background of the smartphone industry Analyze the smartphone market trends and the role played by the youths in this marekts Understand reasons why youths buy smartphones through a survey on 100 people […]
• Internet and Smartphone Effect In this essay, I analyze the arguments advanced by experts in five different publications in order to investigate the consequences of internet and smartphone use on human behavior and relationships during the COVID-19 epidemic.
• The Impact of Smartphones on Mental and Emotional Well-Being Twenge, the author argues that the widespread use of smartphones among teenagers and young adults has led to a decline in their mental and emotional well-being.
• Smartphones: Benefits and Side-Effects The findings have led to a greater understanding of smartphones’ influence on young people of school age, which lays the framework for minimizing the harmful effects of smartphone usage in children and adolescents.
• Smartphone Selection: Decision-Making Assistance For the front camera specifications, both iPhone 14 Pro Max and iPhone 14 have no significant differences. The price for both the Samsung Galaxy s22 and iPhone 14 is $799, but iPhone 14 Pro Max […]
• Smartphone Addiction in the United States With the advent of phones that have the function of many other gadgets, people began to move away from the real world into the virtual one. This paper examines the essence of the issue of […]
• Smartphones: Development and Popularity The claims that, in the future, smartphones will become the most important electronic devices are outdated by now: they already are.
• Exposure to Smartphones on Learning Development in Preschoolers Parents might allow their children to use smartphones not to be distracted during work, to put children to sleep or to make them eat during the appropriate time.
• Assessing Smartphone Brand Preferences or Use Thus, for other mobile manufacturers to get where the Apple brand is in terms of popularity and market shares, they need to develop revolutionary devices that change who people view and use mobile phones.
• Smartphone Technology and Its Brief History One of the ways that prove smartphone technology has impacted the global economy is the dramatic increase in the capacity to communicate and collaborate.
• How Smartphones Changed Society and the World Introduction of the smartphone to mass public Driven by iPhone created by Apple and Steve Jobs in 2007 Revolutionized the world of communications and information exchange Smartphone went from being a tool to the […]
• Smartphone Blackberry Company Another important feature of the smart phone is the QWERTY keyboard, which is the same as that of the normal computer.
• Smartphones and Generations: Hyper-Connected World First, it is social network, the essence of which is to communicate with other people and peers, as well as to show the details of their lives.
• US Smartphone Market and the Movie Industry In this work tables and other statistical graphics are used to plan, collect and prepare data on the US smartphone market and the movie industry.
• Privacy and Smartphone Apps: Documentary Review The documentary is about the privacy risks posed by the many apps that people are using on their smartphones. If a person is not ready to give access to the information the application wants, they […]
• Apple and Its Product Range in the UK Smartphone Market The history of Apple Inc.is closely tied to that of the technology industry, with the company being one of the best-recognized developers and sellers of smartphones, computers, and software.
• Nokia’s Lumia Smartphone’s Annual Marketing Plan The Nokia Corporation is hopeful of broadening the market of the Lumia series to attract new segments like the lower end of the market, traditionally the stronghold of Nokia with Symbian handsets and feature phones.
• Saudi Developers in Smartphone Applications There are many companies in the world involved in the development of the apps available in the market today. The above successes show the potential that Saudi Arabia has in the smart phone app development.
• Blackberry and iPhone: Exploits of Smartphones The trends in mobile technology changed the entire concept of mobile phones and different models are entering the market. Unauthorized calls from Blackberry and iPhones have, in many cases, caused threats to the security of […]
• Social Media, Smartphones and Confidentiality in the Healthcare System The purpose of the paper is to provide an in-depth understanding of the consequences of the breach of patients’ confidentiality with social media and cell phones, as well as of regulatory acts on the issue.
• Smartphone Market and Consumer Behavior The ability to improve communication, entertainment, and online education by using smartphones is a milestone in the development of the world.
• Smartphones for Work: Advantages and Disadvantages Employees are not bound to the office, and they can negotiate different working hours that are comfortable for them and their customers.
• Smartphone Store Commercial Website: Description Plan Considering the growth of the demand of the smartphone market, the present report provides a description plan for an online smartphone store, in which users will be able to purchase different brands of smartphones.
• Smartphones in Europe & Asia: Marketing Management Therefore, to market the organization producing smartphones for Europe and Asia, one will need to build the competitive advantage that will allow target audiences to pay closer attention to the brand in question. The key […]
• “Are Smartphones Really Destroying the Lives of Teenagers?” by Flora By showing the inconsistencies in research results, the author suggests that the use of smartphones on its own is not a dangerous behavior, but how teens use smartphones could play both a positive and a […]
• Smartphones and Mobile Applications in Business Given the high level of advancement in the field of information and technology, Atluri et al asserted that the tastes and preferences of consumers are likely to change as a result of their increased needs.
• Survival of the Fittest in the Smartphone Industry They strive to estimate the potential for Nokia’s revival in the technology industry, understand the factors that led to the company’s demise, evaluate the factors that led to the prosperity of its rivals and formulate […]
• Risky Business: Students and Smartphones The study builds upon the previous research, and it is made visible in the introduction where the authors referred to the findings of many different studies concerning the issues or mobile security, their prevalence, the […]
• Samsung Galaxy Note 7 Smartphone’s Security Issue The exploding smartphones have shown that, although the Samsung Company’s status and quality of products were supposed to be at the highest levels, they are not trustworthy from the consumers’ point of view.
• Steering Wheel and Smartphone: A Deadly Combination Moreover, being distracted by mobile devices can cause harm not only to their owners but also to total strangers who merely happen to be in the wrong place at the wrong time.
• Samsung Company Smartphone Marketing The article gives a summary of the Smartphone market controlled by Samsung and the ensuing advertising expenditure from mobile marketing and advertisements because of the strategy employed by Samsung.
• The Smartphone: Anatomy of an Industry The smartphones industry, which has phone makers like Apple, HTC, LG, Samsung, and Lenovo, falls under the Electronic Computer Manufacturing section of the NAICS, with code 33-4111.
• Smartphones for Children: Design and Usage To reduce the above challenges, the software should follow a design that enables the children to use them conveniently. Additionally, the software needs to have features that match the demand presented by the children.
• Technologies: Customized Smartphones for Children The rising demand for the Smartphone took place because of the benefits that it has for both the parents and the children.
• Smartphones and Dumb Behavior On the one hand, the use of smartphones negatively affects development of short-term memory in people. On the other hand, it is also found that the abundance of information negatively affects people.
• Smartphone Ownership in the World In regard with mobile phone technology, this paper examines the growing use of smart phones in the world, and the kind of impact these types of phones have on people’s lives.
• Mobile Application Software Pros & Cons It is the software installed in apple phones such as iphone, the iPod Touch, and the ipad. This is the latest operating system, and it has various advantages.
• Value of Smartphone Security The security standards include the use keystroke dynamics, monitoring the time of key holding, the flight time, multiplayer access regulations, priority regarding the application accessibility.
• Apple Inc. Smartphones Strategic Marketing Plan Within the market segment, the objectives of the marketing mix includes To ensure sales increase by 40% To ensure increase in the sales margin by more than 20% To ensure increase in the total Smartphones […]
• Social Networks Application in Smartphone With the emergence and widespread use social networks applications, the challenge of distance should no longer be a hindrance in a relationship, especially in the wake of Smartphones.
• Apple Inc. Smartphone Marketing Strategy Presentation The popularity of the brand has enabled the company to be successful and become the leader in Smartphones market under a highly competitive and volatile environment.
• Smartphone as a Communication Sector Revolution The Smartphone has done much to pull the world towards the core of digital database. The youth has exploited this utility to download music and movies from the world over and store it for their […]
• Smartphone Technology: Apple, Samsung, and Nokia The iPhone 5 is a windows product that has proved to be among the best selling smart phone in the market.
• Characterizing Smartphone Usage: Diversity and End User Context
• Android and the Smartphone Market
• Consumer Preferences and Implicit Prices of Smartphone Characteristics
• Consequences of Late-Night Smartphone Use
• Competition between the Most Successful Smartphone Companies
• Brain Drain: The Mere Presence of One’s Own Smartphone Reduces Available Cognitive Capacity
• Employee Monitoring System Using Android Smartphone
• Gender and Income Effects of Smartphone Use
• The Areas of Usage of Smartphones
• What Age Should a Child Get a Smartphone: Pros and Cons of Early Phone Use
• Diagnostic Criteria for Smartphone Addiction
• The Impact of Smartphone Advertising on Consumer Purchase Intention
• Accessing the Smartphone from Remote Location Using Android Application
• Enhancing Patient-Caregivers Relationship: Innovative Use of Smartphone
• The Growth of the Application Market for Smartphones and Tablets
• Increasing Dependence on Computers and Smartphones
• New Trends in the Chinese Smartphone Market
• Barriers, Benefits, and Beliefs of Brain Training Smartphone Apps
• Essential Smartphone Filmmaking Accessories
• Smartphone Use Among Japanese Medical Students
• Changing the Competitive Landscape of the Smartphone Industry
• Fundamental Requirements for Smartphone Commercial Applications Development
• Australian Smartphone Industry Analysis
• Driving Forces for Smartphone Industry
• Focus, Mindfulness, and Using a Smartphone
• Global Mobile Gaming Market Growth Driven by Sprialing Smartphone Sales
• Evaluating the Security of Smartphone Messaging Applications
• History and Future Trends in Smartphone Technology
• The Effect of Frequent Smartphone Use on Social Skills
• Smartphone: Mobile Phone and Excellent Time Killer
• The Impact of Smartphones on Our Culture
• Modeling Habitual and Addictive Behavior With a Smartphone
• Intangible Assets and Value Capture in Global Value Chains: The Smartphone Industry
• Smartphone Addiction in Japanese Youth: Social Isolation and the Social Network
• Mobile Technology: Pros and Cons of California Smartphone Bill
• Oppose Arguing That Smartphone Helps Student on Learning
• Product Features Influencing Purchase Decisions for Smartphones
• The Impact of Smartphone Use by Parents on Their Children
• Product Life Cycles Analysis for Smartphone
• Relationship between Smartphone Usage in Young Adults and Depression
• Sedentary Behavior and Problematic Smartphone Use in Adolescents
• Smartphone Applications and Childhood Obesity
• Using Smartphone Apps for Cognitive Learning in Healthy Aging
• Smartphone Use and Academic Performance
• Social Media and Smartphone Habits
• The Advantages and Disadvantages of Having a Smartphone
• The Emergence, Opportunities, and Importance of Mobile E-Commerce Using Smartphones
• Smartphones and Its Integration into Our Daily Lives
• The Smartphone Revolution and Its Effects on Business
• Can Smartphone Apps That Use Biofeedback Help Reduce Stress?
• What Values and Motives Are the Drivers of Smartphone Use Activity?
• How Are Self-Esteem and Problematic Smartphone Use Among Adolescents Related?
• What Are the Pros and Cons of a Smartphone, Does It Help Our Lives?
• How Does Parents’ Use of Smartphones Affect Their Children?
• Does the Mere Presence of One’s Own Smartphone Reduce Available Cognitive Capacity?
• What Is the Relationship Between Smartphone Use and Sleep Quality in Chinese Students?
• How Does Smartphone Advertising Affect Consumers’ Willingness to Buy?
• What Factors Influence the Intention to Purchase a Smartphone?
• Is Prolonged Smartphone Use Before Bed Associated with Altered Resting-State Functional Connectivity of the Insula?
• What Are the Diagnostic Criteria for Smartphone Addiction?
• How Does Frequent Smartphone Use Affect Social Skills?
• Can Smartphone Applications Serve as Effective Cognitive Training Tools in Healthy Aging?
• What Is the Effect of Smartphone Use and Group Conversation on Pedestrian Speed?
• Should Children Own a Smartphone?
• What Product Features Influence Smartphone Purchase Decisions?
• How Are Young People’s Smartphone Use and Symptoms of Depression and Anxiety Related?
• What Is the Percentage of Smartphone Users in the US?
• Does the Fingerprint Sensor Take Smartphone Security to a Whole New Level?
• How Are Emotional Intelligence, Self-Regulation, and Smartphone Addiction Related to Student Well-Being and Quality of Life?
• What Are the Barriers, Benefits, and Beliefs About Brain Training Apps for Smartphones?
• Could a Person’s Impaired Decision-Making Process Be a Consequence of Smartphone Addiction?
• What Is the Relationship Between Smartphone Application Use and Student Performance?
• How Does the Use of Smartphones Negatively Affect Society?
• What Are the Effects of Late-Night Smartphone Use on Sleep?
• Are Telecom Firms Under Pressure to Keep Up with Smartphone Obsession?
• What Are the Driving Forces for the Smartphone Industry?
• How Are Sedentary Lifestyles and Problematic Smartphone Use Related in Chinese Adolescents?
• Why Do Mobile Users Trust Smartphone Social Networking Services?
• Does Smartphone Use Affect Gender and Income?
• Chicago (A-D)
• Chicago (N-B)

IvyPanda. (2024, February 29). 138 Smartphone Essay Topic Ideas & Examples. https://ivypanda.com/essays/topic/smartphone-essay-topics/

"138 Smartphone Essay Topic Ideas & Examples." IvyPanda , 29 Feb. 2024, ivypanda.com/essays/topic/smartphone-essay-topics/.

IvyPanda . (2024) '138 Smartphone Essay Topic Ideas & Examples'. 29 February.

IvyPanda . 2024. "138 Smartphone Essay Topic Ideas & Examples." February 29, 2024. https://ivypanda.com/essays/topic/smartphone-essay-topics/.

1. IvyPanda . "138 Smartphone Essay Topic Ideas & Examples." February 29, 2024. https://ivypanda.com/essays/topic/smartphone-essay-topics/.

Bibliography

IvyPanda . "138 Smartphone Essay Topic Ideas & Examples." February 29, 2024. https://ivypanda.com/essays/topic/smartphone-essay-topics/.

• Huawei Topics
• iPhone Topics
• Mass Communication Essay Topics
• Nokia Topics
• Samsung Topics
• Mobile Technology Paper Topics
• Samsung Galaxy Topics

• CBSE Class 10th
• CBSE Class 12th
• UP Board 10th
• UP Board 12th
• Bihar Board 10th
• Bihar Board 12th
• Top Schools in India
• Top Schools in Delhi
• Top Schools in Mumbai
• Top Schools in Chennai
• Top Schools in Hyderabad
• Top Schools in Kolkata
• Top Schools in Pune
• Top Schools in Bangalore

Products & Resources

• JEE Main Knockout April
• Free Sample Papers
• Free Ebooks
• NCERT Notes
• NCERT Syllabus
• NCERT Books
• RD Sharma Solutions
• Navodaya Vidyalaya Admission 2024-25
• NCERT Solutions
• NCERT Solutions for Class 12
• NCERT Solutions for Class 11
• NCERT solutions for Class 10
• NCERT solutions for Class 9
• NCERT solutions for Class 8
• NCERT Solutions for Class 7
• JEE Main 2024
• MHT CET 2024
• JEE Advanced 2024
• BITSAT 2024
• View All Engineering Exams
• Colleges Accepting B.Tech Applications
• Top Engineering Colleges in India
• Engineering Colleges in India
• Engineering Colleges in Tamil Nadu
• Engineering Colleges Accepting JEE Main
• Top IITs in India
• Top NITs in India
• Top IIITs in India
• JEE Main College Predictor
• JEE Main Rank Predictor
• MHT CET College Predictor
• AP EAMCET College Predictor
• GATE College Predictor
• KCET College Predictor
• JEE Advanced College Predictor
• View All College Predictors
• JEE Main Question Paper
• JEE Main Cutoff
• JEE Main Advanced Admit Card
• JEE Advanced Admit Card 2024
• Download E-Books and Sample Papers
• Compare Colleges
• B.Tech College Applications
• KCET Result
• MAH MBA CET Exam
• View All Management Exams

Colleges & Courses

• MBA College Admissions
• MBA Colleges in India
• Top IIMs Colleges in India
• Top Online MBA Colleges in India
• MBA Colleges Accepting XAT Score
• BBA Colleges in India
• XAT College Predictor 2024
• SNAP College Predictor
• NMAT College Predictor
• MAT College Predictor 2024
• CMAT College Predictor 2024
• CAT Percentile Predictor 2023
• CAT 2023 College Predictor
• CMAT 2024 Admit Card
• TS ICET 2024 Hall Ticket
• CMAT Result 2024
• MAH MBA CET Cutoff 2024
• Download Helpful Ebooks
• List of Popular Branches
• QnA - Get answers to your doubts
• IIM Fees Structure
• AIIMS Nursing
• Top Medical Colleges in India
• Top Medical Colleges in India accepting NEET Score
• Medical Colleges accepting NEET
• List of Medical Colleges in India
• List of AIIMS Colleges In India
• Medical Colleges in Maharashtra
• Medical Colleges in India Accepting NEET PG
• NEET College Predictor
• NEET PG College Predictor
• NEET MDS College Predictor
• NEET Rank Predictor
• DNB PDCET College Predictor
• NEET Admit Card 2024
• NEET PG Application Form 2024
• NEET Cut off
• NEET Online Preparation
• Download Helpful E-books
• Colleges Accepting Admissions
• Top Law Colleges in India
• Law College Accepting CLAT Score
• List of Law Colleges in India
• Top Law Colleges in Delhi
• Top NLUs Colleges in India
• Top Law Colleges in Chandigarh
• Top Law Collages in Lucknow

Predictors & E-Books

• CLAT College Predictor
• MHCET Law ( 5 Year L.L.B) College Predictor
• AILET College Predictor
• Sample Papers
• Compare Law Collages
• Careers360 Youtube Channel
• CLAT Syllabus 2025
• CLAT Previous Year Question Paper
• NID DAT Exam
• Pearl Academy Exam

Predictors & Articles

• NIFT College Predictor
• UCEED College Predictor
• NID DAT College Predictor
• NID DAT Syllabus 2025
• NID DAT 2025
• Design Colleges in India
• Top NIFT Colleges in India
• Fashion Design Colleges in India
• Top Interior Design Colleges in India
• Top Graphic Designing Colleges in India
• Fashion Design Colleges in Delhi
• Fashion Design Colleges in Mumbai
• Top Interior Design Colleges in Bangalore
• NIFT Result 2024
• NIFT Fees Structure
• NIFT Syllabus 2025
• Free Design E-books
• List of Branches
• Careers360 Youtube channel
• IPU CET BJMC
• JMI Mass Communication Entrance Exam
• IIMC Entrance Exam
• Media & Journalism colleges in Delhi
• Media & Journalism colleges in Bangalore
• Media & Journalism colleges in Mumbai
• List of Media & Journalism Colleges in India
• CA Intermediate
• CA Foundation
• CS Executive
• CS Professional
• Difference between CA and CS
• Difference between CA and CMA
• CA Full form
• CMA Full form
• CS Full form
• CA Salary In India

Top Courses & Careers

• Bachelor of Commerce (B.Com)
• Master of Commerce (M.Com)
• Company Secretary
• Cost Accountant
• Charted Accountant
• Credit Manager
• Financial Advisor
• Top Commerce Colleges in India
• Top Government Commerce Colleges in India
• Top Private Commerce Colleges in India
• Top M.Com Colleges in Mumbai
• Top B.Com Colleges in India
• IT Colleges in Tamil Nadu
• IT Colleges in Uttar Pradesh
• MCA Colleges in India
• BCA Colleges in India

Quick Links

• Information Technology Courses
• Programming Courses
• Web Development Courses
• Data Analytics Courses
• Big Data Analytics Courses
• RUHS Pharmacy Admission Test
• Top Pharmacy Colleges in India
• Pharmacy Colleges in Pune
• Pharmacy Colleges in Mumbai
• Colleges Accepting GPAT Score
• Pharmacy Colleges in Lucknow
• List of Pharmacy Colleges in Nagpur
• GPAT Result
• GPAT 2024 Admit Card
• GPAT Question Papers
• NCHMCT JEE 2024
• Mah BHMCT CET
• Top Hotel Management Colleges in Delhi
• Top Hotel Management Colleges in Hyderabad
• Top Hotel Management Colleges in Mumbai
• Top Hotel Management Colleges in Tamil Nadu
• Top Hotel Management Colleges in Maharashtra
• B.Sc Hotel Management
• Hotel Management
• Diploma in Hotel Management and Catering Technology

Diploma Colleges

• Top Diploma Colleges in Maharashtra
• UPSC IAS 2024
• SSC CGL 2024
• IBPS RRB 2024
• Previous Year Sample Papers
• Free Competition E-books
• Sarkari Result
• QnA- Get your doubts answered
• UPSC Previous Year Sample Papers
• CTET Previous Year Sample Papers
• SBI Clerk Previous Year Sample Papers
• NDA Previous Year Sample Papers

Upcoming Events

• NDA Application Form 2024
• UPSC IAS Application Form 2024
• CDS Application Form 2024
• CTET Admit card 2024
• HP TET Result 2023
• SSC GD Constable Admit Card 2024
• UPTET Notification 2024
• SBI Clerk Result 2024

Other Exams

• SSC CHSL 2024
• UP PCS 2024
• UGC NET 2024
• RRB NTPC 2024
• IBPS PO 2024
• IBPS Clerk 2024
• IBPS SO 2024
• Top University in USA
• Top University in Canada
• Top University in Ireland
• Top Universities in UK
• Top Universities in Australia
• Best MBA Colleges in Abroad
• Business Management Studies Colleges

Top Countries

• Study in USA
• Study in UK
• Study in Canada
• Study in Australia
• Study in Ireland
• Study in Germany
• Study in China
• Study in Europe

Student Visas

• Student Visa Canada
• Student Visa UK
• Student Visa USA
• Student Visa Australia
• Student Visa Germany
• Student Visa New Zealand
• Student Visa Ireland
• CUET PG 2024
• IGNOU B.Ed Admission 2024
• DU Admission 2024
• UP B.Ed JEE 2024
• LPU NEST 2024
• IIT JAM 2024
• IGNOU Online Admission 2024
• Universities in India
• Top Universities in India 2024
• Top Colleges in India
• Top Universities in Uttar Pradesh 2024
• Top Universities in Bihar
• Top Universities in Madhya Pradesh 2024
• Top Universities in Tamil Nadu 2024
• Central Universities in India
• CUET Exam City Intimation Slip 2024
• IGNOU Date Sheet
• CUET Mock Test 2024
• CUET Admit card 2024
• CUET PG Syllabus 2024
• CUET Participating Universities 2024
• CUET Previous Year Question Paper
• CUET Syllabus 2024 for Science Students
• E-Books and Sample Papers
• CUET Exam Pattern 2024
• CUET Exam Date 2024
• CUET Cut Off 2024
• CUET Exam Analysis 2024
• IGNOU Exam Form 2024
• CUET 2024 Exam Live
• CUET Answer Key 2024

Engineering Preparation

• Knockout JEE Main 2024
• Test Series JEE Main 2024
• JEE Main 2024 Rank Booster

Medical Preparation

• Knockout NEET 2024
• Test Series NEET 2024
• Rank Booster NEET 2024

Online Courses

• JEE Main One Month Course
• NEET One Month Course
• IBSAT Free Mock Tests
• IIT JEE Foundation Course
• Knockout BITSAT 2024
• Career Guidance Tool

Top Streams

• IT & Software Certification Courses
• Engineering and Architecture Certification Courses
• Programming And Development Certification Courses
• Business and Management Certification Courses
• Marketing Certification Courses
• Health and Fitness Certification Courses
• Design Certification Courses

Specializations

• Digital Marketing Certification Courses
• Cyber Security Certification Courses
• Artificial Intelligence Certification Courses
• Business Analytics Certification Courses
• Data Science Certification Courses
• Cloud Computing Certification Courses
• Machine Learning Certification Courses
• View All Certification Courses
• UG Degree Courses
• PG Degree Courses
• Short Term Courses
• Free Courses
• Online Degrees and Diplomas
• Compare Courses

Top Providers

• Coursera Courses
• Udemy Courses
• Edx Courses
• Swayam Courses
• upGrad Courses
• Simplilearn Courses
• Great Learning Courses

Smartphone Essay

A smartphone is a mobile gadget that makes it possible to combine cellular and mobile computing capabilities into a single device. People love to use smartphones because, compared to feature phones, smartphones offer more powerful hardware and robust mobile operating systems. Here are a few sample essays on ‘smartphones’.

100 Words Essay On Smartphone

Modern technological developments have made living simpler. Today, we can quickly call or video chat with anyone by moving our fingers when using a mobile phone. Mobile phones can be used for many different things today, including voice calling, video chatting, text messaging, multimedia messaging, internet browsing, email, video games, and photography. They come in a variety of sizes and shapes and with a variety of technical specifications. Thus, it is referred to as a "Smart Phone." Today you can see everyone's home with at least one smartphone. Even in rural areas where the rate of development isn't as high as that of a city, nearly every person can be seen using a smartphone.

200 Words Essay On Smartphone

With the help of mobile smartphones, life has become increasingly simple. When using a landline phone, calling someone was extremely difficult, expensive, and time-consuming while waiting for the call to connect to your loved ones.

However, a smartphone puts your loved ones within reach with a single click. No matter how far they are from you, whether they are living domestically or abroad, you may always talk to them whenever you want. You can call them via voice or even use the best smartphone technology, video calls. Through the video call feature on your smartphone, you can video chat with family members you can't see every day, but it's always excellent to see them every day.

Smartphones are now utilised for various things, including calling, video calling, texting, sending emails, playing games, and even taking good photos or selfies using the front and primary cameras.

Advantages Of Smartphone

Keeps us connected

Day-to-Day Communicating

Entertainment for All

Managing Office Work

Mobile Banking

Disadvantages Of Smartphones

Wasting Time

Making Us Non- communicable

Money Wastage

Loss of Privacy

Depending on how a user utilises a mobile phone, it could have both beneficial and harmful effects. We should use our mobile devices cautiously and follow best practices to live better, hassle-free lives rather than utilising them carelessly and turning them into life-threatening viruses.

500 Words Essay On Smartphone

In many aspects, smartphones are beneficial in our daily lives. The use of cell phones is essential to everyday life. The smartphone is an excellent improvement to our life. Our work has been made simpler and more convenient due to it. It has numerous uses, including a phone, camera, music player, and alarm clock. We can maintain contact with our friends and family members thanks to it.

Understanding The Smartphone

A smartphone is a mobile gadget that makes it possible to combine cellular and mobile computing capabilities into a single device. In addition, compared to feature phones, smartphones offer more powerful hardware and robust mobile operating systems. Smartphones' robust operating systems enable online browsing, software, and multimedia features. They also support standard phone features like voice calls and text messaging.

Importance Of Smartphones In Our Life

A smartphone is a mobile phone that provides more sophisticated networking and computational capabilities than a typical mobile phone. A touchscreen interface, an internet connection, and an operating system that can execute downloaded programmes are all standard features of a smartphone.

The Simon Personal Communicator, built in 1992, was the original smartphone. It incorporated functions from many devices, including a cell phone, pager, fax machine, and contact book. However, cell phones did not take off until the 2000s.

The ability to always be online is one of the key factors contributing to cell phones' immense popularity. This enables us to keep up with current events, check our email and social media accounts, and even conduct online shopping. We can communicate with our friends and family via smartphones. We can reach them whenever we want by phone, text, or video-chat.

Smartphone Use By Students

Students can benefit from smartphones. They can use them to look up material swiftly, take class notes, and maintain organisation. Nevertheless, there are several drawbacks to using a smartphone in class. A student may not be paying attention to the lecture and miss vital information if they are continually using their phone.

While some schools have outlawed cell phone use in the classroom, others have welcomed it and have even developed apps that can be used for learning. The best course of action for each school's children is up to them.

The excessive use of cell phones has some drawbacks. For instance, if we are always staring at our phones, we risk missing out on what is happening worldwide. We could also develop a phone addiction and use them excessively. This may result in issues like anxiousness and lack of sleep.

My Life Experiences

In my home, there are five smartphones, one for my father, one for my mother, one for my sister, one for my brother, and one for myself. We all use smartphones for personal work. Smartphones help us in many ways, depending on how we use them. A smartphone can affect us in both ways, negatively and positively, depending on how we use it. My father always told us to use mobile phones with a limit. We all have phone-free zones in our house where we are not allowed to use any devices and we spend that time bonding with each other or reading books.

Overall, smartphones have a lot of benefits and uses that make our life easier. However, to use them wisely, we should be aware of any potential drawbacks.

Applications for Admissions are open.

Aakash iACST Scholarship Test 2024

Get up to 90% scholarship on NEET, JEE & Foundation courses

ALLEN Digital Scholarship Admission Test (ADSAT)

Register FREE for ALLEN Digital Scholarship Admission Test (ADSAT)

JEE Main Important Physics formulas

As per latest 2024 syllabus. Physics formulas, equations, & laws of class 11 & 12th chapters

PW JEE Coaching

Enrol in PW Vidyapeeth center for JEE coaching

PW NEET Coaching

Enrol in PW Vidyapeeth center for NEET coaching

JEE Main Important Chemistry formulas

As per latest 2024 syllabus. Chemistry formulas, equations, & laws of class 11 & 12th chapters

Download Careers360 App's

Regular exam updates, QnA, Predictors, College Applications & E-books now on your Mobile

Certifications

We Appeared in

Have you recently bought a mobile phone? Write a review about the phone explaining why you bought it, how easy it is to use and if you would recommend it to others.

Unauthorized use and/or duplication of this material without express and written permission from this site’s author and/or owner is strictly prohibited. Excerpts and links may be used, provided that full and clear credit is given to Writing9 with appropriate and specific direction to the original content.

Fully explain your ideas

To get an excellent score in the IELTS Task 2 writing section, one of the easiest and most effective tips is structuring your writing in the most solid format. A great argument essay structure may be divided to four paragraphs, in which comprises of four sentences (excluding the conclusion paragraph, which comprises of three sentences).

For we to consider an essay structure a great one, it should be looking like this:

• Paragraph 1 - Introduction
• Sentence 1 - Background statement
• Sentence 2 - Detailed background statement
• Sentence 3 - Thesis
• Sentence 4 - Outline sentence
• Paragraph 2 - First supporting paragraph
• Sentence 1 - Topic sentence
• Sentence 2 - Example
• Sentence 3 - Discussion
• Sentence 4 - Conclusion
• Paragraph 3 - Second supporting paragraph
• Paragraph 4 - Conclusion
• Sentence 1 - Summary
• Sentence 2 - Restatement of thesis
• Sentence 3 - Prediction or recommendation

Our recommended essay structure above comprises of fifteen (15) sentences, which will make your essay approximately 250 to 275 words.

Discover more tips in The Ultimate Guide to Get a Target Band Score of 7+ » — a book that's free for 🚀 Premium users.

• Check your IELTS essay »
• Find essays with the same topic
• View collections of IELTS Writing Samples
• Show IELTS Writing Task 2 Topics

Some people say that in the modern world it is very difficult for people to have a healthy lifestyle. Others,however, say that it is easy for people to be healthy and fit if they want to be. Discuss both these views and give your opinion

Some people think that parents should teach their children how to be good members of society. others, however, believe that school is the best place to learn this. discuss both views and give your own opinion., nowadays many people complain that they have difficulties having enough sleep what are problems caused by lack of sleep what can be done about this lack of sleep, we always say that there is a generation gap and there always will be. what causes generation gap is there no way to strike a balance in the relations between two generations, some people believe that it is a good idea to share as much information as possible in scientific research, business and the academic world. others believe that some information is too important or too valuable to be shared freely. discuss both these views and give your own opinion..

The Best AR Smart Glasses of 2024

Project a private theater-sized screen or virtual workspace over your real-world surroundings.

Gear-obsessed editors choose every product we review. We may earn commission if you buy from a link. Why Trust Us?

Smart glasses are wearable technology that project digital content directly into your field of view. You look through them instead of at a screen—freeing up your hands to interact with AR objects in the environment. Apple’s $3,499 Vision Pro headset won’t be available until early next year and hasn’t quite achieved a lightweight glasses form factor ideal for everyday use. In the meantime display manufacturers like TCL and Xreal have already created affordable AR smart glasses that you can buy today. These sub $500 headsets can be used for everyday tasks like projecting a virtual private theater screen or multi-monitor setup for work. All of this squeezed into a gadget that slides into your pocket. I’ve spent the past month working, watching, and gaming with the best models you can buy right now. Here’s how they held up.

What To Look For

Display technology is the most important factor of smart glasses. You’d be better off sticking with a screen if you can’t make out text, if the image is too dim, or if the narrow field of view makes it too hard to focus on content. Modern smart glasses use birdbath optics, which position the picture downward then reflect the visuals close to the wearer’s eye for a rich, full HD projection that seems to appear several feet in front of you. Paired with micro OLED displays for each eye, this creates rich a vibrant image that frees you from the size limitations of physical screens and pops from your surroundings. If you choose to look outside of this guide you’ll want to make sure any alternatives have at least a 1080p resolution and 45 degree field of view.

Next, you want to ensure the fit is comfortable enough for extended use. An easy way to gauge this is to look for smart glasses with plenty of adjustability. Take note of different arm heights, the number of nose bridges included, and whether or not there is built-in diopter tuning for nearsightedness. A proper fit ensures there’s minimal (or no) blur and you won’t lose any corners of your content. Modern smart glasses have come a long way from bulky and nerdy prototypes like Google Glass. Take into account if you like the design to find a pair that fits with your sense of fashion.

XREAL Air AR Glasses

The Xreal (formerly Nreal) Air are the most feature-packed AR glasses available. In addition to projecting a a private 130-inch theater screen, they’re capable of running a full AR operating system on the go. This spatial computing interface is called Nebula. It is home to a suite of apps including Microsoft Office for work, virtual desktops for PC or Mac, and a variety of indie games in the AR Lab. The Air’s picture quality comes close to our top performer (the Rokid Max, below) but isn’t as smooth when gaming due to their lower 60-hertz refresh rate. Still, they excel in value as they are the most affordable pair with the most expansive feature set.

Rokid’s Max AR glasses project a large 215-inch screen six meters ahead of you. They lack the multitasking chops of the Xreal Nebula workspace and its AR ecosystem consists of basic web apps. However, they make up for it with the best big-screen mode in our test pool—they have the highest refresh rate and most vibrant picture.

There are plenty of customization options on the Rokid Max. Everything from the angle of the arms to a physical myopia correction dial on the top is adjustable. That makes it easier to share them with other people without them needing contacts or glasses. Other glasses require you to get prescription lenses fitted to your eyesight. Rokid’s headset is streamlined out of the box—simply attach the pieces to find the perfect fit, plug the Max into a USB-C device, and the screen docks into the center of your view from a distance. The experience is straightforward and instantly creates a sharp theater-sized picture. While ideal for gaming it also magnified my Mac screen, but this view isn’t as useful as the Xreal remote desktop. Its AR workspace looks much like the original iPhone’s layout down to the app icons but it feels a bit laggy and lacks the multitasking of the Airs above.

Where it clearly excels from the competition is in creating a massive, immersive virtual display. The latest blockbuster games like “Elden Ring” benefit from the silky smooth 120-hertz refresh rate. Motions like rolls and jumps feel more fluid than on the Xreal or TCL glasses (below). Aiming crosshairs in FPS games feels snappier, and the brighter 600 nits gives lush foliage and color variations in sand noticeably more pop. I was pleasantly surprised to find that the higher refresh rate had no discernible impact on the battery life of my Steam Deck or laptop (these devices did run slightly warmer after an hour or so). There was, however, noticeably less image ghosting of trails left behind on fast moving objects, like when driving vehicles at high speeds in racing games.

The glasses’ oval design casts the widest field of view to maximize immersion and block distractions from your peripheral vision. On nights when my partner wanted to watch the bedroom TV, I slid these on and played “The Legend of Zelda: Tears of the Kingdom” on a 200-inch screen while laying down. The screen shakes with your head so if you experience turbulence on a plane (I experienced a slight jitter on a bus and train) you will see the screen wobble a bit. Despite this, it still has superior image quality. Rokid’s Max is the best-looking virtual screen for games and movies for those who need the most immersive experience possible.

TCL NXTWEAR S RayNeo XR Glasses

Although the TCL NXTWEAR S smart glasses aren’t as vibrant, their comfort is unmatched thanks to their sleek finish which runs cool and TÜV Low Blue Light eye protection. These are perfect for binging movies or marathon gaming sessions. TCL nailed the basics in ways its rivals above don’t, so they’re much easier to use. For example, you don’t have to forcefully snap the front sunglass shade over the frame because there is a slick magnetic attachment system to instantly pop off or swap out blockers. Although it sounds minor, the inclusion of physical buttons makes it so much easier to adjust volume and brightness, or enter 3D movie mode, rather than gamble on a touch sensitive panel or multifunctional wheel. The NXTWEAR S is also the only pair to use a Pogo USB connector. Much like Apple’s MagSafe system, this wire disconnects easily to reduce wire strain and improve convenience when traveling.

While it’s picture is equally as sharp as the others at 1080p, the limited brightness and refresh rate is especially noticeable compared to the Rokid Max while gaming. I needed to pay more attention to spot the torches of distant enemies and better anticipate an enemy attack in “Elden Ring.” However I preferred watching movies on the TCL, as these are shot at under 30 FPS and the dimmer screen felt more lifelike with less strain on my eyes—an actor’s five o’clock shadow or pimple were just as sharp. TCL’s speakers created the best bass and most immersive 3D sound. You can even pair an additional device to them over Bluetooth so you can listen to your phone simultaneously.

Keep in mind that the glasses’ wayfarer design is narrower than the rounded shapes of the other two glasses, so you end up seeing more of your surroundings in your peripheral vision. I prefer this while I travel as it looks the most “normal” of the bunch (think a square pair of Ray-Bans) and is more compact. However, if you’re sitting in a busy room, this higher level of awareness can be distracting. These have the highest quality feel to them and are backed by the biggest of all three companies. But, if you want a multi-window virtual workspace or will primarily be using these at home, then the other two models fill more of your vision and offer computing capabilities that the NXTWEAR S lacks.

Hunter Fenollol, our resident expert of all things consumer tech, from smart home to VR gaming headsets, has years of knowledge creating product explainers, in-depth reviews, and buying guides to help you get the most from the latest electronics. Throughout college, he covered and reviewed the latest gadget releases for sites like Tom’s Guide, Laptop Magazine, and CNN Underscored. If he’s not elbow-deep in the latest hardware, you can find Hunter at one of Long Island’s many beaches, in Manhattan, or gambling away his paycheck. 

.css-cuqpxl:before{padding-right:0.3125rem;content:'//';display:inline;} Best Product Reviews of 2024 .css-xtujxj:before{padding-left:0.3125rem;content:'//';display:inline;}

Take Up to 41% Off Editor-Favorite Grills

The Best Memorial Day TV Sales to Shop Now

The 5 Best Water Storage Containers

The Best Bladeless Fans for 2024

Save 25% on Goal Zero Portable Power Stations

The Best Memorial Day Generator Sales 2024

The Best Air Conditioners We Tested and Recommend

The 7 Best Self-Propelled Lawn Mowers

The 7 Best Inflatable Standup Paddleboards of 2024

The Best Small Dehumidifiers for Your Home

The Best Electric Mowers of 2024 for Any Yard

The 8 Android phones to look forward to in 2024

2024 is shaping up to be an exciting year for Android smartphones of all kinds, be it foldable or a traditional candy bar. We’ve already seen high-end flagships announced left and right, such as the Samsung Galaxy S24 Ultra . Most new premium devices sport the latest Snapdragon 8 Gen 3 chipset, improved cameras, and enhanced hardware, making them more compelling.

Still, it’s worth remembering that we’re only three months in, and many more devices are yet to be announced later this year. Let's look at some of the most anticipated smartphones still to come this year and explorre what we expect to see and when.

Google Pixel 8a

Assuming Google keeps up its tradition of unveiling the Pixel A-lineup at Google I/O, we expect to see the new midranger announced on stage on May 14th . The latest rumors claim Google's next phone could sport a curvier design alongside a slightly revamped camera bar — both elements that would appear more in line with the recent Pixel flagships.

The device will reportedly come equipped with the Google Tensor G3 chip with 8GB of RAM, similar to the Pixel 7a. We’re not expecting to see any major changes, and even the camera hardware is expected to remain similar to the previous generation.

Google Pixel 8a: News, leaks, rumored price, and release window

That said, we’re still excited to see the new Pixel 8a, primarily due to its affordable — albeit less so this year — price tag, and excellent set of features it brings to the table. Last year’s Pixel 7a was one of the best camera smartphones in its category, and we look forward to seeing what Google has in store for us in 2024.

Motorola Razr+ (2024)

Motorola announced the Razr+ (also known as Motorola Razr 40 Ultra) in June 2023, and it’s safe to predict that we might see an upgraded model sometime in mid-2024.

The Motorola Razr Plus was a massive hit, marking a major shift in design, software, and several key improvements. The Razr Plus delivered an excellent software experience and brought a massive cover screen measuring 3.6 inches. While the information about the next generation is scarce, we expect key improvements to the hardware, further refining the experience and overall the hardware.

Motorola Razr+ review: My new favorite foldable

Motorola also unveiled a more affordable Motorola Razr (3rd gen, 2023), also known as Motorola Razr 40. This device was more affordable and equipped with the Snapdragon 7 Gen 1, bringing a foldable flip phone to the masses with prices as low as $500. In 2024, we’re expecting the same two models to continue taking on Samsung’s flip phone efforts, and while we don’t have any rumor to go by just yet, we’re excited to see what Motorola has come up with this year.

Samsung Galaxy Z Fold 6

Samsung is expected to unveil the Galaxy Z Fold 6 in July 2024, alongside the Galaxy Z Flip 6 and, potentially, new Galaxy Buds and Galaxy Watch smartwatches. There’s a chance we could also see the newly unveiled Galaxy Ring at the summer event.

According to the latest rumors, the new foldable flagship will feature a slightly wider cover and main display with a crease-less display, a slimmer design that’s more in line with recent flagships, such as the OnePlus Open.

There’s a lot unknown about the next foldable, and we’re still waiting to see and hear more news regarding the specifications and other hardware improvements. As things stand, the Fold 6 isn’t expected to come with a dedicated S Pen stylus slot, and there are mixed rumors about the device getting a new camera that’d make it more in line with devices such as the Galaxy S24 Ultra.

Galaxy Z Fold 6 leaks prove Samsung is stuck in a foldable rut

Speaking of the S24 Ultra, reports also suggest that Samsung could launch two Fold flagships, a standard Galaxy Z Fold 6, and a Galaxy Z Fold 6 Ultra – which could be equipped with even more features, and potentially house the S Pen slot and a more advanced camera array.

Samsung Galaxy Z Flip 6

The Samsung Galaxy Z Flip 6 will also be announced sometime in July 2024, right along side its bigger foldable brother. The latest rumors claim the Flip 6 could look a lot like last year’s Galaxy Z Flip 5, sporting a nearly identical design, and small improvements. The changes could include a crease-less display, a first on a Galaxy Z series foldable, a more powerful Snapdragon chip, and other small enhancements.

Samsung Galaxy Z Flip 6: News, leaks, rumored price, and release window

We haven’t heard much about the camera on the upcoming devices, and we’re yet to see whether Samsung has managed to fit larger sensors on the Flip 6 without sacrificing the device's design. We’ll have to wait for more leaks to come through, but as it stands, we could see a similar camera array and performance.

Google Pixel 9 and Google Pixel 9 Pro

Google usually holds its main flagship event in October, and we’re expecting the same tradition to continue in 2024. When it comes to the two flagships, early renders suggested Google to retain the already familiar design, with a slight revamp to the camera bar’s looks.

The Pixel 9 could keep its rounded and curvy design with flat edges, making it look similar to the recently seen Pixel 8a renders. The current leaks also suggest we could see either the same display size, or even larger panels on the Pixel 9 series. The new lineup would also benefit from an improved chipset, the Google Tensor G4, which could be a small bump over the current Tensor G3.

Google Pixel 9: News, leaks, rumored price, and release window

When it comes to the cameras, we’re expecting a ton of improvements and potentially even more AI magic to edit photos and videos directly on the device. There’s a chance we could also finally see the new Qi2 wireless charging standard come to the new devices, adding MagSafe-like magnets to the back of the phones, making wireless charging easier.

Google Pixel Fold 2

For the first time, Google is rumored to launch the next-generation Google Pixel Fold 2 alongside the Pixel 9 series flagship, sometime in October 2024. The new foldable is expected to sport a completely new design, featuring a narrower cover, main display, and a new camera bar.

The Pixel Fold 2 could be one of the thinnest foldable devices in North America, with its reportedly 155.2 x 150.2 x 5.27mm measurements when unfolded, making it slim, and more in line with other premium foldables such as the OnePlus Open. The move wouldn’t be too surprising, given that Google failed to convince developers that the original Fold’s landscape orientation was the way to move forward.

Many apps failed to properly support the orientation, and it looks like Google is giving in. Based on the rumors and early renders, the Fold 2 will follow a form factor that’s easier to operate with one hand and one that hopefully supports more apps on a full screen.

Google Pixel Fold 2: News, leaks, rumored price, and release window

There’s plenty we don’t know, such as the weight, the camera setup, and even the battery capacity. As things stand, we’re expecting to see the Fold 2 launch with the Google Tensor G4 chipset, a new design, and various software tweaks, and even the same 7-year support, mirrored from the Pixel 8 and the Pixel 9 lineup.

OnePlus Open 2

There’s not much we know about the OnePlus Open 2, and the device is likely a few months away. It’s difficult to say if the phone will pack any major changes. The changes we expect—and keep in mind this is all speculation—involve software features, camera sensors, and potentially improving durability.

The OnePlus Open was one of the best foldable smartphones in 2023, and it’s fair to assume that OnePlus will unveil a successor that’ll fix some of the device’s shortcomings. The device arrived with IPX4 ingress protection that was below the competition, and that’s one key area where we could see a small upgrade to IPX6, improving the water resistance, and potentially some form of dust protection.

OnePlus Open 2: News, leaks, rumored price, and release window

On the software side, OnePlus got everything right with the first-generation Open. We’re expecting small improvements and the addition of AI-enhanced features. When it comes to cameras, we could see Hasselblad-tuned sensors arrive on the Open 2, making it an even better competitor against the Galaxy Z Fold series and the Pixel Fold.

Although the OnePlus 13 is likely to be considered a 2025 smartphone, it's likely we'll get our first previews before the year is up. OnePlus traditionally holds two events: one in China — which we predict will take place at the very end of 2024 — and one in January, which we anticipate will take place in January 2025. Because the phone that’ll be unveiled in China is usually the same model that we see internationally, except for the software and pre-order bonuses, it's worth putting on this list.

Whatever the OnePlus 13 ends up being, it'll undoubtedly be an exciting new smartphone. While we’re still a long way away from hearing any concrete rumors, we expect the next flagship to come with Qualcomm's next-generation Snapdragon 8 Gen 4 chipset and various software and hardware upgrades. It’s too early to speculate, but we’re excited about what’s yet to come.

A busy year ahead of us

In addition to all of these smartphones, we're expecting everything from a new Pixel Watch to some surprise tablets that could act as rivals to Samsung's latest Galaxy Tab S9 lineup. No matter which you're most excited to see, one thing's for sure — we've got a busy year ahead of us.

The State of Development Journals 2024: Quality, Acceptance Rates, Review Times, and What’s New

David mckenzie.

This is the eighth in my annual series of efforts to put together data on development economics journals that is not otherwise publicly available or easy to access (see  2017 ,  2018 ,  2019 ,  2020 , 2021 , 2022 , 2023 for the previous editions). I once again thank all the journal editors and editorial staff who graciously shared their statistics with me.

Journal Quality

The most well-known metric of journal quality is its impact factor . The standard impact factor is the mean number of citations in the last year of papers published in the journal in the past 2 years, while the 5-year is the mean number of cites in the last year of papers published in the last 5. As noted in previous years, the distribution of citations are highly skewed, and while the mean number of citations differs across journals, there is substantial overlap in the distributions – most of the variation in citations is within, rather than across journals. We continue to see growth in these impact factors at many journals. The big news this year is that they have decided that you really don’t need three decimal places any more in the impact factors.  I compliment these stats with RePec’s journal rankings which take into account article downloads and abstract views in addition to citations. 

Table 3 then shows two additional metrics, taken from Scimago , which uses information from the Scopus database. The first is the SJR (SCImago Journal Rank), which is a prestige-weighted citation metric – which works like Google PageRank, giving more weight to citations in sources with a relatively high SJR. I’ve included some of the top general journals in economics for comparison. Scimago also provides an H-index which is the number of papers published by a journal  in any year that were cited at least h times in the reference year – so this captures how many papers continue to be influential but as a result, favors more established journals, and ones that publish more articles, that have a larger body of articles to draw upon. 

How many submissions are received, and what are the chances of getting accepted?

Table 4 shows the number of submissions received each year. See previous years posts for statistics before 2019. The total submissions in the 11 journals tracked is almost 10,000 papers (note I received no data from the Review of Development Economics this year so have excluded it).  Total submissions in these journals are up 7.7% over last year, although not quite at the 2020 peak.

At most journals the number of submissions has either leveled off or fallen since a peak in 2020-21. World Development had the largest 2020 peak when they had a special call for a variety of short papers on COVID-19, but perhaps the combination of people sending off lots of papers during the pandemic and then being a little slower to start new projects has halted the rapid growth somewhat.

·       The newish World Development Perspectives already received 532 submissions last year, more than many long established development journals.

·       The Review of Development Economics has seen very rapid growth in submissions. I only started collecting stats for it last year, but the editors note that in 2015 they received about 450 submissions, and this has now grown to more than 1,500 last year.

Table 5 shows the total number of papers published in each journal. 782 papers were published in 2023, so that’s a lot of development research (even though less than 1 in 10 of the submitted papers and down slightly on the 811 papers published in 2022). I’ve noted in previous years that some of the journals have been able to flexibly increase the number of articles published as their submission numbers have risen, reducing publication lags as well. 

The ratio of the number of papers published to those submitted is approximately the acceptance rate. Of course papers are often published in a different year from when they are submitted, and so journals calculate acceptance rates by trying to match up the timing. Each journal does this in somewhat different ways. Hence Economia-Lacea reports a 0% acceptance rate for 2023 since none of the papers submitted in 2023 have yet been accepted, although some are still under review.  Table 6 shows the acceptance rates at different journals as reported by these journals. Of course the number and quality of submissions varies across journals, and so comparing acceptance rates across journals does not tell you what the chances are of your particular paper getting accepted is at these different journals.  

How long does it take papers to get refereed?

In addition to wanting to publish in a high quality outlet, and having a decent chance of publication, authors also care a lot about how efficient the process is. Table 7 provides data on the review process (see the previous years’ posts for historic data). The first column shows the desk rejection rate, which averages 73%. Column 2 uses the desk rejection rates and acceptance rates to estimate the acceptance rate conditional on you making it past the desk rejection stage. On average, about one in three papers that gets sent to referees gets accepted, with this varying from 12% to 63% across journals.

The remaining columns give some numbers on how long it takes to get a first-round decision. The statistics “Unconditional on going to referees” includes all the desk rejections, which typically don’t take that many days. The average conditional on going to referees is in the 3-5 month range. The last two columns then show that at most journals, almost all papers have a decision within 6 months – so in my opinion, you should feel free to send an enquiry if your paper takes longer than that. 

Do revisions typically get sent back to the referees or handled by the editor?

Another factor that can make a big difference in how long it takes to publish a paper is whether editors send revised papers back to referees, or instead reads the response letter and revision themselves and just makes a decision on this basis. This is something that the AER and AEJ Applied have been trying to do more and more, with only 25% of revisions at the AEJ Applied going back to referees. In my own editing at WBE, I send fewer than 5% of revisions back to referees. This year I asked the different journals what their approaches were. Many do not systematically track this, but offered some approximations:

·       Journal of Development Economics: approximately 60% of revisions go back to referees, although 0% for the short papers (see below)

·       Development Policy Review: only 10% of revisions go back to referees.

·       Journal of Development Effectiveness: 7.7% were sent back to referees

·       Journal of Development Studies: not tracked, but less than 20% go back to referees

·       Journal of African Economies: 52% are sent back to referees

·       Economia: 70% go back to referees.

·       EDCC: does not track this, but first revisions are usually sent back to referees.

·       World Development, World Development Perspectives, WBRO, and WBER do not track this, and results may vary a lot by editor.

Updates on the JDE Short Paper and Registered Report Tracks

The Journal of Development Economics has two other categories of papers that differ from other development journals:

·       The short paper format has proved popular. There were 148 submissions in 2023 (about 8% of total submissions), and 21 short papers were accepted. These papers follow the model of AER Insights, ReStat, etc in which papers are either conditionally accepted or rejected, and so any revisions are minor and are not sent back to referees.

·       The JDE registered reports had 19 stage 1 acceptances in 2023, and 1 stage 2 acceptance, reflecting a lag from COVID when there were not many new submissions. They have a website jdepreresults.org which tracks the stage 1 and stage 2 registered reports, but some of the data was lost when transitioning the website, so if you have a registered report accepted that is not listed there, please let the journal know.

Other Development Journal News

Finally, I asked the journals if they had any other major news or changes to report. Here are what they wanted to share:

·       At EDCC, Prashant Bharadwaj has replaced Marcel Fafchamps as editor. Thanks to Marcel for 10 years at the helm. The journal is one of the few development journals with a submission fee ($50), but offers a fee waiver to referees who have submitted a timely report in the year prior to submission.

·       Other editorial changes are Ganeshan Wignaraja replacing Colin Kirkpatrick as co-editor at Development Policy Review, and Marie Gardner and Ashu Handa taking over from Manny Jimenez at the Journal of Development Effectiveness.

·       The Journal of Development Effectiveness notes they are implementing a set of actions to raise awareness about transparency, ethics and equity in research, and to address power imbalances among HIC-L&MIC research teams. The editors note they are particularly concerned with research involving primary data collection in an L&MIC where there is no author from an institution in that country. For articles submitted to JDEff that fall into this category, they will require the authors to complete a short author reflexivity statement that will be published along with the article. The statement will explain the contribution of each author per Taylor & Francis authorship criteria, which are consistent with the criteria established by the International Committee of Medical Journal Editors. Authors will be asked to explain why there is no contributing author from the study location, specifically, whether any team member based in the study location made a ’significant contribution to conception, study design, execution or acquisition of data,’ and if so, why they were not subsequently invited to review the manuscript and take responsibility for its contents. And for work involving randomized controlled trials or interviews with vulnerable groups, authors will also be asked to answer a set of questions about research ethics. Final manuscript acceptance and publication in JDEff will be based on the scientific quality of the work as well as an assessment of whether the work was conducted in an equitable, inclusive and ethical manner.

Finally, thanks again to all the editors for all the time and effort they devote to improving the quality and visibility of development research. As you can see, they have a lot to deal with!

Lead Economist, Development Research Group, World Bank

Join the Conversation

• Share on mail
• comments added

If this iPhone SE 4 leak is real, mid-range Android phones are doomed

If you buy through a BGR link, we may earn an affiliate commission, helping support our expert product labs.

The iPhone SE 4 is expected to launch in mid-2025. Reports have revealed many details of this device, but the latest leak shows that it could actually be almost too good to be true. We expect an all-new design, Face ID support, and a fair price.

Last week, The Information revealed that Apple plans to redesign the iPhone SE with a similar look to the iPhone 14. This will include Face ID support, a bigger display (from a 4.7-inch to a 6.1-inch version), OLED instead of LCD, and an improved processor. Currently, Apple uses the A15 Bionic on the iPhone SE 3. Other rumors support the addition of USB-C and even an Action Button to this phone, but even if these two features aren’t added, the most surprising detail about this phone would be its price.

According to leaker Revegnus on X , Apple plans to sell the iPhone SE 4 between $429 and $499. While the leaker expects the iPhone SE 4 to have a 10% price increase in the US, this could mean a 15% more expensive smartphone in other markets.

Tech. Entertainment. Science. Your inbox.

Sign up for the most interesting tech & entertainment news out there.

By signing up, I agree to the Terms of Use and have reviewed the Privacy Notice.

iPhone SE 4 Price Rumors: – The US launch price will either remain at $429 or see an increase of around 10%. – Even if the price increases, the maximum target is to stay within $499. https://t.co/fROQe2eabr — J. Reve (@Revegnus1) May 19, 2024

BGR will keep following the latest iPhone SE 4 rumors and leaks. Below, you can learn more about the upcoming iPhone 16.

This article talks about:

José is a Tech News Reporter at BGR. He has previously covered Apple and iPhone news for 9to5Mac, and was a producer and web editor for Latin America broadcaster TV Globo. He is based out of Brazil.

• Apple is developing a foldable iPhone with a self-healing display
• tvOS 18: Release date, features, Apple TV compatibility, more
• Leak claims iPhone 16 Pro Max will once again have exclusive camera features

ECOVACS DEEBOT X2 Combo Complete is the last vacuum you’ll ever buy

Sonos Roam 2 is here, but don’t bother upgrading from the original

The ultra-thin iPhone 17 Slim may turn out to be a bigger deal than we all think

Sonos Ace headphones are official: $450 for ANC, spatial audio, and 30-hour battery life

Latest news.

StreamSaver: Comcast introduces $15 bundle of Peacock, Netflix, and Apple TV+

Prepare to drop $22,000 off your paycheck if you want to stay working hybrid

Apple celebrates World Meditation Day with exclusive Apple Watch Award

Apple Watch Series 9 is missing a key feature from S8 – here’s why no one cares

Sign up for the most interesting tech & entertainment news out there.