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Open Access

Peer-reviewed

Research Article

Mobile phones: The effect of its presence on learning and memory

Roles Conceptualization, Data curation, Investigation, Writing – original draft

Affiliation Department of Psychology, Sunway University, Selangor, Malaysia

Roles Formal analysis, Investigation, Methodology, Resources, Supervision, Writing – original draft, Writing – review & editing

* E-mail: [email protected]

• Clarissa Theodora Tanil, 
• Min Hooi Yong

• Published: August 13, 2020
• https://doi.org/10.1371/journal.pone.0219233
• See the preprint
• Peer Review
• Reader Comments

Our aim was to examine the effect of a smartphone’s presence on learning and memory among undergraduates. A total of 119 undergraduates completed a memory task and the Smartphone Addiction Scale (SAS). As predicted, those without smartphones had higher recall accuracy compared to those with smartphones. Results showed a significant negative relationship between phone conscious thought, “how often did you think about your phone”, and memory recall but not for SAS and memory recall. Phone conscious thought significantly predicted memory accuracy. We found that the presence of a smartphone and high phone conscious thought affects one’s memory learning and recall, indicating the negative effect of a smartphone proximity to our learning and memory.

Citation: Tanil CT, Yong MH (2020) Mobile phones: The effect of its presence on learning and memory. PLoS ONE 15(8): e0219233. https://doi.org/10.1371/journal.pone.0219233

Editor: Barbara Dritschel, University of St Andrews, UNITED KINGDOM

Received: June 17, 2019; Accepted: July 30, 2020; Published: August 13, 2020

Copyright: © 2020 Tanil, Yong. This is an open access article distributed under the terms of the Creative Commons Attribution License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.

Data Availability: All relevant data are within the manuscript.

Funding: MHY received funding from Sunway University (GRTIN-RRO-104-2020 and INT-RRO-2018-49).

Competing interests: The authors have declared that no competing interests exist.

Introduction

Smartphones are a popular communication form worldwide in this century and likely to remain as such, especially among adolescents [ 1 ]. The phone has evolved from basic communicative functions–calls only–to being a computer-replacement device, used for web browsing, games, instant communication on social media platforms, and work-related productivity tools, e.g. word processing. Smartphones undoubtedly keep us connected; however, many individuals are now obsessed with them [ 2 , 3 ]. This obsession can lead to detrimental cognitive functions and mood/affective states, but these effects are still highly debated among researchers.

Altmann, Trafton, and Hambrick suggested that as little as a 3-second distraction (e.g. reaching for a cell phone) is adequate to disrupt attention while performing a cognitive task [ 4 ]. This distraction is disadvantageous to subsequent cognitive tasks, creating more errors as the distraction period increases, and this is particularly evident in classroom settings. While teachers and parents are for [ 5 ] or against cell phones in classrooms [ 6 ], empirical evidence showed that students who used their phones in class took fewer notes [ 7 ] and had poorer overall academic performance, compared to those who did not [ 8 , 9 ]. Students often multitask in classrooms and even more so with smartphones in hand. One study showed no significant difference in in-class test scores, regardless of whether they were using instant messaging [ 10 ]. However, texters took a significantly longer time to complete the in-class test, suggesting that texters required more cognitive effort in memory recall [ 10 ]. Other researchers have posited that simply the presence of a cell phone may have detrimental effects on learning and memory as well. Research has shown that a mobile phone left next to the participant while completing a task, is a powerful distractor even when not in use [ 11 , 12 ]. Their findings showed that mobile phone participants could perform similarly to control groups on simple versions of specific tasks (e.g. visual spatial search, digit cancellation), but performed much poorer in the demanding versions. In another study, researchers controlled for the location of the smartphone by taking the smartphones away from participants (low salience, LS), left the smartphone next to them (high salience/HS), or kept the smartphones in bags or pockets (control) [ 13 ]. Results showed that participants in LS condition performed significantly better compared to HS, while no difference was established between control and HS conditions. Taken together, these findings confirmed that the smartphone is a distractor even when not in use. Further, smartphone presence also increases cognitive load, because greater cognitive effort is required to inhibit distractions.

Reliance on smartphones has been linked to a form of psychological dependency, and this reliance has detrimental effect on our affective ‘mood’ states. For example, feelings of anxiety when one is separated from their smartphones can interfere with the ability to attend to information. Cheever et al. observed that heavy and moderate mobile phone users reported increased anxiety when their mobile phone was taken away as early as 10 minutes into the experiment [ 14 ]. They noted that high mobile phone usage was associated with higher risk of experiencing ‘nomophobia’ (no mobile phone phobia), a form of anxiety characterized by constantly thinking about one’s own mobile phones and the desire to stay in contact with the device [ 15 ]. Other studies reported similar separation-anxiety and other unpleasant thoughts in participants when their smartphones were taken away [ 16 ] or the usage was prohibited [ 17 , 18 ]. Participants also reported having frequent thoughts about their smartphones, despite their device being out of sight briefly (kept in bags or pockets), to the point of disrupting their task performance [ 13 ]. Taken together, these findings suggest that strong attachment towards a smartphone has immediate and lasting negative effects on mood and appears to induce anxiety.

Further, we need to consider the relationship between cognition and emotion to understand how frequent mobile phone use affects memory e.g. memory consolidation. Some empirical findings have shown that anxious individuals have attentional biases toward threats and that these biases affect memory consolidation [ 19 , 20 ]. Further, emotion-cognition interaction affects efficiency of specific cognitive functions, and that one’s affective state may enhance or hinder these functions rapidly, flexibly, and reversibly [ 21 ]. Studies have shown that positive affect improves visuospatial attention [ 22 ], sustained attention [ 23 ], and working memory [ 24 ]. The researchers attributed positive affect in participants’ improved controlled cognitive processing and less inhibitory control. On the other hand, participants’ negative affect had fewer spatial working memory errors [ 23 ] and higher cognitive failures [ 25 ]. Yet, in all of these studies–the direction of modulation, intensity, valence of experiencing a specific affective state ranged widely and primarily driven by external stimuli (i.e. participants affective states were induced from watching videos), which may not have the same motivational effect generated internally.

Present study

Prior studies have demonstrated the detrimental effects of one’s smartphone on cognitive function (e.g. working memory [ 13 ], visual spatial search [ 12 ], attention [ 11 ]), and decreased cognitive ability with increasing attachment to one’s phone [ 14 , 16 , 26 ]. Further, past studies have demonstrated the effect of affective state on cognitive performance [ 19 , 20 , 22 – 25 , 27 ]. To our knowledge, no study has investigated the effect of positive or negative affective states resulting from smartphone separation on memory recall accuracy. One study showed that participants reporting an increased level of anxiety as early as 10 minutes [ 14 ]. We also do not know the extent of smartphone addiction and phone conscious thought effects on memory recall accuracy. One in every four young adults is reported to have problematic smartphone use and this is accompanied by poor mental health e.g. higher anxiety, stress, depression [ 28 ]. One report showed that young adults reached for their phones 86 times in a day on average compared to 47 times in other age groups [ 29 ]. Young adults also reported that they “definitely” or “probably” used their phone too much, suggesting that they recognised their problematic smartphone use.

We had two main aims in this study. First, we replicated [ 13 ] to determine whether ‘phone absent’ (LS) participants had higher memory accuracy compared to the ‘phone present’ (HS). Second, we predicted that participants with higher smartphone addiction scores (SAS) and higher phone conscious thought were more likely to have lower memory accuracy. With regards to separation from their smartphone, we hypothesised that LS participants will experience an increase of negative affect or a decrease in positive affect and that this will affect memory recall negatively. We will also examine whether these predictor variables–smartphone addiction, phone conscious thought and affect differences—predict memory accuracy.

Materials and methods

Participants.

A total of 119 undergraduate students (61 females, M age = 20.67 years, SD age = 2.44) were recruited from a private university in an Asian capital city. To qualify for this study, the participant must own a smartphone and does not have any visual or auditory deficiencies. Using G*Power v. 3.1.9.2 [ 30 ], we require at least 76 participants with an effect size of d = .65, α = .05 and power of (1-β) = .8 based on Thornton et al.’s [ 11 ] study, or 128 participants from Ward’s study [ 13 ].

Out of 119 participants, 43.7% reported using their smartphone mostly for social networking, followed by communication (31.1%) and entertainment (17.6%) (see Table 1 for full details on smartphone usage). Participants reported an average smartphone use of 8.16 hours in a day ( SD = 4.05). There was no significant difference between daily smartphone use for participants in the high salience (HS) and low salience groups (LS), t (117) = 1.42, p = .16, Cohen’s d = .26. Female participants spent more time using their smartphones over a 24-hour period ( M = 9.02, SD = 4.10) compared to males, ( M = 7.26, SD = 3.82), t (117) = 2.42, p = .02, Cohen’s d = .44.

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https://doi.org/10.1371/journal.pone.0219233.t001

Ethical approval and informed consent

The study was conducted in accordance with the protocol approved by the Department of Psychology Research Ethics Committee at Sunway University (approval code: 20171090). All participants provided written consent before commencing the study and were not compensated for their participation in the study.

Study design

Our experimental study was a mixed design, with smartphone presence (present vs absent) as a between-subjects factor, and memory task as a within-subjects factor. Participants who had their smartphone out of sight formed the ‘Absent’ or low-phone salience (LS) condition, and the other group had their smartphone placed next to them throughout the study, ‘Present’ or high-phone salience (HS) condition. The dependent variable was recall accuracy from the memory test.

Working memory span test.

A computerized memory span task ‘Operation Span (OS)’ retrieved from software Wadsworth CogLab 2.0 was used to assess working memory [ 31 ]. A working memory span test was chosen as a measure to test participants’ memory ability for two reasons. First, participants were required to learn and memorize three types of stimuli thus making this task complex. Second, the duration of task completion took approximately 20 minutes. This was advantageous because we wanted to increase separation-anxiety [ 16 ] as well as having the most pronounced effect on learning and memory without the presence of their smartphone [ 9 ].

The test comprised of three stimulus types, namely words (long words such as computer, refrigerator and short words like pen, cup), letters (similar sound E, P, B, and non-similar sound D, H, L) and digits (1 to 9). The test began by showing a sequence of items on the left side of the screen, with each item presented for one second. After that, participants were required to recall the stimulus from a 9-button box located on the right side of the screen. In order to respond correctly, participants were required to click on the buttons for the items in the corresponding order they were presented. A correct response increases the length of stimulus presented by one item (for each stimulus category), while an incorrect response decreases the length of the stimulus by one item. Each trial began with five stimuli and increased or decreased depending on the participants’ performance. The minimum length possible was one while the maximum was ten. Each test comprised of 25 trials with no time limit and without breaks between trials. Working memory ability was measured through the number of correct responses over total trials: scores ranged from 0 to 25, with the highest score representing superior working memory.

Positive and Negative Affect Scale (PANAS).

We used PANAS to assess the current mood/affective state of the participants with state/feeling-descriptive statements [ 32 ]. PANAS has ten PA statements e.g. interested, enthusiastic, proud, and ten NA statements e.g. guilty, nervous, hostile. Each statement was measured using a five-point Likert scale ranging from very slightly or not at all to extremely, and then totalled to form overall PA or NA score with higher scores representing higher levels of PA or NA. In the current study, the internal reliability of PANAS was good with a Cronbach’s alpha coefficient of .819, and .874 for PA and NA respectively.

Smartphone Addiction Scale (SAS)

SAS is a 33-item self-report scale used to examine participants’ smartphone addiction [ 33 ]. SAS contained six sub-factors; daily-life disturbance that measures the extent to which mobile phone use impairs one’s activities during everyday tasks (5 statements), positive anticipation to describe the excitement of using phone and de-stressing with the use of mobile phone (8 statements), withdrawal refers to the feeling of anxiety when separated from one’s mobile phone (6 statements), cyberspace-oriented relationship refers to one’s opinion on online friendship (7 statements), overuse measures the excessive use of mobile phone to the extent that they have become inseparable from their device (4 statements), and tolerance points to the cognitive effort to control the usage of one’s smartphone (3 statements). Each statement was measured using a six-point Likert scale from strongly disagree to strongly agree, and total SAS was identified by totalling all 33 statements. Higher SAS scores represented higher degrees of compulsive smartphone use. In the present study, the internal reliability of SAS was identified with Cronbach's alpha correlation coefficient of .918.

Phone conscious thought and perceived effect on learning

We included a one-item question for phone conscious thought: “During the memory test how often do you think of your smartphone?”. The aim of this question was two-fold; first was to capture endogenous interruption experienced by the separation, and second to complement the smartphone addiction to reflect current immediate experience. Participants rated this item on a scale of one (none to hardly) to seven (all the time). We also included a one-item question on how much they perceived their smartphone use has affected their learning and attention: “In general, how much do you think your smartphone affects your learning performance and attention span?”. This item was similarly rated on a scale of one (not at all) to seven (very much).

We randomly assigned participants to one of two conditions: low-phone salience (LS) and high-phone salience (HS). Participants were tested in groups of three to six people in a university computer laboratory and seated two seats apart from each other to prevent communication. Each group was assigned to the same experimental condition to ensure similar environmental conditions. Participants in the HS condition were asked to place their smartphone on the left side of the table with the screen facing down. LS participants were asked to hand their smartphone to the researcher at the start of the study and the smartphones were kept on the researcher’s table throughout the task at a distance between 50cm to 300cm from the participants depending on their seat location, and located out of sight behind a small panel on the table.

At the start of the experiment, participants were briefed on the rules in the experimental lab, such as no talking and no smartphone use (for HS only). Participants were also instructed to silence their smartphones. They filled in the consent form and demographic form before completing the PANAS questionnaire. They were then directed to CogLab software and began the working memory test. Upon completion, participants were asked to complete the PANAS again followed by the SAS, phone conscious thought, and their perception of their phone use on their learning performance and attention span. The researcher thanked the participants and returned the smartphones (LS condition only) at the end of the task.

Statistical analysis

We examined for normality in our data using the Shapiro-Wilk results and visual inspection of the histogram. For the normally distributed data, we analysed our data using independent-sample t -test for comparison between groups (HS or LS), paired-sample t test for within groups (e.g. before and after phone separation), and Pearson r for correlation. Non-normally distributed or ranked data were analysed using Spearman rho for correlation.

Preliminary analyses

Our female participants reported using their smartphone significantly longer than males, and so we examined the effects of gender on memory recall accuracy. We found no significant difference between males and females on memory recall accuracy, t (117) = .18, p = .86, Cohen’s d = .03. Subsequently, data were collapsed, analysed and reported on in the aggregate.

Smartphone presence and memory recall accuracy

An independent-sample t- test was used to examine whether participants’ performance on a working memory task was influenced by the presence (HS) or absence (LS) of their smartphone. Results showed that participants in the LS condition had higher accuracy ( M = 14.21, SD = 2.61) compared to HS ( M = 13.08, SD = 2.53), t (117) = 2.38, p = .02, Cohen’s d = .44 (see Fig 1 ). The effect size ᶇ 2 = .44 indicates that smartphone presence/salience has a moderate effect on participant working memory ability and a sensitivity power of .66.

https://doi.org/10.1371/journal.pone.0219233.g001

Relationship between Smartphone Addiction Score (SAS), higher phone conscious thought and memory recall accuracy

Sas and memory recal..

We first examined participants’ SAS scores between the two conditions. Results showed no significant difference between the LS (M = 104.64, SD = 24.86) and HS (M = 102.70, SD = 20.45) SAS scores, t (117) = .46, p = .64, Cohen’s d = .09. We predicted that those with higher SAS scores will have lower memory accuracy, and thus we examined the relationship between SAS and memory recall accuracy using Pearson correlation coefficient. Results showed that there was no significant relationship between SAS and memory recall accuracy, r = -.03, n = 119, p = .76. We also examined the SAS scores between the LS and HS groups on memory recall accuracy scores. In the LS group, no significant relationship was established between SAS score and memory accuracy, r = -.04, n = 58, p = .74. Similarly, there was no significant relationship between SAS score and memory accuracy in the HS group, r = .10, n = 61, p = .47. In the event that one SAS subscale may have a larger impact, we examined the relationship between each subscale and memory recall accuracy. Results showed no significant relationship between each sub-factor of SAS scores and memory accuracy, all p s > .12 (see Table 2 ).

https://doi.org/10.1371/journal.pone.0219233.t002

Phone conscious thought and memory accuracy.

We found a significant negative relationship between phone conscious thought and memory recall accuracy, r S = -.25, n = 119, p = .01. We anticipated a higher phone conscious thought for the LS group since their phone was kept away from them during the task and examined the relationship for each condition. Results showed a significant negative relationship between phone conscious thought and memory accuracy in the HS condition, r S = -.49, n = 61, p = < .001, as well as the LS condition, r S = -.27, n = 58, p = .04.

Affect/mood changes after being separated from their phone

We anticipated that our participants may have experienced either an increase in negative affect (NA) or a decrease in positive affect (PA) after being separated from their phone (LS condition).

We first computed the mean difference (After minus Before) for both positive ‘PA difference’ and negative affect ‘NA difference’. A repeated-measures 2 (Mood change: PA difference, NA difference) x 2 (Conditions: LS, HS) ANOVA was conducted to determine whether there is an interaction between mood change and condition. There was no interaction effect of mood change and condition, F (1, 117) = .38, p = .54, n p 2 = .003. There was a significant effect of Mood change, F (1, 117) = 13.01, p < .001, n p 2 = .10 (see Fig 2 ).

https://doi.org/10.1371/journal.pone.0219233.g002

Subsequent post-hoc analyses showed a significant decrease in participants’ positive affect before ( M = 31.12, SD = 5.79) and after ( M = 29.36, SD = 6.58) completing the memory task in the LS participants, t (57) = 2.48, p = .02, Cohen’s d = .28 but not for the negative affect, Cohen’s d = .07. A similar outcome was also shown in the HS condition, in which there was a significant decrease in positive affect only, t (60) = 3.45, p = .001, Cohen’s d = .37 (see Fig 2 ).

PA/NA difference on memory accuracy.

We predicted that LS participants will experience either an increase in NA and/or a decrease in PA since their smartphones were taken away and that this will affect memory recall negatively. Results showed that LS participants who experienced a higher NA difference had poorer memory recall accuracy ( r s = -.394, p = .002). We found no significant relationship between NA difference and memory recall accuracy for HS participants ( r s = -.057, p = .663, n = 61) and no significant relationship for PA difference in both HS ( r s = .217, p = .093) and LS conditions ( r s = .063, p = .638).

Relationship between phone conscious thought, smartphone addiction scale and mood changes to memory recall accuracy

Preliminary analyses were conducted to ensure no violation of the assumptions of normality, linearity, multicollinearity and homoscedasticity. There was a significant positive relationship between SAS scores and phone conscious thought, r S = .25, n = 119, p = .007. Using the enter method, we found that phone conscious thought explained by the model as a whole was 19.9%, R 2 = .20, R 2 Adjusted = .17, F (4, 114) = 7.10, p < .001. Phone conscious thought significantly predicted memory recall accuracy, b = -.63, t (114) = 4.76, p < .001, but not for the SAS score, b = .02, t (114) = 1.72, p = .09, PA difference score, b = .05, t (114) = 1.29, p = .20, and NA difference score, b = .06, t (114) = 1.61, p = .11.

Perception between phone usage and learning

For the participants’ perception of their phone usage on their learning and attention span, we found no significant difference between LS ( M = 4.22, SD = 1.58) and HS participants ( M = 4.07, SD = 1.62), t (117) = .54, p = .59, Cohen’s d = .09. There was also no significant correlation between perceived cognitive interference and memory accuracy, r = .07, p = .47.

We aimed [ 1 ] to examine the effect of smartphone presence on memory recall accuracy and [ 2 ] to investigate the relationship between affective states, phone conscious thought, and smartphone addiction to memory recall accuracy. For the former, our results were consistent with prior studies [ 11 – 13 ] in that participants had lower accuracy when their smartphone was next to them (HS) and higher accuracy when separated from their smartphones (LS). For the latter, we predicted that the short-term separation from their smartphone would evoke some anxiety, identified by either lower PA or higher NA post-test. Our results showed that both groups had experienced a decrease in PA post-test, suggesting that the reduced PA is likely to have stemmed from the prohibited usage (HS) and/or separation from their phone (LS). Our results also showed lower memory recall in the LS group who experienced higher NA providing some evidence that separation from their smartphone does contribute to feelings of anxiety. This is consistent with past studies in which participants reported increased anxiety over time when separated from their phones [ 14 ], or when smartphone usage was prohibited [ 17 ].

We also examined another variable–phone conscious thought–described in past studies [ 11 , 13 ], as a measure of smartphone addiction. Our findings showed that phone conscious thought is negatively correlated to memory recall in both HS and LS groups, and uniquely contributed 19.9% in our regression model. We propose that phone conscious thought is more relevant and meaningful compared to SAS as a measure of smartphone addiction [ 15 ] because unlike the SAS, this question can capture endogenous interruptions from their smartphone behaviour and participants were to simply report their behaviour within the last hour. The SAS is better suited to describe problematic smartphone use as the statements described behaviours over a longer duration. Further, SAS statements included some judgmental terms such as fretful, irritated, and this might have influenced participants’ ability in recalling such behaviour. We did not find any support for high smartphone addiction to low memory recall accuracy. Our participants in both HS and LS groups had similar high SAS scores, and they were similar to Kwon et al. [ 33 ] study, providing further evidence that smartphone addiction is relatively high in the student population compared to other categories such as employees, professionals, unemployed. Our participants’ high SAS scores and primary use of the smartphone was for social media signals potential problematic users [ 34 ]. Students’ usage of social networking (SNS) is common and the fear of missing out (FOMO) may fuel the SNS addiction [ 35 ]. Frequent checks on social media is an indication of lower levels of self-control and may indicate a need for belonging.

Our results for the presence of a smartphone and frequent phone conscious thought on memory recall is likely due to participants’ cognitive load ‘bandwidth effect’ that contributed to poor memory recall rather than a failure in their memory processes. Past studies have shown that participants with smartphones could generally perform simple cognitive tasks as well as those without, suggesting that memory failure in participants themselves to be an unlikely reason [ 1 , 3 , 5 ]. Due to our study design, we are unable to tease apart whether the presence of the smartphone had interfered with encoding, consolidation, or recall stage in our participants. This is certainly something of consideration for future studies to determine which aspects of memory processes are more susceptible to smartphone presence.

There are several limitations in our study. First, we did not ask the phone conscious thought at specific time points during the study. Having done so might have determined whether such thoughts impaired encoding, consolidating, or retrieval. Second, we did not include the simple version of this task as a comparison to rule out possible confounds within the sample. We did maintain similar external stimuli in their environment during testing, e.g. all participants were in one specific condition, lab temperature, lab noise, and thereby ruling out possible external factors that may have interfered with their memory processes. Third, the OS task itself. This task is complex and unfamiliar, which may have caused some disadvantages to some participants. However, the advantage of an unfamiliar task requires more cognitive effort to learn and progress and therefore demonstrates the limited cognitive load capacity in our brain, and whether such limitation is easily affected by the presence of a smartphone. Future studies could consider allowing participants to use their smartphone in both conditions and including eye-tracking measures to determine their smartphone attachment behaviour.

Implications

Future studies should look into the online learning environment. Students are often users of multiple electronic devices and are expected to use their devices frequently to learn various learning materials. Because students frequently use their smartphones for social media and communication during lessons [ 34 , 36 ], the online learning environment becomes far more challenging compared to a face-to-face environment. It is highly unlikely that we can ban smartphones despite evidence showing that students performed poorer academically with their smartphones presented next to them. The challenge is then to engage students to remain focused on their lessons while minimising other content. Some online platforms (e.g. Kahoot and Mentimeter) create a fun interactive experience to which students complete tasks on their smartphones and allow the instructor to monitor their performance from a computer. Another example is to use Twitter as a classroom tool [ 37 ].

The ubiquitous nature of the smartphone in our lives also meant that our young graduates are constantly connected to their smartphones and very likely to be on SNS even at work. Our findings showed that the most frequently used feature was the SNS sites e.g. Instagram, Facebook, and Twitter. Being frequently on SNS sites may be a challenge in the workforce because these young adults need to maintain barriers between professional and social lives. Young adults claim that SNS can be productive at work [ 38 ], but many advise to avoid crossing boundaries between professional and social lives [ 39 , 40 ]. Perhaps a more useful approach is to recognise a good balance when using SNS to meet both social and professional demands for the young workforce.

In conclusion, the presence of the smartphone and frequent thoughts of their smartphone significantly affected memory recall accuracy, demonstrating that they contributed to an increase in cognitive load ‘bandwidth effect’ interrupting participants’ memory processes. Our initial hypothesis that experiencing higher NA or lower PA would have reduced their memory recall was not supported, suggesting that other factors not examined in this study may have influenced our participants’ affective states. With the rapid rise in the e-learning environment and increasing smartphone ownership, smartphones will continue to be present in the classroom and work environment. It is important that we manage or integrate the smartphones into the classroom but will remain a contentious issue between instructors and students.

Acknowledgments

We would like to thank our participants for volunteering to participate in this study, and comments on earlier drafts by Louisa Lawrie and Su Woan Wo. We would also like to thank one anonymous reviewer for commenting on the drafts.

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The Smartphone as a Pacifying Technology

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Shiri Melumad, Michel Tuan Pham, The Smartphone as a Pacifying Technology, Journal of Consumer Research , Volume 47, Issue 2, August 2020, Pages 237–255, https://doi.org/10.1093/jcr/ucaa005

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In light of consumers’ growing dependence on their smartphones, this article investigates the nature of the relationship that consumers form with their smartphone and its underlying mechanisms. We propose that in addition to obvious functional benefits, consumers in fact derive emotional benefits from their smartphone—in particular, feelings of psychological comfort and, if needed, actual stress relief. In other words, in a sense, smartphones are not unlike adult pacifiers. This psychological comfort arises from a unique combination of properties that turn smartphones into a reassuring presence for their owners: the portability of the device, its personal nature, the subjective sense of privacy experienced while on the device, and the haptic gratification it affords. Results from one large-scale field study and three laboratory experiments support the proposed underlying mechanisms and document downstream consequences of the psychological comfort that smartphones provide. The findings show, for example, that (a) in moments of stress, consumers exhibit a greater tendency to seek out their smartphone (study 2); and (b) engaging with one’s smartphone provides greater stress relief than engaging in the same activity with a comparable device such as one’s laptop (study 3) or a similar smartphone belonging to someone else (study 4).

Arguably, no recent technological innovation has had a more transformative effect on consumers’ lives than the virtually indispensable smartphone. Eighty-one percent of adult Americans own the device ( Pew Research Center 2019 ), with one-third of all consumer purchases—over $1 trillion—now occurring on mobile platforms ( Wu 2018 ). Virtually everywhere, whether on public transit, at dinner, in bed, or even while crossing the street, consumers can be found engrossed in their devices, calling or texting friends, listening to music, or viewing the latest content posted on social media. Indeed, the extent of smartphone usage has become so immense that one-half of owners describe their device as something that they “could not live without” ( Perrin 2017 ).

In spite of the central role that these devices play in the consumption economy, one question has received surprisingly little attention in consumer research: What is the nature of consumers’ relationship to their smartphone? The purpose of this article is to explore this issue by shedding new light on the characteristics and underpinnings of this relationship. Drawing on results from a large field study and three controlled laboratory experiments, we offer evidence that consumers are drawn to their smartphones not just because of the immense array of practical benefits they provide, but also because of a deeper emotional benefit: smartphones can serve as a source of psychological comfort for their owners. In a sense, one’s smartphone is not unlike an adult pacifier.

Consistent with this general proposition, we show that consumers are especially drawn to their smartphone in moments of stress, and that once engaged with, smartphones are sufficiently comforting to alleviate the stress. Moreover, this effect is specific to feelings of comfort in particular—not just any type of positive affect. We also offer findings that shed light on the drivers of this relationship, showing that smartphones are particularly comforting because of a unique combination of properties: (a) they are highly personal objects; (b) they are highly portable; (c) they provide a private space where users can escape their external environment; (d) they possess haptic properties that consumers find pleasurable—all of which allow phones to (e) provide a reassuring presence for owners. The sense of reassurance afforded by one’s phone, in turn, enables the device to act as a general source of psychological comfort.

We divide our presentation into four sections. We begin by reviewing prior work on which we base our predictions and propose a theoretical account of how the unique combination of physical and functional properties available on smartphones allows the device to serve as a source of psychological comfort for owners. We then report the results of four studies that test our hypotheses. We conclude by discussing the implications of our findings for consumer welfare, marketers, and the broader study of consumer product attachment.

How Do We Relate to Our Smartphones?

In recent years, an emerging body of academic literature—and much popular press—has discussed the relationship that people seem to develop with their smartphone ( Alter 2017 ; Fullwood et al. 2017 ; Melumad, Inman, and Pham 2019 ; Wilmer, Sherman, and Chein 2017 ). Perhaps the most common account of this relationship is that it resembles a behavioral addiction ( Alter 2017 ; Bernroider, Krumay, and Margiol 2014 ; De-Sola Gutiérrez, Rodríguez de Fonseca, and Rubio 2016 ; Grant et al. 2010 ; Roberts, Pullig, and Manolis 2015 )—a compulsive desire to engage in a behavior despite the risks of social, physical, or financial harm that it might impose ( Albrecht, Kirschner, and Grüsser 2007 ). As an illustration of this, prior work shows that respondents report a variety of problematic behaviors with their smartphone, such as use of the device that hinders productivity (e.g., using one’s phone at work), the degradation of interpersonal interactions (e.g., using one’s phone at dinner with a friend), or a generally unsafe style of usage (e.g., texting while driving; Bianchi and Phillips 2005 ; Vahedi and Saiphoo 2017 ; Yen et al. 2009 ). Relatedly, in one of the only studies of smartphone use in consumer research, Ward et al. (2017) found that participants restricted from their smartphones experienced cognitive load and consequently demonstrated impaired performance on a cognitive task. Likewise, research outside marketing consistently shows that people experience heightened anxiety and stress when restricted from interacting with their phones ( Cheever et al. 2014 ; Clayton, Leshner, and Almond 2015 ; Hunter et al. 2018 ; Panova and Lleras 2016 ).

While research on cellphone addiction has been useful in documenting the apparent dependency of some consumers on the device, it is important to note that excessive smartphone use is not recognized as a clinical form of addiction according to the Diagnostic and Statistical Manual for Mental Disorders (DSM-5) , and debate exists over whether it could be clinically characterized as such ( Panova and Carbonell 2018 ). More importantly, while extant research describes the class of behaviors some users demonstrate with their phone, it is relatively silent on the psychological mechanisms that give rise to this dependence. To the degree that such origins have been explored, the focus has been to examine covariation between personality types and reliance on specific functional applications of the devices—such as how extraversion versus introversion relates to users’ dependence on text messaging ( Bianchi and Phillips 2005 ; Igarashi et al. 2008 ), social media ( van Koningsbruggen et al. 2017 ), and gaming ( Cole and Hooley 2013 ). In fact, an important feature of this work is the assumption that there is nothing unique about smartphones per se that make them addictive; rather, the so-called addiction is thought to arise from the specific functionalities the phones provide (e.g., access to social media), not the device itself. Hence, in principle, this account would predict that the same “addictive” needs could be satisfied by any device offering similar functionalities—whether it be a laptop, tablet, or smartphone—and regardless of whether one owns the device or it belongs to someone else.

While functionalities undoubtedly play an important role in explaining why consumers form attachments to their smartphone, in this research we argue that there is a deeper psychological explanation for this special relationship. We propose that consumers are drawn to their phone because it offers a unique combination of functional, haptic, and personal ownership benefits that allow the device to serve as a source of psychological comfort. Thus, while the very notion of “smartphone addiction” frames the relationship that people have with their phone in an exclusively negative light, here we argue that consumers also derive emotional and psychological benefits from use of their device.

Smartphones as a Source of Psychological Comfort

The idea that individuals can develop deep emotional bonds with material objects has a long tradition in both consumer research and psychology ( Ball and Tasaki 1992 ; Belk 1988 ; Nedelisky and Steele 2009 ; Schifferstein and Zwartkruis-Pelgrim 2008 ). For example, young children often develop ties to transitional or attachment objects, such as blankets and teddy bears, that give them comfort in moments of stress and produce separation anxiety when unavailable ( Passman 1977 ; Winnicott 1953 ). For children, these ties are presumed to have a developmental function, allowing the child to transition away from the comfort and security of the primary caregiver ( Bowlby 1969 ; Winnicott 1953 ). While the developmental drivers that give rise to childhood attachment objects are typically outgrown by early adolescence, adults too can develop emotional attachments to material objects that have similar behavioral earmarks as children’s attachments to transitional objects ( Bachar et al. 1998 ; Keefer, Landau, and Sullivan 2014 ). For example, prior work shows that most adults report having “special possessions” that are both highly cared for and provide feelings of warmth and security ( Schultz, Kleine, and Kernan 1989 ; Wallendorf and Arnould 1988 ).

Notably, even if the analogy is only paramorphic (see, e.g., Hoffman 1960 , for a discussion of the distinction between isomorphic and paramorphic representations of psychological processes), consumers’ smartphones possess properties that are parallel to those that characterize attachment objects for children. For one, much like a child’s attachment object is small and lightweight enough to be carried around for use across various contexts ( Lehman et al. 1992 ; Winnicott 1953 ), a smartphone is highly portable, enabling the owner to access its benefits virtually always. Attachment objects also tend to have a tactile quality, with their benefits primarily derived through physical touch—such as a child self-soothing by gripping and stroking a teddy bear ( Busch et al. 1973 ; Lehman et al. 1992 ). Similarly, most smartphones are ergonomically designed to enhance and facilitate the user’s tactile experience with the device ( Aquino 2016 ), and consumers must physically interact with their device through its touchscreen interface to access its benefits. In combination with the item exhibiting the key physical traits of an attachment object, the child must expect it to provide certain positive outcomes—a learned association that develops for fixed or constant objects that consistently or reliably provide a particular set of positive outcomes ( Cairns 1966 ). Consumers likewise come to expect their smartphone to deliver a specific combination of positive outcomes, such as social interaction with loved ones or informational updates, in an immediate and consistent fashion ( Aoki and Downes 2003 ; Oulasvirta et al. 2012 ). Finally, similar to the highly personal nature of attachment objects (e.g., children have their own security blanket, pacifier, or stuffed animal that is not to be shared with others), smartphones are also highly personal objects; for example, one’s phone is rarely shared with anyone else and is often highly customized (e.g., personalized case; unique set of apps).

The central thesis of this article is that smartphones are endowed with a unique combination of properties that lead them to be viewed not just as pragmatic tools, but also as sources of comfort for owners—not unlike pacifiers for children. This thesis, in turn, makes specific predictions about downstream consequences of using the device. For example, owners will show a heightened tendency to seek out and engage with the device in moments of stress; and, once engaged with, the device will provide relief from stress. This proposition is compatible with other phenomena documented with the device, such as the anxiety ( Cheever et al. 2014 ) and cognitive load ( Ward et al. 2017 ) that users experience when separated from their smartphone.

Figure 1 illustrates the hypothesized mechanisms by which smartphones come to provide psychological comfort to their owners. In particular, we argue that as a result of four particular properties of the device—its portability, associated sense of privacy, personal nature, and haptic benefits—smartphones provide a reassuring presence for their owners, which leads the device to serve as a source of psychological comfort. We review these properties in turn:

CONCEPTUAL MODEL: SMARTPHONES AS A SOURCE OF PSYCHOLOGICAL COMFORT

Smartphones are portable. An essential property of smartphones that allows them to be a reassuring presence for owners is their portability. Their inherently compact nature enables these devices to be carried around by owners practically everywhere and at all times. As a result, the vast array of functionalities available on the device—such as communication features, social media, entertainment, and news updates—can be accessed at virtually any time and place, making one’s device dependable and readily available.

Smartphones afford a sense of privacy. A second critical property of smartphones is the sense of privacy that users experience while engaging with the device. One’s smartphone creates a private space in which users can immerse themselves in activities of their choosing—not unlike a teenager retreating to her room to listen to music or an adult retreating to his “man cave” to play video games. This sense of privacy is reinforced by the small screen of these devices, which encourages users to immerse themselves in their device and away from their external environment. In addition, the relatively small screen of a smartphone makes users feel as though their activities are less observable to others around them. The idea that use of a smartphone provides a heightened sense of privacy is consistent with some authors’ conceptualization of mobile phones as a form of “refuge” ( Trub and Barbot 2016 ). It is also consistent with research showing that computer-mediated environments facilitate the disclosure of personal information by enhancing users’ sense of privacy ( Joinson 2001 ).

Smartphones are highly personal possessions . Smartphones have properties that make them highly personal objects for owners. For example, as mentioned above, today’s smartphones involve a great deal of customization (e.g., selected apps, organization of content, personalized cases) and are in many ways connected to a person’s identity: unlike landline numbers, cellphone numbers are typically linked to a single person, and the device typically contains highly personal content such as personal messages, cherished photos, and favorite songs. Further, many owners tend to use the device for very personal reasons such as communicating with family, checking private messages, and interacting with friends on social media. Moreover, its portability implies that owners carry the device around on their person throughout most of the day, and many even keep it close to their bedside at night—features that further enhance the personal nature of the device relative to other personal objects ( Fullwood et al. 2017 ).

Smartphones provide haptic benefits. Another defining feature is the ergonomic design that makes smartphones easy and pleasant to hold in one’s hands. Moreover, most interactions with the device occur through physically touching and swiping its touchscreen interface. Importantly, such haptic qualities have been shown to generate hedonic benefits in the form of comfort and pleasure ( Peck and Childers 2003 ; Shu and Peck 2011 ; Vaucelle, Bonanni, and Ishii 2009 ).

We propose that this unique combination of properties—the knowledge that, whenever and wherever they want, consumers can retreat to a “private space” that is highly personal and functional and even provides haptic pleasure—enables the device to serve as a reassuring presence for owners. The reassuring presence provided by one’s phone, in turn, leads the device to play a special role for owners: that of providing feelings of psychological comfort when needed. The goal of the empirical work that follows is to substantiate this general thesis, which yields three specific empirical predictions:

P1: The psychological comfort that consumers derive from their smartphone arises from a combination of four properties that render it a reassuring presence in their lives: (a) its highly personal nature, (b) its portability, (c) the sense of privacy it provides, and (d) its rich haptic qualities. P2: In moments of stress, consumers show an increased tendency to seek out and engage with their smartphone as a means of coping with their discomfort (even when other objects are at their disposal). P3: Because of the psychological comfort that smartphones provide, even brief engagement with one’s phone can afford relief from a stressful situation.   P3A: This effect is greater when using one’s smartphone than when using another personal device with comparable functionality: one’s laptop.   P3B: This effect is greater when using one’s smartphone than when using an otherwise similar smartphone belonging to someone else.

We test these predictions across four studies. In the first study we obtain correlational evidence for the hypothesized drivers of the enhanced psychological comfort associated with smartphones as well as its downstream consequences ( figure 1 ). We then report the results of three controlled experiments that demonstrate the palliative effects of using one’s smartphone, showing that the device is sought out in moments of stress (study 2) and that, once engaged with, the device indeed provides greater relief than comparable devices (studies 3 and 4). In web appendix 1 we additionally report the results of a fifth study that lend further support for our hypotheses in a real-world context of stress, showing that consumers who recently quit smoking rely more heavily on their smartphone as a substitute for the palliative effects afforded by cigarettes.

The purpose of this first study was twofold: (a) to test the mechanisms hypothesized to underlie the role of smartphones as sources of psychological comfort, and (b) to assess some downstream consequences of this psychological comfort. As explained earlier, we theorize that smartphones come to serve as a key source of psychological comfort for consumers as a result of four specific properties: smartphones (a) tend to be highly personal, (b) are very portable, (c) can provide a heightened sense of privacy, and (d) have rich haptic qualities. These properties combine to make smartphones a reassuring presence for owners, ultimately enabling the device to enhance feelings of psychological comfort when the consumer engages with it (prediction 1).

We also examined whether the extent to which smartphones provide psychological comfort varies across consumers. For example, consistent with our conceptualization (see prediction 2), the more external stress people experience in their lives, the more we expect them to rely on their phone as a source of stress relief. Demographic traits may also play a role: older consumers, for instance, may be less dependent on their phone as a source of comfort than younger consumers, since older individuals are more likely to have developed alternate means of coping with stress (prior to the introduction of the smartphone). Likewise, consumers who have fewer positive associations with their smartphone—such as those who primarily use the device for work—may derive less psychological comfort from it.

To test these predictions, 885 participants from the Amazon Mechanical Turk (MTurk) panel (46% female) were surveyed about their use of and attitudes toward their smartphone. In addition, to obtain a comparison baseline, a separate sample of 470 MTurk participants responded to the same survey but with the questions rephrased to refer to their primary PC (e.g., laptop). These different sample sizes were based on a priori power calculations that reflected the different types of analyses planned for the two samples: given that participants’ responses about their smartphone were the primary focus of interest (e.g., examining the relationship between smartphone usage and levels of daily stress), we sought a sample size that would be large enough to detect small effects ( d = .2 with 80% power at α = .05). In contrast, given that the PC sample was collected solely as a basis for simple contrasts with the smartphone sample, we anticipated larger effect sizes (e.g., d = .3–.5) for which a smaller sample size was required to achieve the same power.

The survey, reproduced in web appendix 2 , was composed of four sections designed to measure the theorized psychological constructs depicted in figure 1 , their downstream consequences, as well as possible correlates of the effects. Each set of measures will be reviewed in turn.

Main Dependent Measure

The degree to which smartphones provide psychological comfort to their owners was measured through five seven-point items such as “Using my smartphone provides a source of comfort” and “When using my smartphone I feel safe and secure” (1 = “Not at all” to 7 = “Very much so”; α = .94).

Antecedents of Psychological Comfort

The first proposed antecedent of psychological comfort—the reassuring presence afforded by one’s phone—was measured on a four-item scale with items such as “Whenever I need my phone I know it will be there for me” and “I think of my phone as a reliable companion” (on a scale of 1 = “Not at all” to 7 = “Very much so”; α = .88). This construct was expected to arise from four hypothesized properties of the device: its perceived portability, sense of privacy, personal nature, and haptic pleasure (all of which were measured on the same seven-point scale). The perceived portability of the device was measured as a five-item scale with items such as “It is easy to reach for my phone whenever I need it” and “Wherever I go, my phone goes” (α = .88). The sense of privacy afforded by the device was measured as a four-item scale with items such as “My phone enables me to retreat to my private space” and “When I use my phone I feel like I am in my own safe space” (α = .94). The extent to which the device has a personal nature was measured as a five-item scale with items such as “I think of my smartphone as a very personal object” and “I would feel uncomfortable if someone used my smartphone” (α = .85). Finally, the haptic pleasure derived from interacting with the device was measured as a four-item scale with items such as “I enjoy the physical feeling of touching or holding my phone” and “Touching or swiping my phone’s screen/keypad feels pleasant” (α = .95).

Downstream Consequence: Use of Phone as Relief from Stress

To examine a potential downstream consequence of the psychological comfort expected to arise from smartphones, participants were asked to answer four items assessing the degree to which they used their phone as a means of coping with different exogenous sources of stress on a scale of 1 = “Not at all” to 7 = “Very much so”: “Using my phone helps me escape my daily pressures,” “I often turn to my phone in a moment of stress or anxiety,” “If I am in an uncomfortable social situation I turn to my phone,” and “I use my phone as a way of comforting myself when I feel stressed” (“stress relief” index; α = .91).

Individual Differences

To measure the extent to which usage contexts predicted the degree of comfort associated with one’s phone, participants were asked to indicate the degree to which they relied on their phone for social, entertainment, and work-related purposes. We theorized that consumers who use their phones more for “hedonic” purposes, such as communicating with friends/family and entertainment, would generally derive greater comfort from their smartphone than those who use it for more “utilitarian” reasons—namely, work-related purposes. We were also interested in whether the extent to which one’s phone is used to alleviate stress differs across different types of stress—specifically personal stresses (e.g., breakups, loneliness) and stress from work-related problems (e.g., meeting a late-night deadline). We therefore asked participants to rate the extent to which they were subject to different types of external stresses including health, financial, family, and work-related stress. Items related to the first three domains were averaged to form an index of “personal stress” (α = .82), and items related to the latter domain were averaged into an index of “work-related stress” (α = .65). Finally, participants were asked a number of demographic questions including their age and gender, as well as general device usage questions such as length of ownership and estimated hours of use per day.

Results and Discussion

We report and discuss this study’s findings in three stages. We begin by offering model-free evidence of the degree to which participants perceive their phone as a source of comfort, and the extent to which they report using it as a means of relieving stress. We then report the results of two structural equation models that test our theoretical predictions. The first tests our central hypothesis about the antecedents of the psychological comfort provided by smartphones as well as a key downstream consequence: the use of one’s phone for stress relief. The second model examines how the degree of comfort derived from one’s phone and its use for stress relief covaries with individual-difference factors, such as the degree of daily stress faced by owners and contexts in which the device is used (work vs. personal). A correlation matrix of all variables used in the analysis is reported in web appendix 3 .

Model-Free Evidence: Do Consumers Derive Comfort from Their Smartphone?

As theorized, participants indicated that their smartphone serves as a source of psychological comfort for them, rating it significantly above the scale midpoint ( M = 4.51; t (884) = 9.53, p < .001). Moreover, participants assessing their smartphone rated the device as a stronger source of psychological comfort than did participants assessing their PC ( M PC = 3.62; F (1, 1353) = 88.63; η 2 = .06; p < .001).

Similar support was observed for the predicted antecedents of psychological comfort. First, participants evaluating their smartphone rated it as significantly above the scale midpoint in terms of providing a reassuring presence ( M Reassurance = 5.18; t (884) = 25.11, p < .001), haptic pleasure ( M Haptic = 4.65; t (884) = 12.59, p < .001), feelings of privacy ( M Private = 4.69; t (884) = 12.33, p < .001), being a particularly personal object ( M Personal = 5.25; t (884) = 26.39, p < .001), and being highly portable ( M Portable = 6.21; t (884) = 67.16, p < .001). Smartphones were also rated as exhibiting most of these properties to a greater extent than PCs. Compared to PCs, smartphones were seen as providing more of a reassuring presence ( M PC = 4.50; F (1, 1353) = 66.26; η 2 = .05; p < .001), conveying greater haptic pleasure ( M PC = 4.42; F (1, 1353) = 6.05; η 2 = .004; p = .014), being more portable ( M PC = 4.09; F (1, 1353) = 875.88; η 2 = .39; p < .001), and being a more personal object ( M PC = 4.86; F (1, 1353) = 20.93; η 2 = .02; p < .001). The only dimension on which participants reported no difference was in the degree to which the devices provide a sense of privacy ( M PC = 4.72; F  < 1), suggesting that while users indeed derive a sense of privacy from using their phone, this may be a benefit afforded by one’s PC as well.

Participants also reported experiencing the hypothesized downstream consequence of psychological comfort. Participants rated the extent to which they used their smartphone as a means of relieving stress as significantly higher than the midpoint on average ( M Stress relief = 4.61; t (884) = 31.86, p < .001), and additionally, they reported exhibiting this behavior more with their smartphone than with their PC ( M PC = 4.12; F (1, 1353) = 27.60; η 2 = .02; p < .001).

Testing the Theoretical Model

As depicted in figure 1 , we hypothesize that four distinctive properties of smartphones—their portability, personal nature, haptic benefits, and capacity to provide a sense of privacy—make them a reassuring presence in the lives of consumers, which results in enhanced feelings of psychological comfort when using the device. This enhanced psychological comfort, in turn, allows the device to serve as a source of relief from stress. To test this account, we submitted the measures of the various theoretical constructs to a structural path model of the hypothesized process (using SAS’s Proc CALIS). Standardized maximum-likelihood estimates of the parameters of the model are reported in figure 2A .

(A) STUDY 1: PARAMETERS OF HYPOTHESIZED STRUCTURAL MODEL OF DRIVERS OF PSYCHOLOGICAL COMFORT FROM SMARTPHONE USE and ITS DOWNSTREAM CONSEQUENCE. (B) STUDY 1: PARAMETERS OF THE EFFECTS OF INDIVIDUAL DIFFERENCES ON COMFORT and STRESS RELIEF.

** DENOTES p ( t ) < .01; * p ( t ) < .05

The estimated model provides a good fit to the data (Bentler Comparative Fit Index [BCFI] = .88; standardized root mean squared residual [SRMSR] = .08), with estimates of the parameters supporting the hypothesized path structure. As predicted, the model supports the proposition that the reassuring presence afforded by one’s smartphone leads to enhanced psychological comfort from the device ( r Reassurance→Comfort = .48, t  =   21.61, p < .001). This reassuring presence, in turn, is driven by perceptions of the portability of the device ( b Portable→Reassurance = .23, t  =   9.60, p < .001), perceptions of the phone as a personal object ( b Personal→Reassurance = .10, t  =   3.77, p < .001), perceptions of privacy when using a phone ( b Privacy→Reassurance = .28, t  =   9.28, p < .001), and the perceived haptic benefits it affords ( b Haptic→Reassurance = .36, t  =   12.82, p < .001). The analysis shows that these perceived haptic benefits do not just contribute to the reassuring presence afforded by the device but also directly affect the psychological comfort it provides ( b Haptic→Comfort = .46, t  =   12.82, p < .001). Finally, the analysis supports the proposition that the more comfort derived from the device, the more it serves as a source of relief from stress ( b Comfort→Stress relief = .64, t  =   32.81, p < .001).

Examining Individual Differences

To examine whether the degree of comfort and stress relief afforded by one’s phone varies across different individual factors, we estimated an expanded form of the proposed process model that included two sets of additional paths. One set of paths estimated how much the degree of psychological comfort derived from a smartphone depends on the person’s age, gender, use for work, and use for social or entertainment purposes. A second set of paths estimated how much the tendency to rely on a smartphone for stress relief depends on personal sources of stress (family, health, financial) and on work-related stress.

The resulting model, depicted in figure 2B , again provides a good fit to the data (BCFI = .89; SRMS = .06). As expected, the more participants relied on their smartphone for “hedonic” purposes (e.g., entertainment), the more comfort they reported deriving from the device ( b Hedonic use→Comfort = .07, t  =   3.30, p < .001). In contrast, the use of one’s smartphone for work purposes was unrelated to the psychological comfort derived from the device ( b Work use→Comfort = .01, t = .45, NS). These results suggest that the psychological comfort that many consumers derive from their smartphone is not driven by its form factor alone: it is also driven by the types of activities that users tend to engage in on the device. Participants’ age had a negative correlation with psychological comfort ( b Age→Comfort = –.05, t = –2.86, p = .004), indicating that younger consumers are more prone to associate their smartphones with psychological comfort than older consumers. There was no relation between gender and degree of comfort derived from the device ( b Gender→Comfort = –.00, NS), suggesting that men and women are similarly likely to derive psychological comfort from their smartphone.

Consistent with our general conceptualization, participants who reported relying most on their phone as a means of coping with stress tended to be those under the greatest level of personal stress (e.g., health, family) ( b Personal stress→Stress relief = .21, t  =   7.61, p < .001). While work-related stress showed a similar relationship, this effect was not as pronounced as it was for personal stress ( b Work stress→Stress relief = .06, t  =   2.08, p = .038), suggesting that the psychological comfort afforded by one’s phone might better alleviate stress arising from personal issues than from work-related problems.

An obvious limitation of the first study is that the findings are correlational in nature, limiting our ability to draw causal conclusions. The purpose of the second study was therefore to test our general thesis—that an important role of smartphones is to provide psychological comfort when needed—in a controlled experimental setting. Specifically, we test the prediction that in moments of stress, consumers will show an increased tendency to seek out and engage with their smartphone (prediction 2). To examine this, in study 2 we manipulated participants’ level of stress and then unobtrusively filmed their behavior while they waited for the next part of the study. To the extent that smartphones indeed serve as a source of comfort for their owners, we predicted that compared to those under low stress, participants under high stress would be more likely to reach for and engage with their smartphones over other objects available in their vicinity as a means of coping with their discomfort.

Seventy-eight students from a large US East Coast university (69% women) were randomly assigned to one of two conditions of a single-factor (high stress vs. low stress), between-subjects design. The study was conducted in a controlled lab setting, with one participant per session, in sessions lasting 40 minutes. Participants were each paid $12.

The lab room was split into two separate areas: a waiting area containing a single chair alongside a small table with newspapers, and a survey area containing a single desk and chair. Upon arrival, all participants were asked to place their belongings, including their “smartphone and anything else that could be distracting,” in the waiting area. They were then led to the survey area, where they were asked to complete a series of paper-and-pencil tasks.

First, they were asked to rate their momentary feelings (with pencil and paper), including their level of felt comfort (measures described below). Next, depending on their assigned condition, participants completed a task designed to either increase their level of stress (in the high-stress condition) or not (in the low-stress condition). After the stress manipulation, participants were instructed to return to the waiting area and sit until the experimenter returned. All participants then sat alone in the waiting area for a predetermined amount of time while, unbeknownst to them, their behavior was surreptitiously filmed by a hidden camera located in a wall clock facing their chair. Upon the experimenter’s return, participants were led back to the survey area, where they were asked to complete another paper-and-pencil questionnaire in which they reported their felt comfort for a second time and answered a series of control and demographic questions. They were then debriefed, asked for permission to use their data, and paid.

Stress Manipulation

The stress manipulation was adapted from the classic Trier Social Stress Test ( Kassam, Koslov, and Mendes 2009 ; Kirschbaum, Pirke, and Hellhammer 1993 ) and was administered on paper. Participants were randomly assigned to either a high-stress or low-stress condition. In the high-stress condition, participants were given five minutes to prepare in writing a speech about why they are the perfect candidate for a particular job, under the cover of a “Job Interview Preparation Study.” They were led to believe that they would subsequently have to recite this speech from memory on camera so that a video analysis of their speech could be conducted at a later time. To boost the cover story, a video camera was placed in the survey area, facing participants as they prepared their speech. In contrast, in the low-stress condition, the task was positioned as a “Job Preparation Study.” Participants in this condition were given five minutes to write about advice they would give to someone who was starting the same position as described in the high-stress condition. Unlike in the high-stress condition, participants in this condition were not led to believe that they would need to present their writing on camera, and correspondingly there was no visible camera in the survey area. The exact instructions used in the stress manipulations are reproduced in web appendix 4 .

Unobtrusive Measurement of Behavior

After participants completed their assigned job-preparation task, the experimenter asked them to sit in the waiting area for “about 10 minutes.” In the high-stress condition, the alleged rationale for this waiting period was that a PhD student needed to review their speech to generate follow-up questions for them to answer on camera. In the low-stress condition, the alleged rationale was that the research assistant simply needed to transcribe their writing. In reality, during this 10-minute period, participants were unobtrusively filmed as they waited alone for the next part of the study.

During this time, participants had access to their personal belongings, including their smartphone and any other items that they had brought with them to the study (e.g., backpacks, books, laptop). In addition, two newspapers (the New York Times and Wall Street Journal ) were intentionally placed on the small table beside the chair, providing participants the option of engaging with alternative stimuli. Newspapers were chosen because they are commonly available in waiting areas and therefore serve as a natural, externally valid object with which participants could potentially engage.

Behavioral Dependent Measures

The video footage of participants’ behavior during the waiting period was subsequently coded by two independent judges who were blind to the study’s hypothesis and to participants’ conditions. The judges were instructed to code the footage for a set of objective aspects of the participant’s behavior (e.g., what time the participant reached for the first object during the waiting period; what the first object was). From these observable indicators we calculated a battery of behavioral measures designed to capture participants’ propensity to preferentially seek out and engage with their smartphone (e.g., the time elapsed before the participant first reached for his or her phone, if at all; the proportion of waiting time spent on the phone). Table 1 provides summary statistics of the key measured variables with respect to smartphone behavior across the two conditions.

STUDY 2: FREQUENCIES, MEANS, and INTERRATER RELIABILITIES FOR ALL BEHAVIORS DURING THE WAITING PERIOD

Means reported were calculated after two coders (blind to both condition and hypothesis) reconciled the measures they had originally disagreed on. Cronbach’s alphas reported in the table reflect the interrater reliability prior to reconciliation of measures.

Felt Comfort and Other Measures

Participants’ level of felt comfort was assessed at two points in time: once upon arrival (time 1) and a second time after the waiting period (time 2). Specifically, participants were asked to rate their agreement with 13 statements about their momentary feelings (see web appendix 5 ), five of which focused on their felt comfort: “I feel relaxed,” “I feel calm,” “I feel at ease,” “I feel a sense of comfort,” and “I feel anxious” (reverse-coded) on a scale of 1 = “Not at all” to 7 = “Very much so” ( Kolcaba and Kolcaba 1991 ; Marteau and Bekker 1992 ). Responses to these five items were averaged to create a felt-comfort measure for times 1 and 2. The change in felt comfort from time 1 (α = .86) to time 2 (α = .90) provided a check of the stress manipulation (albeit an imperfect one).

As control measures, participants additionally reported how frequently they use their phone per day, how long they have owned their current smartphone, specific behaviors surrounding the device (e.g., how much they paid for their phone case), and how emotionally connected they are to their phone (four items, α = .70). Participants were also asked to indicate the last time they used their smartphone and to describe what they did on their phone while in the waiting area (see web appendix 6 ). This latter measure was gathered to address the alternative explanation that it is solely the social functionality afforded by phones—rather than the phones themselves—that engenders usage under stress.

Preliminary Analyses

Upon debriefing, two participants (one in the high-stress condition) refused to have their data included in the study, and another five (two in the high-stress condition) were excluded for not having their smartphone with them at the study, thus leaving 71 participants for analysis. Participants did not differ across conditions in terms of their momentary feelings upon arrival to the study, the number of daily hours spent on the device, the length of time they owned the device, emotional connection to their smartphone, reported behaviors involving their device, or demographics (all F -values < 1). This suggests that randomization across conditions was effective. As a tentative check of the stress manipulation, while participants in the two conditions reported similar levels of felt comfort at time 1 ( M High-Stress = 4.19 vs. M Low-Stress = 4.29; F  <   1), at time 2 high-stress participants reported significantly lower levels of comfort ( M  =   3.44) than did low-stress participants ( M  =   4.71; F (1, 69) = 21.68; η 2 = .24; p < .001), suggesting that the manipulation was effective.

Main Analyses

Based on prediction 2, we predicted that high-stress (vs. low-stress) participants would be more likely to seek out their smartphones over other available objects, and to exhibit greater engagement with the device. Consistent with this prediction, the results showed that high-stress participants were indeed more likely to engage with their smartphone first—that is, before other available objects (63.9%)—than were low-stress participants (34.3%; x 2 (1) = 5.09, p = .024; see table 1 ). In addition, among participants who did reach for their phone at some point during the waiting period (72.2% in the high-stress condition and 60.0% in the low-stress condition), high-stress participants reached for their phone much sooner than did low-stress participants (Poisson regression β = –1.37, p < .001). Specifically, on average, high-stress participants reached for their phones only 23.9 sec after first sitting down, whereas low-stress participants waited 89.7 sec before first reaching for their phone.

With respect to the degree of sustained attention on the device, we first tested for differences in the average time spent per interaction with one’s smartphone during the waiting period. As predicted, high-stress participants spent significantly more time per interaction with their device ( M  =   299.32 seconds per interaction) than did low-stress participants on average ( M  =   165.54 seconds; Poisson regression β   = .37, p < .001). Relatedly, high-stress participants also showed greater engagement with their smartphone, spending a greater proportion of the total waiting time on their device ( M  =   51.3%) than low-stress participants ( M  =   31.3%; t  =   1.96, p = .054).

An additional analysis shows that high-stress participants were much more likely to reach for their smartphone first than for any of their other personal belongings (e.g., laptop, book) available during the waiting period ( M  =   13.9%; z  = 3.56, p < .001), suggesting that smartphones have special status as an object of comfort relative to other personal belongings.

Social Contact as an Alternative Explanation

One possible alternative explanation is that high-stress (vs. low-stress) participants sought out and engaged with their smartphones not for their comforting effects per se, but because they were in search of social contact—one of the many functions available on the device (e.g., writing a text message to a friend). Inconsistent with this account, high-stress participants were no more likely to make social contact on their phone during the waiting period (30.8%) than were low-stress participants who used their device (23.8%; x 2 (1) = 0.04, NS).

The findings of the first lab experiment support the prediction that moments of greater stress make consumers more likely to seek out and engage with their smartphone as a means of coping with their discomfort (prediction 2). Specifically, we found that compared to low-stress participants, high-stress participants were quicker to reach for their smartphone first, and they engaged with the device more intensely. In addition, high-stress participants preferentially sought out their phone over other personal objects they brought with them, such as items in their backpack (e.g., their laptop), as well as newspapers made available to them in the waiting area. This suggests that the palliative effect provided by one’s smartphone is not equally afforded by any potential source of distraction (e.g., one’s laptop, newspapers). The results also show that the tendency for participants to seek out their phones under greater stress cannot be accounted for by preexisting differences in participants’ situational feelings upon arrival, general emotional connection to their smartphones, or demographic factors, nor did it appear to be driven by differences in the desire to engage in social contact.

The results of the first lab experiment confirmed that in moments of stress, consumers show an enhanced tendency to seek out and engage with their smartphone, even when other objects are at their disposal (prediction 2). The purpose of the next two studies was to examine in a controlled lab experimental setting whether engagement with one’s smartphone does indeed provide psychological comfort when needed. Specifically, in study 3 we test the prediction that even brief engagement with one’s smartphone can provide relief from a stressful situation—more so than engaging with another personal device with comparable functionality: one’s laptop (prediction 3A). Laptops provide a meaningful comparison as a control condition for several methodological, theoretical, and substantive reasons. From a methodological standpoint, laptops and smartphones can be used to perform many of the same activities, which allows us to hold constant the task and information consumed across conditions. In addition, laptops and smartphones have similar ownership and usage rates among US consumers, which helps address possible alternative explanations related to device familiarity. (Tablets such as iPads, which exhibit lower ownership and usage rates among US consumers, were not selected for this reason.) From a theoretical standpoint, laptops and smartphones share many functionalities (e.g., browsing, social media, email), which is helpful in testing the idea that the special relationship that consumers form with their smartphone cannot be solely explained by its functionalities. At the same time, laptops differ from smartphones in several ways that are theoretically meaningful with respect to our conceptualization; namely, they are less portable, less haptic, and potentially less personal than smartphones (prediction 1). Finally, from a substantive standpoint, the comparison with laptops is a natural one, often discussed by marketers and firms as part of the “mobile revolution.”

In this study, all participants first underwent a stress induction and were then instructed to browse the same web page either on their smartphone in one condition, or on their laptop in the other condition. Participants’ momentary feelings were measured at three points during the study session: (1) prior to the stress induction, (2) after the stress induction but before participants used their assigned device, and (3) after participants used their assigned device.

We predicted that participants who used their smartphone would show greater recovery from discomfort due to stress than participants who performed the same task on their laptop. We additionally predicted that smartphone usage would be uniquely associated with enhanced feelings of comfort as opposed to other emotions. In other words, we expected smartphone use to enhance feelings of psychological comfort in particular rather than positive emotions in general (e.g., satisfaction).

Fifty students from the same university participant pool as in study 2 were randomly assigned to the conditions of a 2 (device: smartphone vs. laptop) × 3 (time: time 1 vs. time 2 vs. time 3) mixed design, with device as a between-subjects factor and time as a within-subject factor. We note here that, given that participants needed to be run one at a time per session, the sample size of study 3 was constrained by available lab resources; nevertheless, the within-subject nature of the mixed design lent reasonable power to the analysis. Specifically, an a priori power analysis using SAS PROC GLMPOWER concluded that the design study had an 85% chance of correctly rejecting a false null hypothesis of no time-by-device interaction at p = .05 (assuming a standard deviation and serial correlation of measures of .6, and an expected post-stress difference between devices of .5).

All participants were required to bring both their smartphone and their laptop with them to the session. To control for potential distractions posed by the presence of other participants, the study was administered one participant at a time. To ensure that the presence of the devices would not impact participants’ feelings prior to the device manipulation, upon arrival participants were asked to put their smartphone and laptop in the adjacent cubicle. They each received $8 for 30 minutes of participation.

Felt Comfort Measure (Time 1)

At the beginning of the study, participants were told that they would be participating in two (allegedly) unrelated studies that were combined for greater efficiency. Before beginning the “first” study, participants were asked to rate their agreement with the same 13 statements about their momentary feelings as in study 2 (see web appendix 5 ), including the five statements focusing on participants’ felt comfort (“I feel relaxed,” “I feel calm,” “I feel at ease,” “I feel a sense of comfort,” and “I feel anxious” [reverse-coded]). Responses to these five items were averaged to create a measure of felt comfort at time 1 (α = .88) as part of the main dependent variable. To test the prediction that it is felt comfort in particular that is enhanced by smartphone (vs. laptop) use rather than other types of feelings in general, the remaining nine statements were included to assess a variety of other momentary emotions.

Stress Induction

Next, all participants completed “study 1,” which was cast as a task performance study but actually served as a stress induction. To induce stress among participants, we used a standard stress procedure in the literature, which consists of administering a series of cognitive tasks under time constraints ( Boyes and French 2010 ). Based on two pretests, one of them with students from the same pool as the main study, we selected three sets of cognitive tasks for the stress induction: (a) 15 GMAT math problems, (b) 18 Remote Associates Test (RAT) items ( Mednick and Mednick 1967 ), and (c) 18 anagrams. The three sets of tasks were presented in increasing order of task difficulty, as were the individual items within each set. Participants in the main study carried out the tasks on paper and were given three minutes to complete each task, which pretests had shown was greatly insufficient. To intensify the stressful aspects of the overall procedure, the experimenter set a timer to ring loudly every minute. Pretest results indicated that the overall procedure was effective in inducing stress among participants. The problem sets are reproduced in web appendix 7 .

Felt Comfort Measure (Time 2)

After completing the stress induction, participants were again asked to report their momentary feelings on the same items as at time 1, with responses to the five comfort-related items averaged into an index of felt comfort at time 2 (α = .85). Changes in felt comfort from time 1 to time 2 served as a check of the stress induction.

Device Manipulation

Next, participants completed “study 2,” which was ostensibly about social media but in fact administered the device manipulation. Participants were randomly assigned to browse a specific social media site either on their smartphone in the experimental condition, or on their laptop in the control condition. To minimize the possibility that any effects observed might be driven by differences in the content consumed across conditions, all participants were asked to browse a page called “Things Fitting Perfectly into Other Things” on Tumblr. This specific web page was chosen for two reasons. First, Tumblr has comparable interfaces across its mobile and web-based formats, and second, this particular Tumblr blog displays simple images with minimal or no text, making the content similarly amenable to browsing on both laptop and smartphone devices. As a check, at the end of the study participants across conditions were asked to rate how user-friendly they found the browsing experience to be. All participants were given five minutes to browse the site “Things Fitting Perfectly into Other Things,” allegedly in order to search for images that they particularly liked on the page.

Felt Comfort Measure (Time 3)

After five minutes had passed, the experimenter instructed participants to stop browsing and handed out the final set of questions that measured participants’ felt comfort after using their assigned device. Participants responded to the same questions presented at times 2 and 3, yielding a third five-item index of felt comfort (α = .78). Increases in felt comfort from time 2 to time 3 were interpreted as relief from stress following device usage, which was the primary focus of the study.

Finally, participants were asked to indicate their preexisting familiarity with the Tumblr site (whether they had a Tumblr account prior to the study) and how user-friendly they found the Tumblr application or website to be on a scale of 1 (“Not user-friendly at all”) to 5 (“Very user-friendly”). They also answered a series of control questions about demographics, frequency of smartphone use (i.e., average number of hours spent on the device per day), as well as the perceived difficulty of the stress-induction tasks (i.e., how difficult they found each of the three problem sets to be, and how much more time they would have liked to complete the tasks) (see web appendix 8 ).

The results confirmed no differences between device conditions in terms of participants’ demographics, smartphone usage frequency, and preexisting familiarity with Tumblr. Of the 13 items assessing momentary feelings at time 1 (prior to the stress induction), only one—felt frustration—indicated an unexpected initial difference between conditions, with participants in the smartphone condition reporting a marginally higher level of frustration upon arrival ( M  =   2.60) than those in the laptop condition ( M PC = 1.88; F (1, 48) = 3.96, p = .051; see web appendix 9 for all means). However, none of the five items assessing the dependent measure of interest—felt comfort—showed any initial difference.

A check of the stress induction confirmed a significant decrease in participants’ felt comfort from time 1 (upon arrival; M  =   4.87) to time 2 (immediately following the stress induction; M  =   3.54; F (1, 48) = 93.08; η 2 = .66; p < .001). Importantly, between time 1 and time 2, there was no time × device interaction ( F  <   1), confirming that the stress induction had parallel effects across conditions. There were no significant differences across conditions in the reported difficulty of each stress-induction task (largest F (1, 48) = 2.16, NS), the additional amount of time participants would have liked in order to complete the tasks ( F (1, 48) = 2.84, NS), or in the number of questions attempted in each task (all F -values < 1). These latter findings suggest that the randomization was largely effective in equating participants across conditions prior to the device-usage manipulation.

Stress Relief Due to Device Usage

To test the prediction that using one’s smartphone provides greater relief from stress than using another personal device with comparable functionality (one’s laptop), measures of participants’ felt comfort at times 1, 2, and 3 were submitted to a mixed ANOVA with time as a within-subject factor and device as a between-subjects factor. A significant main effect of time ( F (2, 96) = 64.80; η 2 = .57; p < .001) showed a decrease in participants’ felt comfort from time 1 ( M  =   4.87) to time 2 ( M  =   3.55), as reported earlier ( F (1, 48) = 83.40; η 2 = .63; p < .001), followed by an increase in felt comfort from time 2 to time 3 ( M  =   5.11; F (1, 48) = 98.64; η 2 = .64; p < .001).

More importantly, this effect was qualified by a significant time × device interaction ( F (2, 96) = 4.16; η 2 = .04; p = .018). Focusing on changes in comfort between time 2 and time 3, a planned interaction contrast reveals, as predicted, a greater increase in felt comfort from time 2 to time 3 among participants who used their smartphone ( M Time 2 = 3.37 vs. M Time 3 = 5.33; F (1, 24) = 68.32; η 2 = .74; p < .001) than among participants who browsed the same content on their laptop M Time 2 = 3.73 vs. M Time 3 = 4.88; F (1, 24) = 31.67; η 2 = .57; p < .001; interaction contrast: F (1, 48) = 6.55; η 2 = .04; p = .014).

In fact, participants in the smartphone condition reported even greater comfort at time 3 ( M  =   5.33) than they did at time 1 ( M  =   4.77; F (1, 24) = 7.97; η 2 = .25; p = .013), whereas participants in the laptop condition only returned to the same level of felt comfort at time 3 ( M  =   4.89) as they reported at time 1 ( M  =   4.97; F  <   1; interaction contrast: F (1,48) = 5.23; η 2 = .09; p = .027). Therefore, not only did the use of their smartphone help participants recover from stress better than did the use of their laptop, it actually raised participants’ overall sense of comfort over and above the initial state they were in prior to the stress induction.

Mixed ANOVAs of other feelings measured at times 1, 2, and 3 show main effects of time on feelings of confidence, satisfaction, focus, frustration, happiness, and sadness (largest F (2, 96) = 52.04, p < .001; see web appendix 6 for all means). However, none of these effects was moderated by the type of device (largest F (2, 96) = 1.40, NS), suggesting that it is feelings of comfort (and stress alleviation) in particular that smartphones enhance, rather than positive affect in general.

The results of study 3 support the proposition that consumers not only preferentially seek out their smartphone in moments of stress (as shown in study 2), but also derive psychological comfort from their device when needed. Specifically, the study shows that compared to the use of another personal device with comparable functionality, the mere use of one’s smartphone to perform the same brief task is sufficiently comforting to provide relief from a recent stressful experience. The results of this experiment are noteworthy in three respects. First, methodologically, the fact that the task and associated content (the web page) were held constant across conditions means that any observed difference in comfort and stress relief cannot be attributed to mere differences in information consumed across devices. Second, from a more theoretical perspective, the fact that the effects cannot be attributed to differences in content means that the observed sense of comfort arises from the device itself. Third, the fact that the sense of comfort and stress relief provided by the use of one’s smartphone exceeds that afforded by the use of one’s laptop—a device with comparable functionalities—is consistent with a general view that the relationship that consumers have with their smartphone is a special one that cannot be strictly reduced to the device’s functional value.

Although personal laptops provide a natural point of comparison for testing whether smartphones serve as distinct sources of comfort for owners, a limitation of study 3 is that laptops may have been less stress-relieving for reasons that are unrelated to our theorizing. For example, while laptops differ in terms of two of the theorized drivers of psychological comfort—their portability and haptic nature (prediction 1)—it is possible, for example, that consumers may use their laptop more for work and less for leisure than their smartphone, or that the effects are driven in part by differences in their physical form that are not accounted for by our theory. Thus, a more stringent test of our theorizing would hold the type of device constant.

The purpose of the final lab experiment is to show that the use of one’s own smartphone to engage in a given activity helps alleviate stress to a greater extent than the use of an otherwise similar smartphone belonging to someone else (prediction 3B). Such a finding would lend support to our theorizing in several ways. First, it would provide further evidence that the comfort that people derive from interacting with their smartphone does not strictly arise from the sheer functionalities available on the device. Second, it would provide support for our proposition that the psychological comfort derived from smartphones is driven in part by the highly personal nature of the device (prediction 1).

The general design of this study was similar to that of study 3. All participants first underwent a stress induction and then were asked to browse the same content on a smartphone. In one condition, it was their own smartphone; in the other condition, it was an otherwise similar phone belonging to the lab. We predicted that compared to participants engaging in the task on the lab’s smartphone, participants engaging in the same task on their own smartphone would derive greater psychological comfort and thus exhibit greater recovery from stress.

Seventy-five participants from a different university than in studies 2 and 3 (71% women) were randomly assigned to the conditions of a 2 (ownership: own smartphone vs. lab smartphone) × 3 (time: time 1 vs. time 2 vs. time 3) mixed design, with ownership as a between-subjects factor and time as a within-subject factor. The effect sizes observed in study 3 provided guidance for determining the sample size for study 4, which was planned using SAS’s PROC GLMPOWER. Assuming means and standard deviations similar to those observed in study 3 as well as the same mixed design, we sought a sample size that would have a 90% probability of correctly rejecting the null hypothesis of no interaction between device and time 2-to-3 at α = .05. The final sample size of 75 had an a priori power of .93.

The study was again conducted in a controlled lab setting, one participant at a time, in sessions lasting 30 minutes for which participants were paid $10. All participants were required to bring their smartphone to the lab. They were led to believe that they were completing two separate surveys combined for greater efficiency. As part of “study 1,” which was cast as a task performance study, participants were first asked to report their momentary feelings along the same 13 items as in studies 2 and 3 (see web appendix 5 ), plus an additional item measuring felt stress (“I feel stressed”). The five comfort-related items from the prior studies and the additional stress-related item (reverse-coded) were combined to form a six-item index of felt comfort at time 1 (α = .90).

All participants were then administered the same stress induction as were the high-stress participants in study 2. That is, under the cover of a “Job Interview Preparation Study,” all participants were given five minutes to prepare in writing a speech about why they are the perfect candidate for a particular job, and were led to believe that they would have to recite this speech on camera. After completing the stress induction, participants were again asked to report their momentary feelings on the same items as at time 1, which was used to construct the six-item index of felt comfort at time 2 (α = .91). Changes in felt comfort from time 1 to time 2 served as a check of the stress induction.

Next, participants completed “study 2,” ostensibly about the user experience of different devices, which actually served as the ownership manipulation. Participants were led to believe that the experimenters were interested in “how users’ reactions to the use of the lab’s devices compare to their reactions to their own personal devices.” Participants in the own-phone condition were asked to take out their smartphone to browse the “Things Fitting Perfectly” Tumblr page for five minutes (as in study 3), whereas participants in the lab-phone condition were asked to complete the same browsing task on the lab’s smartphone—either on an iPhone 6 or Samsung Galaxy S5 (an exploratory survey revealed that these two smartphone models were the two most commonly owned models among the lab participant pool). To ensure that participants in the lab-phone condition did not need to search through an unfamiliar interface to locate the content, the Tumblr page was already open on the device in this condition.

After five minutes had elapsed, participants’ momentary feelings were measured for a third time, providing an index of felt comfort at time 3 (α = .88). Again, an increase in felt comfort from time 2 to time 3 was interpreted as relief from stress following device usage, which was the primary focus of the study. Participants then responded to the same control questions about demographics and frequency of smartphone use (i.e., average number of hours spent on the device per day) as in study 3. Finally, lab-phone participants were additionally asked to rate how similar the lab’s phone was to their own device along the following dimensions (on a scale of 1: “Completely different” to 7: “Exactly the same”): physical comfort, ease of use, brightness, and vividness/clarity (see web appendix 10 ). These four measures were combined to form a lab-phone similarity index (α = .84).

As in study 3 there were no differences across conditions in terms of participants’ demographics or smartphone usage frequency. In addition, there were no initial differences along any of the 14 items assessing momentary feelings at time 1 prior to the stress induction. Participants in the lab-phone condition reported perceiving the lab’s device as similar to their own, with the mean perceived similarity significantly above the midpoint of the seven-point scale ( M  =   5.92; t (28) = 8.02, p < .001). Finally, a check of the stress induction confirmed a significant decrease in participants’ felt comfort from time 1 (upon arrival; M  =   4.54) to time 2 (immediately following the stress induction; M  =   3.33; F (1, 73) = 91.43; η 2 = .52; p < .001; see web appendix 11 for all means).

To test the central prediction that using one’s smartphone provides greater relief from stress than an otherwise similar phone belonging to someone else, measures of participants’ felt comfort at times 1, 2, and 3 were submitted to a mixed ANOVA with time as a within-subject factor and device as a between-subjects factor. A significant main effect of time ( F (2, 146) = 98.33; η 2 = .55; p < .001) showed that the decrease reported above in participants’ felt comfort from time 1 ( M  =   4.54) to time 2 ( M  =   3.33) was followed by an increase in felt comfort from time 2 to time 3 ( M Time 2 = 3.33 vs. M Time 3 = 5.25; F (1, 73) = 137.50; η 2 = .65; p < .001).

More importantly, as in study 3, this effect was qualified by a significant time × device interaction ( F (2, 146) = 8.15; η 2 = .05; p < .001). Examining the interaction contrast for time 2 to time 3 ( F (1, 73) = 12.24; η 2 = .05; p < .001), we see that the results reveal a significantly greater increase in felt comfort among participants who used their own smartphone ( M Time 2 = 2.90 vs. M Time 3 = 5.36; F (1, 37) = 94.20; η 2 = .72; p < .001) than among those who browsed the same content on the lab’s smartphone ( M Time 2 = 3.76 vs. M Time 3 = 5.14; F (1, 36) = 64.65; η 2 = .64; p < .001). These results thus provide a conceptual replication of those observed in study 3. In addition, on average, the degree of comfort reported immediately after use of the device was greater than that reported at the onset of the study ( M Time 3 = 5.25 vs. M Time 1 = 4.54; F (1, 73) = 28.09; η 2 = .27; p < .001), although here the time-by-device interaction was not significant (own-phone: M Time 3 = 5.36 vs. M Time 1 = 4.51; lab-phone: M Time 3 = 5.14 vs. M Time 1 = 4.57; interaction: F (1, 73) = 1.07; η 2 = .01; NS).

One factor that potentially complicates this analysis, however, is that the effect of the stress manipulation was somewhat stronger for those in the own-phone condition compared to the lab-phone condition, such that participants in the own-phone condition reported lower comfort after the manipulation than those in the lab-phone condition (time 2: M own = 2.90 vs. M lab = 3.76; F (1, 73) = 8.50; η 2 = .10; p = .005). While this difference would presumably make it more difficult to observe greater stress relief in the own-phone condition, to ensure that the effects were not influenced by the difference in the strength of the manipulation we reanalyzed the data in a mixed-model analysis using SAS Proc Mixed that controlled for differences in felt comfort at time 2, treating participants as a nested random effect. The analysis confirmed the original findings, again revealing a significant time-by-ownership interaction after controlling for time 2 differences ( F (1, 73) = 15.75; η 2 = .04; p < .001). Specifically, participants who used their own smartphone still experienced a greater rate of recovery from stress from time 2 to time 3 ( LSM Time 2 = 3.18 vs. LSM Time 3 = 5.63; F (1, 37) = 133.43; η 2 = .89; p < .001) relative to participants who engaged with the lab’s smartphone ( LSM Time 2 = 3.47 vs. LSM Time 3 = 4.86; F (1, 36) = 82.32; η 2 = .27; p < .001).

The results of this study conceptually replicate those of study 3 and extend them in an important way. They again show that the mere use of one’s smartphone to perform a simple task for a few minutes is sufficiently emotionally comforting to provide relief from a recent stressful experience, replicating the results of study 3 within the smartphone (vs. laptop) condition. More importantly, the results additionally seem to suggest that the comforting effects of using a smartphone are stronger for one’s own smartphone than for an otherwise similar smartphone belonging to someone else. This finding is consistent with our theory that the psychological comfort afforded by one’s phone is partly driven by the personal nature of the device, which enables it to serve as a reassuring presence for owners and thus increase their sense of comfort (prediction 1). Studies 3 and 4 tested all the proposed components of our theoretical model.

The purpose of this research was to develop a better understanding of the nature of the relationship that many consumers form with their smartphone—a device that in the span of only a few years has become one of the most ubiquitous and frequently used products among consumers, as well as the primary device through which online consumption activities take place. While in recent years a descriptive literature has emerged on people’s self-reported smartphone use ( Bianchi and Phillips 2005 ; De-Sola Gutiérrez et al. 2016 ), theoretical and experimental investigations into this relationship have been limited. In this work we provide one of the first theoretical accounts of many consumers’ relationship with their smartphone, including the antecedents that underlie it as well as downstream consequences. Our central thesis is that, for many consumers, smartphones serve as more than just practical tools: consumers also experience enhanced psychological comfort from engaging with their device, which allows it to serve as a palliative aid for owners during moments of stress—not unlike how pacifiers (and other attachment objects) provide psychological comfort to young children.

Also central to our theory is the proposition that a smartphone affords feelings of comfort not just because of its functionalities, but rather because of a unique combination of properties: its role as a reassuring presence in the daily lives of consumers, which arises from its portability, highly personal nature, the sense of privacy it invokes when engaged, and the haptic pleasure users derive from handling their device. The role of the device as a reassuring presence, in turn, allows the device to enhance feelings of psychological comfort when consumers engage with it.

In this article we report the results of four studies, including a large-scale field study and three controlled laboratory experiments, that lend support to these ideas. The first field study offered evidence for the proposed theoretical model about how the various properties of one’s smartphone lead it to represent a source of psychological comfort, as well as the downstream consequences of this comfort. Next, a lab experiment showed that participants who underwent greater stress were more likely to seek out their phone and to show greater engagement with the device (even with other objects at their disposal), presumably as a means of coping with stress. The final two controlled lab experiments then provided direct evidence for the role of smartphones as a source of psychological comfort, showing that participants who engaged with their smartphone reported a greater enhancement in comfort after stress relative to those using the same feature on their personal laptop (study 3) and even those using an otherwise similar smartphone belonging to someone else (study 4). An additional study reported in web appendix 1 (study 5) provides further support for our general thesis.

Might Other Conceptualizations Better Describe Consumers’ Relationship to Their Smartphone?

As discussed at the outset of this article, the majority of work on the topic of consumers’ relationship to their phone has argued that it resembles a behavioral addiction ( Alter 2017 ; De-Sola Gutiérrez et al. 2016 ; Hostetler and Ryabinin 2012 ). We believe, however, that “addiction” is an inadequate conceptualization of consumers’ relationship to the device. While the term can be used to label a certain set of behaviors with the device, it is a strictly negativist framing of consumers’ relationship to their phone and, more importantly, does not provide insight into the psychological mechanisms that give rise to this relationship. In this work we offer evidence that there is a positive emotional side to individuals’ relationship with their phone: namely, its ability to serve as a source of comfort for many consumers. We posit that these associations of comfort apply to a broader segment of consumers than “addiction,” which can be understood as a narrower behavioral phenomenon. Moreover, we propose and test a theory that explains the origins of this proposed relationship.

One question that might arise is whether the use of one’s phone as a source of comfort results from its ability to serve as a means of distraction—something that could be similarly satisfied by a number of objects or substances (e.g., smoking or eating, as shown in study 5, web appendix 1 ). Consistent with this, participants in study 1 indicated that they often used their phone to distract themselves when they felt bored ( M =  5.42 on a seven-point scale; significantly above the midpoint: t (884) = 25.60, p < .001). That said, our findings suggest that distraction is only one part of the story. In study 2, for example, participants who felt greater stress were more likely to seek out their smartphone over other objects at their disposal that could serve as a means of distraction (e.g., their laptop, newspapers). Likewise, in studies 3 and 4 we found, for example, that browsing the same distracting content (a particular Tumblr page) after a stress induction indeed helped participants recover from their discomfort, but that the rate of recovery was greater when participants browsed the content on their smartphone than on other devices. Taken together, these results suggest that distraction alone cannot fully account for the palliative benefits afforded by the device.

More generally, another possible conceptualization of the nature of consumers’ relationship with their smartphone is that they view the device not as a source of comfort per se, but rather as an extension of themselves ( Belk 1988 ; Schifferstein and Zwartkruis-Pelgrim 2008 ). To examine this possibility, in study 1 we asked participants to indicate (on a seven-point scale) the degree to which they thought of their smartphone or PC as an extension of themselves. We used a modified version of Ball and Tasaki’s (1992) self-extension scale, which included the items: “My phone (PC) reminds me of who I am,” “If my phone (PC) was praised by others I would feel as if I were praised,” “If someone ridiculed my phone (PC) I would feel attacked,” and “If I lost my phone (PC) I would feel like I lost a little of myself” (α = .87). The results are inconsistent with a self-extension account of consumers’ relationship to their device. First, participants tended to disagree when asked if they saw their smartphone as an extension of themselves ( M Smartphone = 3.42 out of 7; significantly below the scale midpoint; t (884) = –10.44, p < .001). Second, to the degree that they did view their phones as self-extension, it was to a lesser extent than their PC ( M PC = 3.70; contrast F (1, 1353) = 8.12; p = .004).

Implications for Consumer Welfare and Practitioners

Our findings show that, in addition to deriving functional benefits from the device, phone owners seem to also derive emotional benefits that even Steve Jobs may have failed to foresee: a device with the capacity to provide comfort and relief in times of stress. In a field study reported in web appendix 1 (study 5), we provide real-world evidence that consumers particularly susceptible to stress—those who recently quit smoking—were more likely to show emotional and behavioral attachment to their phone, suggesting that the device may serve as a means of compensating for the stress relief previously afforded by cigarettes. The finding that people who recently quit smoking made greater use of their smartphone suggests that such behavior might actually be encouraged by health professionals as a means to reduce stress across a variety of contexts. While our results imply that adults can derive emotional benefits from engaging with their smartphone, as noted above, much of the extant research on people’s relationship to the device has focused on the potential dark side to this attachment—the possibility that, for some, an emotional connection to their phone might develop into an apparent addiction to the device, with negative social and emotional consequences ( Hostetler and Ryabinin 2012 ). An important area for future research, therefore, would be to better understand the conditions under which the comforting benefits of smartphone ownership might transform into an unhealthy dependence on the device and, just as critically, the kinds of design interventions that might be taken to diminish—rather than enhance—attachment in such cases.

Our results also have important practical implications for firms and marketers, who over the past few years have been responding to the “mobile revolution” by diverting more of their budgets to mobile advertising ( eMarketer 2016 ) and attempting to pursue “mobile-first” digital strategies ( Kepes 2015 ). The findings shed light on the unique emotional mind-set that consumers experience while on the device. For one, whereas mobile phone companies focus their persuasive messaging almost exclusively on features available on the device (e.g., battery life, display resolution), our findings suggest that marketers might additionally emphasize the psychological feeling of comfort and reassurance that comes with having one’s smartphone in hand. To the extent that people are more open to processing information when in a relaxed state ( Pham, Hung, and Gorn 2011 ), retailers could leverage this insight by investing more aggressively in beacons and other technology that enable them to reach customers on their smartphone in-store.

Finally, our theoretical model provides insights for smartphone brands that, for example, might be interested in understanding why consumers would be eager to upgrade their current phone even though the device serves as a source of comfort for them. Within our model, feelings of comfort are theorized to flow not just from mere ownership but also from the portability, customizability, and haptic nature of the device—attributes that tend to improve with each new generation of smartphone models. Thus, if a newer model offers more opportunities for personalization and a more ergonomic and haptic interface, for example, then our model would predict that consumers may be willing to abandon their current smartphone for a potentially more comforting one.

Limitations and Future Research

As one of the first theoretically driven attempts to understand the psychology that underlies consumers’ relationship to their smartphone, our research was intentionally limited in scope. For example, study 1 showed that one of the antecedents of consumers’ relationship to their phone is the haptic pleasure that arises from its use, which was further substantiated by the results of study 3 wherein participants derived more comfort from engaging with their smartphone than their PC, a less haptic personal device. An interesting avenue of future research would be to further explore the role of haptics in the effect—for example, whether similar psychological effects arise for other electronic devices that consumers have constant tactile contact with, such as “wearable tech” (e.g., Fitbits, Apple watches).

Future work could also investigate the relation of our findings to the literature on adult attachment theory, which has focused on people’s style of attachments to close others. Notably, prior work has shown that people with insecure attachment (anxious or avoidant) tend to be more likely to rely on childhood attachment objects (such as a teddy bear) in adulthood ( Nedelisky and Steele 2009 ). While a full empirical examination of this issue is outside the scope of the current investigation, as an initial exploration of this issue in study 1 we examined the degree to which participants’ attachment styles correlated with the extent to which they viewed their phone as a source of psychological comfort (using the six-item scale described in study 2). We thus asked participants in study 1 to respond to Hazan and Shaver’s (1987) adult attachment style scale (using rewording suggested by Collins and Read 1990 ), which measures the degree to which individuals exhibit three styles of interpersonal attachments: secure attachment, characterized by trust and friendship; anxious attachment, characterized by fear of being abandoned or unloved; and avoidant attachment, characterized fear of closeness. The results supported the expected associations. Participants who exhibited more of an avoidant attachment style were most likely to rely on their phone as a source of comfort ( r = .18, p < .001), followed by those exhibiting greater anxious attachment ( r = .10, p = .003). In contrast, there was only a weak association between secure attachment and degree of comfort derived from the device ( r = .06, p = .077). These preliminary results suggest that people may rely on their smartphone as a surrogate for the comfort derived from interpersonal relationships, which is generally consistent with our broader conceptualization of smartphones as exhibiting similar properties as attachment objects. We see a more complete investigation of the relationship between people’s attachment styles and the comfort they derive from their smartphone as a fruitful avenue for future research.

Moreover, we show that smartphones yield greater comfort by having all participants browse the same, relatively neutral content across devices (a particular page on Tumblr). We do not suggest, however, that this effect would hold across all types of content or activities. For example, in study 1 we show that people tend to derive greater comfort from their phone if they tend to use the device for more positive purposes such as social/entertainment, but derive relatively less comfort if they rely on it primarily for work. Future research could more directly test the boundaries of the effect, for example, by varying the valence of the content presented to participants across devices. Future work could also test for additional downstream consequences of the effects documented in this article—for example, whether the increased feeling of comfort associated with smartphone use will lead certain persuasive messaging (e.g., messages with more comforting language or ads for comfort-related products) to be particularly effective when targeted to users on their smartphones. Finally, if consumers are indeed more easily persuaded by certain messaging on their smartphone, as previous work suggests ( Pham et al. 2011 ), this can be seen as a potential threat to more vulnerable populations—most notably young people for whom the problem of “smartphone addiction” is seen as quite real ( Walsh et al. 2011 ). Another area for future research might thus be to investigate whether the factors that drive attachment for younger segments of the population differ from those driving attachment among older consumers.

The first author supervised the collection of and analyzed all of the data. Study 1 was an MTurk survey conducted in March 2019. Lab studies 2 and 3 were conducted in the behavioral lab of Columbia Business School. Study 2 was conducted in February through April 2017, and study 3 was conducted in June 2015, and then continued running in January through March 2016. Study 4 was conducted in the behavioral lab of the Wharton School of the University of Pennsylvania in June 2018. Study 5 (reported in the web appendix ) is an MTurk survey that was conducted in May 2016. All data can be accessed on the Open Science Framework at https://osf.io/z36ru/? view_only=03e059d0e7064bde89bc5bf01242bf73 .

This article is based on the first author’s doctoral dissertation completed under the second author’s guidance at Columbia University. The authors thank the other members of the dissertation committee—Jeffrey Inman, Robert Meyer, Oded Netzer, and Olivier Toubia—for their very useful input at various stages of this project. They also thank the Wharton Behavioral Lab and the various members of the Research on Emotions and Decisions (RED) lab at Columbia for their input on some of the studies.

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The impact of mobile phones on high school students: connecting the research dots

Technium Social Sciences Journal

Due to the ubiquity of mobile phones throughout the world, there have been extensive scholarly research on their impact within and across areas. Researchers have looked at the impact of this device from different angles, and many scholars have been concerned with the extent to which its usage affects students&#39; academic performance, particularly in high-school. Several scholars (Kevin Thomas and Marco A. Munoz, 2016, Hosoglu, 2019) have probed the topic using different databases, but the present paper is one of the few to examine Scopus, Science Direct, Web of Science and Cairn with a two-fold aim, namely to summarize the state-of-the-art in this subject area, and, most importantly, to discuss the researchers&#39; attitudes. 70 articles from the three databases were selected following the PRISMA guidelines to investigate the researchers&#39; decade-long trends in the literature. We discerned different attitudes and results vis-a-vis the mobile phone impact depending on the variab...

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From “brick” to smartphone: the evolution of the mobile phone

• FEATURES POSTERMINARIES
• Published: 05 March 2021
• Volume 46 , pages 287–288, ( 2021 )

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Telephony began in the 1870s with the invention of the telephone. Alexander Graham Bell filed a patent for his version of the telephone at the US Patent Office in Washington, DC, on February 14, 1876, just a few hours before his competitor Elisha Gray filed his patent based upon independent work. 1 Since then, materials research has pushed this field of technology involving the development, application, and deployment of telecommunication services, particularly in recent years.

The proliferation of telephones did not make a great overnight leap. In fact, as late as 1950, only 62% of American households contained a telephone, 2 and that number had been significantly smaller before World War II. Most communications were by telegram, letter, or through face-to-face discussions. One significant hindrance to communication was natural disasters, which often led to long periods of no information between family and friends.

On the evening of January 3, 1949, a devastating tornado struck the small town of Warren, Ark. 3 The tornado left more than 50 dead and more than 300 injured. My dad’s parents, brother, sister-in-law, niece, and nephew, as well as many friends, lived in Warren at that time and were affected. Telephone lines were destroyed. My parents and I lived far away and had no way to get in contact with them. More than a week passed before we received word that they were okay.

My father later accepted a faculty position at Arkansas A&M College in Monticello, 16 miles from Warren, to be closer to his family. When we moved to a farm near the campus, our first telephone line was a party line, which consisted of a single channel shared by many people. This offered little in the way of privacy, as others outside of your household could listen to conversations. We eventually upgraded to a private landline. The phone was located in a central part of the house, and the cord was only a few feet long, which meant that you were essentially tethered to that spot when making or answering a call, not ideal for any teenager craving privacy.

People then didn’t have the luxury of cell phones, and instead often used pay phones by inserting money or calling collect. Jim Croce has a wonderful song, “Operator,” 4 about an unsuccessful attempt to connect with some old friends. I often wonder if younger people understand the significance of the lyrics, including the phrase “You can keep the dime.”

Today, humanity is more connected than ever through the use of cell phones. However, mobile phones didn’t start in their current, sleek style. The first mobile phone by Motorola in 1983 5 was so big and heavy that it was nicknamed “the brick.” Current phones are significantly more lightweight and compact and have the capability to text, email, access social media, access the Internet, and much more.

According to recent surveys, 75% of the world’s population owns a cell phone. 6 , 7 Surveys in 2019 indicated that there were 5.11 billion unique mobile phone users, and that 2.71 billion of them used smartphones. People from China (> 782 million users) and India (> 386 million users) are the largest consumers of smartphones, followed by the United States (> 235 million users).

If you search for technological advances that facilitated progress to the current state of cell phone technology, you will find lists that include the Internet, global positioning systems, touch screens, cameras, high-speed modems, displays, batteries, and a host of other materials and technologies. 8 , 9

The computers that drive recent smartphones have 64-bit architectures. 10 , 11 They are usually fabricated as a system-on-a-chip and include multiple cores and extra features, such as neural engines and embedded motion coprocessors. They contain cameras with more than 10 megapixels and multi-element lens systems and include zoom capabilities and two-axis stabilization. The phones support a wide variety of standard communication protocols, including accessibility features for those who wear hearing aids. Recent smartphone microprocessors have been built with fin field-effect transistors (finFETs) 12 manufactured at the 10 nm, 7 nm, and 5 nm processing scales. They also include a range of sensors, including for facial identification, a barometer, a three-axis gyro, an accelerometer, a proximity sensor, an ambient light sensor, a Hall sensor, and a RGB light sensor. 10 , 11

These systems are also designed to take advantage of fifth-generation (5G) cell phone networks with advantages in bandwidth and data rates (eventually up to 10 Gbps). 13

Integrating even a fraction of these capabilities into the early Motorola mobile phone would have likely expanded the size, weight, and power requirements well beyond what one person could have easily carried. (As I write this, an image of a famous body-builder, Arnold Schwarzenegger, struggling to lift this enhanced “brick” popped into my head, as he was trailed by a large generator on wheels to power the phone.) This does not factor in the fact that many of these technologies did not exist at the time.

Microelectronics has evolved through a range of technologies and materials developments over the years 14 , 15 that have affected transistors (bipolar junction transistors, various metal–oxide–semiconductor field-effect transistors, including finFETs), dielectrics (thermal oxides, high- k dielectrics), metallization (aluminum, polysilicon, copper, tungsten vias), high levels of integration, including multilayer metallization, and integration of billions of transistors per chip. Fabrication of modern microchips involves many hundreds of process steps that have to be performed within narrow tolerances. It is remarkable that these fabrication lines yield in numbers high enough to be economically viable. If any step falls outside of the tolerances, then yield can fall catastrophically. This would kick off an investigation to determine the root cause(s) of the problem and can shut down fabrication lines for long periods of time—an expensive proposition. Developing these technologies and the processes that allow them to be inserted into high-yield fabrication lines have occupied hordes of materials researchers for decades.

I could write similar discussions of materials advances in batteries, displays, touch screens, and camera systems that have relied on similar hordes of materials researchers. However, I’m out of space for this article, so those stories will have to wait until another time.

The features described, the ease of carrying modern cell phones, and their economic affordability are driving the surge in worldwide usage. Access to information is only as good as the information. We are constantly bombarded with inaccurate information as well as disinformation. Filtering all of that can be difficult and time consuming. Instantaneous access to information using cell phone and other electronic technologies provides the unwary with an opportunity to make huge mistakes quickly.

The use of landline phones reached a peak in the 2000s. Now they are down to around 40% of American households and declining. 16 I am one of those neo-Luddites who has chosen to keep my landline. I find that, for now, it gives me some comfort to have it available.

Warts and all, the proliferation of cell phone systems is good. Widespread outages due to local events are unlikely to destroy all cell towers in a local community. Therefore, people are likely to maintain some capability for communication, even if impacted by tornados such as the one on January 3, 1949, in Warren, Ark.

The invention of the telephone - Ericsson

https://www.statista.com/statistics/189959/housing-units-with-telephones-in-the-united-states-since-1920/

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Operator (That's Not the Way It Feels) - Wikipedia

S. Wolpin, The first cellphone went on sale 30 years ago for $4,000. Mashable (2014); https://mashable.com/2014/03/13/first-cellphone-on-sale/

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Moss, S. From “brick” to smartphone: the evolution of the mobile phone. MRS Bulletin 46 , 287–288 (2021). https://doi.org/10.1557/s43577-021-00067-7

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Published : 05 March 2021

Issue Date : March 2021

DOI : https://doi.org/10.1557/s43577-021-00067-7

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Smartphone usage and increased risk of mobile phone addiction: A concurrent study

Subramani parasuraman.

Unit of Pharmacology, AIMST University, Kedah, Malaysia

Aaseer Thamby Sam

1 Unit of Pharmacy Practice, Faculty of Pharmacy, AIMST University, Kedah, Malaysia

Stephanie Wong Kah Yee

Bobby lau chik chuon.

This study aimed to study the mobile phone addiction behavior and awareness on electromagnetic radiation (EMR) among a sample of Malaysian population.

This online study was conducted between December 2015 and 2016. The study instrument comprised eight segments, namely, informed consent form, demographic details, habituation, mobile phone fact and EMR details, mobile phone awareness education, psychomotor (anxious behavior) analysis, and health issues. Frequency of the data was calculated and summarized in the results.

Totally, 409 respondents participated in the study. The mean age of the study participants was 22.88 (standard error = 0.24) years. Most of the study participants developed dependency with smartphone usage and had awareness (level 6) on EMR. No significant changes were found on mobile phone addiction behavior between the participants having accommodation on home and hostel.

Conclusion:

The study participants were aware about mobile phone/radiation hazards and many of them were extremely dependent on smartphones. One-fourth of the study population were found having feeling of wrist and hand pain because of smartphone use which may lead to further physiological and physiological complication.

INTRODUCTION

Mobile/hand phones are powerful communication devices, first demonstrated by Motorola in 1973, and made commercially available from 1984.[ 1 ] In the last few years, hand phones have become an integral part of our lives. The number of mobile cellular subscriptions is constantly increasing every year. In 2016, there were more than seven billion users worldwide. The percentage of internet usage also increased globally 7-fold from 6.5% to 43% between 2000 and 2015. The percentage of households with internet access also increased from 18% in 2005 to 46% in 2015.[ 2 ] Parlay, the addiction behavior to mobile phone is also increasing. In 2012, new Time Mobility Poll reported that 84% people “couldn't go a single day without their mobile devices.”[ 3 ] Around 206 published survey reports suggest that 50% of teens and 27% of parents feel that they are addicted to mobiles.[ 4 ] The recent studies also reported the increase of mobile phone dependence, and this could increase internet addiction.[ 5 ] Overusage of mobile phones may cause psychological illness such as dry eyes, computer vision syndrome, weakness of thumb and wrist, neck pain and rigidity, increased frequency of De Quervain's tenosynovitis, tactile hallucinations, nomophobia, insecurity, delusions, auditory sleep disturbances, insomnia, hallucinations, lower self-confidence, and mobile phone addiction disorders.[ 6 ] In animals, chronic exposure to Wi-Fi radiation caused behavioral alterations, liver enzyme impairment, pyknotic nucleus, and apoptosis in brain cortex.[ 7 ] Kesari et al . concluded that the mobile phone radiation may increase the reactive oxygen species, which plays an important role in the development of metabolic and neurodegenerative diseases.[ 8 ]

In recent years, most of the global populations (especially college and university students), use smartphones, due to its wide range of applications. While beneficial in numerous ways, smartphones have disadvantages such as reduction in work efficacy, personal attention social nuisance, and psychological addiction. Currently, the addiction to smartphones among students is 24.8%–27.8%, and it is progressively increasing every year.[ 9 ] Mobile phone is becoming an integral part to students with regard to managing critical situations and maintaining social relationships.[ 10 ] This behavior may reduce thinking capabilities, affect cognitive functions, and induce dependency. The signs of smartphone addiction are constantly checking the phone for no reason, feeling anxious or restless without the phone, waking up in the middle of night to check the mobile and communication updates, delay in professional performance as a result of prolonged phone activities, and distracted with smartphone applications.[ 11 ]

Mobile phone is the most dominant portal of information and communication technology. A mental impairment resulting from modern technology has come to the attention of sociologists, psychologists, and scholars of education on mobile addiction.[ 12 ] Mobile phone addiction and withdrawal from mobile network may increase anger, tension, depression, irritability, and restlessness which may alter the physiological behavior and reduce work efficacy. Hence, the present study was planned to study the addiction behavior of mobile phone usage using an online survey.

This study was approved by Human and Animal Ethics Committee of AIMST University (AUHAEC/FOP/2016/05) and conducted according to the Declaration of Helsinki. The study was conducted among a sample of Malaysian adults. The study participants were invited through personal communications to fill the online survey form. The study was conducted between December 2015 and 2016. The study instrument comprised eight segments, namely, informed consent information, consent acceptance page, demographic details, habituation, mobile phone fact and electromagnetic radiation (EMR) details, mobile phone awareness education, psychomotor (anxious behavior) analysis, and health issues. If any of the participants were not willing to continue in the study, they could decline as per their discretion.

Totally, 450 participants were informed about the study and 409 participated in the study. The demographic details of the study participants are summarized in Table 1 . The incomplete forms were excluded from the study. The participants' details were maintained confidentially.

Demographic details of the study participants

Statistical analysis

Frequency of the data was calculated and the data were analyzed using two-sided Chi-square test with Yate's continuity correction.

Totally, 409 individuals participated in the study, of which 42.3% were males and 57.7% were females, between the age group of 18 and 55 years. Nearly 75.6% of the respondents were between the age group of 21 and 25 years. The mean age of the study participants was 22.88 (standard error = 0.24) years. The study participants' demographic details are summarized in Table 1 .

About 95% of the study participants were using smart phones, with 81.7% of them having at least one mobile phone. Most of the study participants used mobile phone for more than 5 years. Around 64.3% of the study participants use mobile phone for an hour (approximately) and remaining use it for more than an hour. Nearly 36.7% of the study participants have the habit of checking mobile phones in between sleep, while 27.1% felt inconvenience with mobile phone use. Majority of the respondents were using mobile phone for communication purposes (87.8%), photo shooting (59.7%), entertainment (58.2%), and educational/academic purposes (43.8%). Habits of mobile phone usage among the study participants are summarized in Table 2 .

Habituation analysis of mobile phone usage

The study results indicate that 86.8% of the participants are aware about EMR and 82.6% of the study participants are aware about the dangers of EMR. The prolonged use/exposure to EMR may cause De Quervain's syndrome, pain on wrist and hand, and ear discomfort. Among the study participants, 46.2% were having awareness on De Quervain's syndrome, 53.8% were feeling ear discomfort, and 25.9% were having mild-to-moderate wrist/hand pain. Almost 34.5% of the study participants felt pain in the wrist or at the back of the neck while utilizing smartphones [ Table 3a ]. Many of the study participants also agreed that mobile phone usage causes fatigue (12% agreed; 67.5% strongly agreed), sleep disturbance (16.9% agreed; 57.7% strongly agreed), and psychological disturbance (10.8% agreed; 54.8% strongly agreed) [ Table 3b ]. The study participants were having level 6 of awareness on mobile phone usage and EMR.

Analysis of awareness of mobile phone hazards

The behavioral analysis of the smartphone usage revealed that 70.4% of the study participants use smartphone longer than intended and 66.5% of the study participants are engaged for longer duration with smartphone. Nearly 57.7% of the study participants exercise control using their phones only for specific important functions. More number of study participants (58.2%) felt uncomfortable without mobile and were not able to withstand not having a smartphone, feeling discomfort with running out of battery (73.8%), felt anxious if not browsing through their favorite smartphone application (41.1%), and 50.4% of the study participants declared that they would never quit using smartphones even though their daily lifestyles were being affected by it. The study also revealed another important finding that 74.3% of smartphone users are feeling dependency on the use of smartphone. The addiction behavior analysis data of mobile phone are summarized in Table 4 .

Addiction behavior analysis of mobile phone

The study results also suggest that female participants were having more awareness than male participants ( P < 0.001) [ Table 5a ] and were more dependent on smartphones than male participants ( P < 0.05) [ Table 5b ]. Female participants were ready to quit using smartphones, if it affected daily lifestyle compared with male participants ( P < 0.05) [ Table 5b ]. Habituation of mobile phone use and addiction behavior were compared between both genders of the study participants and are summarized in Table 5a and ​ andb, b , respectively.

Comparison of habituation of mobile phone usage between genders

Comparison of addiction behavior between genders

A total of 297 participants were having accommodation in hostel, among them 39.6% of the study participants checked their mobile phone on an average of 21–30 times, a day, and 11.7% of the study participants checked their mobile phone more than 30 times a day. A total of 112 participants have accommodation in home, among them 28.6% of the study participants checked their mobile phone 21–30 times a day, and 13.4% of the study participants checked their mobile phone more than 30 times a day.

A total of 66.1% of participants having accommodation in home use their phones longer than intended, whereas 71.8% of participants having accommodation in hostel are using phone longer than intended. Forty-one (36.6%) and 109 (36.6%) participants from home and hotel checked mobile phone in-between sleep, respectively. About 67.9% of participants having accommodation in home felt dependent on mobile and it was the same for participants having accommodation in hostel (76.5%).

The study results suggest that a significant number of the participants had addiction to mobile phone usage, but were not aware on it, as mobile phones have become an integral part of life. No significant differences were found on addiction behavior between the participants residing in hostel and homes. Mobile phone abuse is rising as an important issue among the world population including physical problems such as eye problems, muscular pain, and psychological problem such as tactile and auditory delusions.[ 13 ] Along with mobile phone, availability of Wi-Fi facility in residence place and work premises also increases mobile phone dependence. The continuous and constant usage of mobile phone reduces intellectual capabilities and work efficacy. A study conducted in Chinese population (160 million out of the total 1.3 billion people) showed that people affected by mobile phone dependence have difficulty in focusing on work and are unsociable, eccentric, and use phones in spite of facing hazards or having knowledge of harmful effects of this form of electromagnetic pollution.[ 14 ]

The statement “I will never quit using my smartphone even though my daily lifestyles are affected by it” was statistically significant ( P = 0.0229). This points to a trend of mobile phone addiction among the respondents. This finding was discussed by Salehan and Negahban. They stated that this trend is due to the fast growth in the use of online social networking services (SNS). Extensive use of technology can lead to addiction. The use of SNS mobile applications is a significant predictor of mobile addiction. Their result showed that the use of SNS mobile applications is affected by both SNS network size and SNS intensity of the user. It has implications for academia as well as governmental and non-for-profit organizations regarding the effect of mobile phones on individual's and public health.[ 15 ] The health risks associated with mobile phones include increased chances of low self-esteem, anxiety or depression, bullying, eye strain and “digital or mobile phone thumb,” motor vehicle accidents, nosocomial infections, lack of sleep, brain tumors and low sperm counts, headache, hearing loss, expense, and dishonesty. The prevalence of cell phone dependence is unknown, but it is prevalent in all cultures and societies and is rapidly rising.[ 16 ] Relapse rate with mobile phone addiction is also high, which may also increase the health risk and affect cognitive function. Sahin et al . studied mobile phone addiction level and sleep quality in 576 university students and found that sleep quality worsens with increasing addiction level.[ 17 ]

The statement “Feeling dependent on the use of smartphone” was also statistically significant ( P = 0.0373). This was also explored by Richard et al . among 404 university students regarding their addiction to smartphones. Half of the respondents were overtly addicted to their phones, while one in five rated themselves totally dependent on their smartphones. Interestingly, higher number of participants felt more secure with their phones than without. Using their phones as an escapism was reported by more than half of the respondents. This study revealed an important fact that people are not actually addicted to their smartphones per se ; however, it is to the entertainment, information, and personal connections that majority of the respondents were addicted to.[ 18 ]

The 2015 statistical report from the British Chiropractic Association concluded that 45% of young people aged 16–24 years suffered with back pain. Long-term usage of smart phone may also cause incurable occipital neuralgia, anxiety and depression, nomophobia, stress, eyesight problem, hearing problems, and many other health issues.[ 19 ]

A study conducted among university students of Shahrekord, Iran, revealed that 21.49% of the participants were addicted to mobile phones, 17.30% participants had depressive disorder, 14.20% participants had obsessive-compulsive disorder, and 13.80% had interpersonal sensitivity.[ 12 ] Nearly 72% of South Korean children aged 11–12 years spend 5.4 h a day on mobile phones, 25% of those children were considered addicts to smartphones.[ 20 ] Thomée et al . collected data from 4156 adults aged between 20 and 24 years and observed no clear association between availability demands or being awakened at night and the mental health outcomes.[ 21 ] Overuse of mobile phone can lead to reduced quality of interpersonal relationships and lack of productivity in daily life. The study outcome from different studies showed variable results on addictive behavior on mobile phone usage. The fact is over-/long-time usage of mobile phone may cause behavioral alteration and induce addictive behavior.

This study suggests that most of the study participants are aware about mobile phone/radiation hazards and many of them developed dependent behavior with smartphone. No significant changes were found on mobile phone dependency behavior between participants having accommodation in house and hostel. One-fourth of the study population is having a feeling of wrist and hand because of smartphone usage which may lead to further physiological and physiological complications.

Limitations

• Cluster sampling from a wider population base could have provided a more clear idea regarding the topic of interest
• Increasing the time frame and number of study phases was not possible due to logistical issues
• Impact of smartphone addiction on sleep pattern could have been studied in-depth.

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Conflicts of interest.

There are no conflflicts of interest.