Deprived of Willpower, Smartphone in Hand – The Effect of Self-Control on Smartphone Use, Its Context, and Consequences
Niklas Johannes 10602038 Master Thesis
Graduate School of Communication Communication Science (Research Master)
University of Amsterdam Supervisor: Dr. Dian de Vries
Abstract
With more and more people using their smartphones almost permanently, they require self-control to resist the constant temptations of their devices. With this article, we extended previous research on the relationship between self-control and smartphone use. Specifically, we conducted two studies to address three goals. First, we investigated in a survey (N = 219) what motivations relate to an experienced loss of control over smartphone use. Second, we looked at the causal relationship between low self-control and smartphone use (N = 52). Third, we examined guilt as a negative affective consequence of the effect of low self-control on smartphone use in the
experiment. Results of the survey showed that loss of control over smartphone use mediated the influence of escapism and fear of missing out on smartphone use. Using the phone to alleviate boredom was not related to a loss of control. In our experiment, we showed that low self-control had a positive effect on actual smartphone use. However, this effect was not moderated by the motivations identified in the survey. More important, there was no effect of smartphone use on guilt. These findings are an important step in applying self-control theory to smartphone use and understanding the mechanisms at play.
Deprived of Willpower, Smartphone in Hand – The Effect of Self-Control on Smartphone Use, Its Context, and Consequences
Libraries around the world have changed. Less than a decade ago, students would either work on their assignments or talk to each other. Today, this picture looks vastly different: It seems almost everyone reaches for her or his smartphone every couple of minutes. Close to 90% of young people in the Netherlands own a smartphone and use it daily (CBS, 2013); in the US, this number is similar (Pew Research Center, 2014, 2015). Especially students spend a
considerable amount of time on their phones; this phenomenon of constant interaction with smartphones has been termed “permanently online” (Vorderer & Kohring, 2013) or “constantly connected” (Harwood, Dooley, Scott, & Joiner, 2014), and many scholars and figures in the public debate regard it as defining of the current generation (Barret, 2012; The Daily Mail, 2013).
Just like laptops, smartphones offer a wide variety of functions, yet they are unique in that they are ubiquitous and flexible (Nielsen & Fjuk, 2010). That is, always at hand, smartphones provide immediate informational, social, and hedonic rewards (Lee, Chang, Lin, & Cheng, 2014; Oulasvirta, Rattenbury, Ma, & Raita, 2012). Interestingly, smartphones are seldom used to obtain information in support of a task in a school or work context; rather, most students use them for leisure (Lepp, Barkley, Sanders, Rebold, & Gates, 2013). Thus, students are not just permanently online, they are also permanently tempted to achieve pleasant states at all times. Resisting such temptations requires a crucial skill young adults have to acquire for a healthy development: self-control (Baumeister & Vohs, 2007; Ent, Baumeister, & Tice, 2015) or self-regulation (Bandura, 1991). Not giving in to the distractions smartphones present means actively exhibiting self-control by self-controlling one’s impulses.
Whereas quite a body of research so far has shown internet and general media use to be related to deficient self-control (e.g., Gámez-Guadix, Calvete, Orue, & Las Hayas, 2015;
Greenwood & Long, 2009; LaRose, Lin, & Eastin, 2003), few studies to date have investigated this relation for smartphones. Indeed, the few conducted on this topic so far show, on aggregate, that those low in self-control are also more likely to use their smartphones (e.g., Khang, Kim, & Kim, 2013; Lee et al., 2014). These findings are interesting, but they leave several important questions unanswered. First, no research so far has investigated what user motivations are related to a loss of control over smartphone use. Smartphones present users with temptations they need to control, but given smartphones’ broad functionalities it is plausible to assume they present different temptations for different users. Thus, it stands to debate what motivations that users hope to fulfill by using their smartphones (i.e., a tempting outcome) relate to their self-reported loss of control over smartphone use. Second, given the correlational nature of previous research, there is a need to investigate if low self-control can actually cause smartphone use.
Last, there is a need to understand the consequences of smartphone use as a result of self-control failure. If a user works at a task but instead gives in to her or his smartphone, a failure of self-control occurs. Prior research in the field of multitasking has shown that such a failure to allocate attention (i.e., exerting self-control by not giving in to a media distraction) can have negative effects on cognitive processing (e.g., Bowman, Levine, Waite, & Gendron, 2010; Lepp, Barkley, & Karpinski, 2014; Smith, Isaak, Senette, & Abadie, 2011). However, more recent investigations show that media use as a result of failed self-control is also related to negative affective reactions. Specifically, individuals low in self-control have been found to exhibit a feeling of guilt as a consequence of their media use (Meier, Reinecke, & Meltzer, 2015; Panek, 2014; Reinecke, Hartmann, & Eden, 2014).
Consequently, there is a need for research examining motivations related to a loss of control, the relationship between self-control and smartphone use, and what consequences this relationship entails. Advancing knowledge on this subject has both theoretical and practical
relevance. On the theoretical side, investigating contributors to a loss of control, the directionality of the relation between smartphone use and self-control, and the consequences of this effect tests the applicability of self-control theory to a timely and highly relevant phenomenon. On the practical side, evidence concerning motivations related to loss of control, the causal chain at play, and possible negative consequences also provides a leverage point for media literacy programs and interventions.
With this paper, we address those three issues regarding the relationship of self-control and smartphone use: First, we examine by means of a survey what motivations contribute to a perceived loss of control over smartphone use. Second, we test experimentally if low self-control can cause smartphone use. Third, we test experimentally if such a failure in self-regulation results in guilt.
Smartphones as Temptations
Smartphones can be considered temptations, because they are mostly used for enjoyable purposes, such as accessing pleasurable content or interacting with others. Various functions of smartphones enable users to easily pursue such enjoyable purposes, for example, accessing social networking sites (SNS), email, texts, instant messaging, videos, surfing, and so forth (Nielsen & Fjuk, 2010; Sultan, 2014). Contrary to self-reports of students, who claim they engage with digital media parallel to their school work for reasons of productivity and efficiency (Bardhi, Rohm, & Sultan, 2010; Wang & Tchernev, 2012), recent research shows that this is not the case: Instead, most students use their phones for leisure (Lepp et al., 2013), making smartphones permanent temptations while there might be other, more important tasks at hand, such as school work (David, Kim, Brickman, Ran, & Curtis, 2014; Ravizza, Hambrick, & Fenn, 2014). Social use seems to be especially tempting, as using smartphones for social motives has shown to be predictive of both mobile phone flow and addiction (Khang et al., 2013). In light of the easy and
flexible access to social interaction, the threshold to obtain social rewards is especially low, explaining why shy and lonely people have shown to be more likely to develop smartphone addiction (Bian & Leung, 2014). The social temptation of smartphones becomes even more obvious considering that also extraverted people with a high need for social recognition are drawn to them (Smetaniuk, 2014; Sultan, 2014). In their study, Khang and colleagues (2013) also provided evidence for smartphones as hedonic temptations, showing that using smartphones as pastime predicted mobile flow. Within a framework of technology acceptance, Chun, Lee, and Kim (2012) found that hedonic enjoyment of the smartphone was directly related to adoption intention, extending similar findings for instant messaging on computers (Gu, Fan, Suh, & Lee, 2010).
Whereas smartphone use can be pleasant, when asked to describe their devices, young Americans were very likely to call them distracting or frustrating (Pew Research Center, 2015). As smartphone use occurs mostly in the form of ritualistic checking habits, spread throughout the day (Oulasvirta et al., 2012), such habits can also result in a state of permanent vigilance, with the expectation of always needing to be online (King et al., 2013; Thomée, Dellve, Härenstam, & Hagberg, 2010). Consequently, smartphones offer pleasant states at all times and all locations, yet it seems they can be equally distracting, because they also provide need satisfaction in the face of more important tasks, so much that their mere presence can be distracting (Thornton, Faires, Robbins, & Rollins, 2014). Despite its obvious perks, “mobile communication amounts to having a temptation nearby in all places at all times” (Panek, 2014, p. 3).
Resisting Smartphones: Self-Control
If smartphones are temptations, one needs self-control to resist them. Self-control means the “capacity for altering one’s own responses, especially to bring them into line with standards such as ideals, values, morals, and social expectations, and to support the pursuit of long-term
goals” (Baumeister, Vohs, & Tice, 2007, p. 351). In the psychological literature, self-control and self-regulation are used almost synonymously, with some considering the exertion of self-control to be a deliberate, effortful subfunction of the general self-regulation process (Baumeister et al., 2007).
The self-regulatory process consists of several sub-processes. An individual has to develop personal standards against which actions and thoughts are held. To oversee if the behavior is actually in line with said standards, a person engages in self-monitoring, that is, observing her or his actions and thoughts (Bandura, 1991). If the action falls short of the
standard, the individual enters the operating stage; she or he has to exert self-regulatory strength to adjust the behavior to bring it in accordance with personal standards (Baumeister &
Heatherton, 1996). However, more recent theoretical work argues that individuals also must have the motivation to exert self-regulatory strength (Baumeister & Vohs, 2007). According to the strength model of self-control (Baumeister et al., 2007), self-regulatory strength, or willpower, constitutes a limited physiological resource which can be drained (Gailliot & Baumeister, 2007). All actions requiring exertion of self-control draw from that same resource, so that controlling oneself in one situation leads to a depleted stock of willpower in an entirely different situation. The individual then is in a state of ego depletion, a “temporary reduction in the self’s capacity or willingness to engage in volitional action . . . caused by prior exercise of volition” (Baumeister, Bratslavsky, Muraven, & Tice, 1998, p. 1253). The notion of self-control as energy has found empirical support in a variety of contexts (Hagger, Wood, Stiff, & Chatzisarantis, 2010; Vohs et al., 2008) and seems also vital with automatic processes that normally occur with little self-monitoring and minimal effort. When motivated to, individuals have to exert self-regulatory strength to inhibit the habitual response. According to Bandura’s (1991) social cognitive theory of self-regulation, when individuals fail to exert sufficient self-regulatory strength they cannot
engage in self-monitoring in order to bring their behavior and thoughts in accordance with personal standards. In such a situation, they violate their personal standards and experience negative affective self-reactions.
Applying this framework to smartphone use, whenever students feel an urge to give in to the temptations of smartphones whilst writing an assignment, they require self-control to inhibit the execution of said urge. Overriding this impulse should happen when the students have a clear standard and motivation to concentrate on the assignment, and if they self-monitor in order to live up to that standard. Every time they are tempted to give in to their smartphone, they need to exert self-regulatory power to resist. Should they be low in self-regulatory power, they are likely to violate their personal standards and experience negative affective reactions.
Deficient self-control has been shown to be strongly related to media use in general, notably because it is considered an essential part of media addiction (Block, 2008; Caplan, 2010; Davis, 2001). In their seminal study, LaRose and colleagues (2003) demonstrated deficient internet self-regulation to not only directly predict general internet use; self-regulation also had an indirect effect on internet use through strengthening habits. The authors argue that internet use at first is consciously controlled, but as it becomes habitual, self-monitoring becomes harder. The crucial role of self-control in managing media use becomes further apparent in an experience sampling study by Hofmann and Baumeister (2012), in which the authors showed that desire for media use was, among all reported everyday desires, the one people were least successful to control. For students, media desires were the most prominent among all groups, and in conflict with goals such as not delaying things or academic achievement. These findings are in line with other studies demonstrating a negative relationship between self-control and media use (Özdemir, Kuzucu, & Ak, 2014; Panek, 2014; Radesky, Silverstein, Zuckerman, & Christakis, 2014).
Unfortunately, only few studies have investigated specifically the relationship of
smartphone use and self-control. Trait self-control and self-monitoring, for instance, were shown to be a robust predictor of flow with (Khang et al., 2013) and addiction to mobile devices
(Khang, Woo, & Kim, 2012; Takao, Takahashi, & Kitamura, 2009). Both flow and addiction are per definition states in which no active control takes place. Furthermore, the less people felt they had control over their lives, the higher their compulsive smartphone usage (Lee et al., 2014). In a similar vein, if smartphones are temptations leading to habits, smartphone use can be regarded as an impulse insufficient self-control cannot inhibit. Indeed, those who could not control their impulses exhibited more problematic smartphone use (Roberts & Pirog, 2013; Smetaniuk, 2014) and cell phone use (Billieux, Van der Linden, D’Acremont, Ceschi, & Zermatten, 2007).
This was also the case for conscientiousness, a trait very close to self-control, as it describes individuals with high level of self-monitoring and self-discipline. Roberts, Pullig, and Manolis (2015) found conscientiousness to indirectly relate to cell phone addiction through attention impulsiveness, explaining why conscientious individuals experience less so-called phantom vibrations, an indicator of excessive smartphone use (Drouin, Kaiser, & Miller, 2012).
Given this body of evidence, it seems plausible to hypothesize a negative relationship between self-control and smartphone use. More specifically, we were interested in a self-reported loss of control over one’s smartphone use, in line with self-reports of users indicating intense feelings of losing control over their devices (Bardhi et al., 2010). We thus predicted that a reported loss of control over smartphone use would be positively related to smartphone use in a survey setting (H1a).
Furthermore, we were also interested in the direction of this relationship. Even though most empirical evidence implicitly assumes self-control to affect smartphone use, some recent investigations have also demonstrated a reverse mechanism (Lanaj, Johnson, & Barnes, 2014).
According to self-control theory (Baumeister et al., 2007), those low in self-regulatory energy should be less able to resist the temptation of their smartphones. Consequently, we investigated this relationship in an experimental setting, predicting that temporarily low self-control (i.e., ego depletion) would lead to smartphone use (H1b).
Motivations Predicting a Loss of Control
Smartphones are temptations; to resist temptations one needs self-control. Given their broad range of functionalities that can satisfy a variety of needs, it is likely smartphones present different temptations for different users. Hence, we were not only interested in the link between loss of control and smartphone use, but also in what tempting outcomes users are motivated to achieve by checking their smartphones. In other words, we also investigated what motivations contribute to a feeling of loss of control over smartphone use.
Boredom. Being motivated to alleviate boredom and thus giving in to the temptation of one’s smartphone should be related to a reported loss of control. Through their mobility and flexibility, smartphones are especially suited to alleviate states of boredom, defined as “the aversive state of wanting, but being unable to engage in satisfying activity” (Eastwood, Frischen, Fenske, & Smilek, 2012, p. 483). So far, alleviating boredom has been found to predict internet use if sought as a gratification (LaRose et al., 2003), or if experienced during leisure time (Lin, Lin, & Wu, 2009); also, motivations to pass time or alleviate boredom were related to general media use (Panek, 2014). For smartphones, it seems their temptation is strongest in ‘empty’ situations, where users cannot resist to escape boredom and seek entertainment (Oulasvirta et al., 2012). Such pastime as a motivation has shown to predict smartphone flow and smartphone addiction, both states involving a loss of control (Khang et al., 2013). Comparing different user types, the high use group was more susceptible to boredom during leisure media use, resulting in
increased distress (Lepp, Li, Barkley, & Salehi-Esfahani, 2015), which can be explained by a loss of control (Eastwood et al., 2012).
Analog to resisting smartphone temptations, self-control is needed to resist boredom. Hence, if people give in to the temptation of their phones, failing to endure boredom, they experience self-control failure, which should result in a reported loss of control. Therefore, we predict that using the smartphone to alleviate boredom is positively related to a reported loss of control over smartphone use (H2).
Escapism. Using smartphones to stave off boredom can also be understood as a “need for constant stimulation” (Bardhi et al., 2010, p. 323). Experiencing a lack of occupation means being alone with one’s (unpleasant) thoughts, so people experience an escapism motivation, a motivation to avoid real-world problems by engaging in media activity (Henning & Vorderer, 2001). Generally, turning to media to escape offline problems is inherently related to addictive use and thus to a loss of control; for instance, escapism has shown to predict addiction to SNS (Masur, Reinecke, Ziegele, & Quiring, 2014), video games (Li, Liau, & Khoo, 2011), or the internet in general (Whang, Lee, & Chang, 2003).
Smartphones are a tempting outlet to escape real-world problems: Always at hand, they not only allow for an escape route the very moment users encounter unpleasant thoughts; as has been shown, this escape route in the form of enjoyable content or social interaction is also very appealing. Hence, those routinely using the smartphone to alleviate a negative state “are more likely to repeat those actions as an escape from real life” (van Deursen, Bolle, Hegner, &
Kommers, 2015, p. 412). In other words, users permanently giving in to the temptation to escape offline problems by using their smartphones should experience their use as automatic and
uncontrolled. It is therefore plausible to assume those users will also report a loss of control over their smartphone if they use it out of an escapism motivation. Consequently, we hypothesize that
using the smartphone to escape offline problems predicts a feeling of losing control over smartphone use (H3).
Fear of missing out. The social functions of smartphones constitute a particularly appealing temptation, which is likely to be accompanied by a loss of control. Those relying on the internet for social purposes exhibit the highest likelihood to develop uncontrolled,
pathological use (Jia, 2012; Yang & Tung, 2007), including social networking sites (Masur et al., 2014). Unfortunately, very few studies have investigated this link for smartphones. Yet, a recent investigation found that individuals addicted to their smartphones were most tempted by their phone’s social functions (Lopez-Fernandez, Honrubia-Serrano, Freixa-Blanxart, & Gibson, 2014). Similarly, neurotic and extraverted individuals, that is, those trying hard to maintain their social relationships, displayed stronger emotions towards and dependence on text messages (Igarashi, Motoyoshi, Takai, & Yoshida, 2008).
In light of many reports from students of feeling a need to be permanently online and connected (King et al., 2013; Thomée et al., 2010), it is reasonable to ask if the appeal of smartphones stems less from a need to belong (Baumeister & Leary, 1995) or relatedness (Deci & Ryan, 2000), but rather from a fear of missing out (Przybylski, Murayama, DeHaan, &
Gladwell, 2013). Instead, smartphones can be understood as temptations, because they present an outlet for users’ motivation to stave off social exclusion (Filipkowski & Smyth, 2012). In an innovative quasi-experiment, Cheever, Rosen, Carrier, and Chavez (2014) had their class of students put their phones away and observed rising anxiety over time, especially for heavy users. The authors speculate that this anxiety was due to a fear of missing out and a feeling of
disconnect. In such a view, heavy users were unable to control thoughts about their phones. Consequently, we assume that those with a high fear of missing out use their phones with the motivation to avoid social exclusion, which becomes habitual and uncontrolled so much that
the unavailability of their phones leads to anxiety. We therefore hypothesize that fear of missing out predicts a feeling of loss of control over smartphone use (H4).
Guilt as a Consequence of Smartphone Use
When certain motivations contribute to a loss of control, and low self-control affects smartphone use, such use constitutes self-regulation failure. When self-regulation fails and people use media they experience aversive reactions; this occurs not just on a cognitive level (Van Cauwenberge, Schaap, & van Roy, 2014; van der Schuur, Baumgartner, Sumter, & Valkenburg, 2015), but recent investigations indicate possible affective reactions (Meier et al., 2015; Panek, 2014; Reinecke et al., 2014). According to Bandura (1991), if one has high personal standards for a task, a violation of those standards can lead to affective self-reactions. Hence, students can have a personal standard to concentrate on an assignment without getting distracted. But if they do not have sufficient energy in order to resist the temptation of their smartphone, using it means violating their standard. Checking their smartphone should thus lead to negative affect.
Work by Reinecke and colleagues (2014) suggests that one negative self-reaction might be guilt, the “dysphoric feeling associated with the recognition that one has violated a personally relevant moral or social standard” (Kugler & Jones, 1992, p. 318). The authors showed that ego depleted individuals were more likely to appraise television and video game use as
procrastination, which they reported to make them feel guilty. Their model has since been replicated and extended (Meier et al., 2015), supporting Panek’s (2014) findings who showed a negative relationship between self-control and students’ leisure media use as well as between leisure media use and guilt. Guilt could also explain why mobile phone involvement relates to depression and stress (Harwood et al., 2014): As more people experience self-regulation failure using their smartphones, they feel a loss of control and guilt, which leads to decreased well-being. Such a mechanism would be in line with the findings of Hefner, Sowka, and Possler
(2015) who demonstrated that it was not mere usage, but the feeling of loss of control over smartphone use that predicted decreased well-being in adolescents.
However, there are also counterarguments to such a mechanism. Xu, Bègue, and Bushman (2012) showed that ego depleted individuals actually displayed less guilt and less prosocial behavior, arguing that guilt is a higher-order process difficult to conduct when
resources for self-control are low (Baumeister, Stillwell, & Heatherton, 1994). Consequently, we aimed to test this mechanism, predicting that smartphone use as self-regulation failure would result in feelings of guilt, but only for those with high personal standards (H5).
Study 1
In study one we sought to examine which motivations were related to a loss of control over smartphone use, and if that loss of control would be related to smartphone use. We expected that boredom, escapism, and fear of missing out would predict loss of control over smartphone use, which we expected to predict actual smartphone use.
Method
Sample. Data were collected in May 2014 through an online survey as part of a
cooperation between three universities in the Netherlands, Germany, and the US on the topic of being permanently online in different countries. In the Dutch sample, 241 students of a subject pool participated in the survey for research credits, of which 92% finished; additionally, two influential cases were removed, leaving a total sample of (N = 219). There were no missing values in the data set, as the survey had forced entry. Participants were told that this survey was intended to measure their behavior of being permanently online (PO) or permanently connected (PC) with their smartphones. The final sample had an age range typical for students from 18 to 27 years, (M = 20.84, SD = 1.76), the majority was female (n = 176, 80%). All respondents owned a smartphone with a mobile internet connection.
Measures.
Smartphone use. Developed by the research group, at the beginning of the survey participants were asked about how frequently they would go online or get connected in various situations. They were instructed what was meant by going online (“i.e., reading, listening, or watching content from the internet, such as news, or searching for information online”) and getting connected to others (“i.e., exchanging messages with peers, fellow students, friends, and/or family, or other people using Facebook, Twitter, WhatsApp, SMS, speaking with them on the phone etc.”). Participants were then asked for 13 different situations (e.g., “When I’m sitting in a classroom or lecture”, “When I am waiting for someone (e.g., a friend) or something (e.g., the bus”) to rate on 5-point Likert-scales from 1 (never/very rarely) to 5 (very frequently) how often they would go online (M = 2.63, SD = .55), or get connected (M = 2.80, SD = .44). Internal consistency of the PO scale was satisfactory (α = .83); consistency of the PC scale was sufficient (α = .74). The combined measure of POPC, or smartphone use, displayed satisfactory internal consistency (M = 2.71, SD = .45, α = .87).
Boredom. Using the smartphone to alleviate boredom was measured with the item “I often go online or get connected when I am bored or have nothing better to do”, which was rated on a scale from 1(strongly disagree) to 7 (strongly agree; M = 6.05, SD = 1.01).
Escapism. Using the smartphone in order to escape unpleasant thoughts or offline problems was an amended item used in previous research for problematic internet use (Lortie & Guitton, 2013), asking participants to rate the statement “Being online or connected lets me forget some of the offline problems I have” on a scale from 1(strongly disagree) to 7 (strongly agree; M = 3.31, SD = 1.79).
Fear of missing out. Participants indicated their fear of missing out on friends and peers by responding to a ten-item scale (Przybylski et al., 2013). They rated items such as “I fear others
have more rewarding experiences than me” or “I get anxious when I don’t know what my friends are up to” on a scale ranging from 1 (not at all true of me) to 5 (extremely true of me; M = 2.53, SD = .72). Internal consistency of the scale was satisfactory (α = .85).
Loss of control. Loss of control over smartphone use was measured amending one item from an established measure of problematic mobile phone (Bianchi & Phillips, 2005), reflecting a dimension of finding it difficult to control one’s mobile phone use. The item was formulated to fit into the context of the survey (“I find it difficult to control if I’m online or connected”), and rated on a scale from 1 (strongly disagree) to 7 (strongly agree). A loss of control was fairly common in our sample (M = 3.36, SD = 1.56), with a median of three and 26% scoring above the mid-point of the scale.
Results
We tested our hypothesized partially latent structural regression model using AMOS 20, employing maximum likelihood estimation. Because boredom, escapism, and loss of control were one-item measures, we included them as observed, rather than latent variables. We also included smartphone use as an observed measure, as we did not regard it as a latent construct of, for instance, smartphone vigilance or a status, but as a measure of actual smartphone use as indicated by respondents. Zero-order correlations for all variables included in the model can be found in Table 1. Our data proved to substantially deviate from a normal multivariate distribution (multivariate critical ratio = 5.49, multivariate kurtosis = 15.72). Therefore, we re-estimated the model with ninety-five percent bias-corrected confidence intervals for all parameters based on 2000 bootstrap samples (Kline, 2010).
Following recommendations by Kline (2010), we first estimated a measurement model with covariances between all variables. Following the modification indices, several error
slightly exceeded conventional thresholds, but still indicated acceptable fit (χ2 (65) = 134.349, p = .00, χ2/df = 2.07, CFI = .93, RMSEA = .070, CI90%[.05, .09]). Next, we replaced all
covariances between exogenous and endogenous variables with effects, and tested with chi square-difference tests if the non-hypothesized effects could be dropped. The final model (see Figure 1) had acceptable fit (χ2 (67) = 137.31, p = .00, χ2/df = 2.05, CFI = .93, RMSEA = .069, CI90%[.05, .09]), although it must be interpreted with caution, as indicated by the significant chi-square statistic.
Supporting H1a, we found a positive relationship between loss of control and smartphone
use (β = .21, p < .01). Contrary to our expectations, there was no significant association between using the phone when bored and loss of control (β = .10, p = .09), failing to support H2. As we
predicted, there was a positive relationship between escapism and losing control (β = .28, p < .001), supporting H3, and a positive relationship between fear of missing out and losing control (β
= .25, p < .01), supporting H4. Interestingly, one additional path could not be dropped from the
model without significantly deteriorating model fit: There was a direct positive relationship between fear of missing out and smartphone use (β = .21, p < .01). Thus, the relationship between fear of missing out and smartphone use was partially mediated by loss of control.
Discussion
The aim of this first study was to investigate which user motivations were related to loss of control over their smartphones, and if a loss of control was related to smartphone use. We predicted those people who use their smartphone to alleviate boredom (H2), to escape problems
(H3), and those high in fear of missing out (H4) to report higher loss of control. Results were
largely consistent with our predictions. In line with previous research relating escapism with pathological media behavior (Li et al., 2011; Masur et al., 2014; Whang et al., 2003), those using their phones to escape offline problems reported a higher loss of control. Consistent with
previous research in the context of the internet (Jia, 2012; Yang & Tung, 2007), it appears users high in fear of missing out (i.e., who were tempted to stave off alleged social exclusion) were at higher risk to report a loss of control over their smartphone. Contrary to our expectation, there was no relationship between using the smartphone out of boredom and loss of control.
As predicted, loss of control was moderately related to self-reported smartphone use, consistent with previous research (Khang et al., 2013; Lee et al., 2014). In accordance with self-control theory (Baumeister et al., 2007), those feeling they could not self-control their use also
reported higher smartphone use. Furthermore, there was also a direct relationship between fear of missing out and smartphone use. In addition to our expectation, those trying to keep up with their friends used their phones more, not just because they experienced a loss of control.
Study 2
In study one we sought to find motivations related to a loss of control and investigate if there was a relationship between self-reported loss of control and smartphone use. However, to assess the directionality of this relationship and its consequences, we tested if there was a causal effect of low self-control on actual smartphone behavior. In addition, it has recently been argued that motivations play a crucial role in the allocation of self-regulatory strength (Baumeister & Vohs, 2007). If people are motivated to inhibit a behavior, they are more likely to succeed at dispensing the necessary volitional energy. Conversely, if they are generally motivated to give in to a temptation for its alleged rewards, they should be less able to allocate the necessary self-regulatory strength to resist the temptation. This should be especially the case if they are already ego depleted, even if they are in a situation where smartphone use is deemed inappropriate. As we showed in study one, people trying to fulfill certain motivations were more likely to
experience a loss of control over their smartphone. Thus, we hypothesized that those motivations would also function as a moderator of the effect of low self-control on smartphone use
(Valkenburg & Peter, 2013). Specifically, we expected that for those motivated to use their phones to stave off escapism (H6) as well as for those high in fear of missing out (H7) the effect
of low self-control on smartphone use would be stronger. In addition, we investigated the
question of affective consequences of self-control failure through smartphone use, predicting that smartphone use would lead to guilt for those with high personal standards.
Method
Sample. A total of 66 students participated in the experiment for either credit or monetary compensation. Fourteen cases were excluded from the analysis because they did not have their smartphone with them, or because they indicated afterwards that their smartphone was out of battery or in flight mode, resulting in a final sample of N = 52. Participants had an age typical for students (M = 22.77, SD = 3.37), and the majority of the sample was female (n = 41, 79%).
Design and procedure. In order to test our hypotheses, we conducted a between-subjects experiment with two conditions (ego depletion vs. control). Upon arrival at the laboratory, were seated in a spacious room with two tables, two computers, and a divider between the tables so participants could not see each other. At each corner of the room was a visible video camera. To reduce suspicion, participants were informed that there had been several break-ins in this and adjacent rooms, and that all rooms would therefore be constantly taped for security reasons. Also, as part of the cover story, we told participants that there had been technological problem with the survey, but that we did not want participants to run through the lab to get the experimenter should the problem occur again. We then told them they could put their phone on the table and let the experimenter’s number ring once or twice. Participants were told the experimenter would then check the problem immediately. This was done to make sure participants had their phones with them and in close proximity. Subsequently, they were told they were to take a career aptitude test, developed by a reputable university, which so far had been quite accurate in predicting people’s
income and GPA two years later. Participants were told they would be informed about their results two weeks after the test. This was done for both groups in order to raise personal relevance, corresponding to what Bandura (1991) termed personal standards.
Participants were instructed that the test would consist of two parts: In the first part they would watch a video of a woman (the manipulation), of whom they had to form an impression. In the second part they would solve different logical problems (the actual task), followed by a questionnaire about how they experienced the test (the measures). The actual task consisted of six logical problems commonly found in IQ-tests, such as detecting rules or derivation of logical conclusions from a diagram. The task lasted around 15 minutes; the whole procedure lasted around 30 minutes. The whole experiment was recorded with the four video cameras in the room. After participants were done, they were debriefed and received their compensations.
Stimulus. To manipulate situational self-control we used an ego depletion stimulus
successfully employed in previous research (Masicampo & Baumeister, 2008; Muraven, Shmueli, & Burkley, 2006; Schmeichel, Vohs, & Baumeister, 2003). Participants watched a six-minute video (without sound) of a woman being interviewed. The video was framed as the social part of the test; participants were to make an impression of her and were asked questions regarding her personality afterwards. At the bottom right corner of the video, a series of common words (e.g., ‘cut’) appeared for ten seconds each. Those randomly assigned to the ego depletion group were instructed to ignore the words, and should they catch themselves looking at them, to immediately redirect their gaze to the woman. This task requires self-control, because participants have to inhibit the natural impulse to pay attention to novel stimuli occurring in the environment. The control group was not given any instructions and thus free to follow their impulses and read the words.
Measures.
Smartphone use. Because we were interested in actual behavior, the whole procedure was recorded and subsequently coded into dichotomous smartphone checking variables (did check/did not check) during the task (n = 6), during the questionnaire (n = 17), right after the experiment before leaving the room (n = 15), and one composite checking variable (n = 25). Checking was recorded only if it was unambiguous that participants did not check their smartphones in support of the task. That is, it was not recorded if right after checking participants filled something in or were actively looking something up.
Fear of missing out. Fear of missing out was assessed with the same scale as in study one (Przybylski et al., 2013). Again, internal consistency was satisfactory (M = 2.38, SD = .70, α = .82).
Escapism. Escapism was assessed with the same item as in study one (M = 2.44, SD = 1.74).
Guilt. Guilt was measured with an adapted version of the Guilt Inventory (Jones,
Schratter, & Kugler, 2000; Kugler & Jones, 1992). We adapted the reference of the items from a state feeling for a short period of days to the feeling right now concerning the test. For instance, we modified the original item “I have recently done something that I deeply regret” to “During the test, I have done something that I regret”. Participants rated ten items on a scale (M = 3.00, SD = 1.01) from 1 (strongly disagree) to 5 (strongly agree). Internal consistency of the scale was satisfactory (α = .86).
Personal standards. To measure personal standards for doing well in the test, we adapted the personal standards subscale from the Frost multidimensional perfectionism scale (Frost, Heimberg, Holt, Mattia, & Neubauer, 1993; Frost, Marten, Lahart, & Rosenblate, 1990). For instance, we modified the original item “I have extremely high goals” to “For the test, I had
extremely high goals”. Participants rated seven statements (M = 4.35, SD = 1.02) on items ranging from 1 (strongly disagree) to 7 (strongly agree). Internal consistency was sufficient (α = .84).
Control variables. As there are individual differences between people with regards to their amount of self-control, we controlled for state self-control (de Ridder, Lensvelt-Mulders, Finkenauer, Stok, & Baumeister, 2012). State self-control was measured with the brief version of the validated self-control scale (Tangney, Baumeister, & Boone, 2004). The scale contains 13 items, (e.g., “I often act without thinking through all of the alternatives” or “People would say that I have iron self-discipline”), which participants could rate on a scale from 1 (not at all) to 5 (very much; M = 2.99, SD = .64). The scale displayed satisfactory internal consistency (α = .84). In addition, we asked participants how tired they were before (M = 3.23, SD = 1.63) and after the test (M = 3.69, SD = 1.76) and how hungry they were (3.27, SD = 1.69) on 7-point scales, and how many hours of sleep they had gotten the night before (M = 7.29, SD = 1.66).
Manipulation checks. To test if the ego depletion manipulation worked, we employed two items (“How much were you fighting an urge on that task?” and (“How much did you have to control yourself during that task?”) used in previous research (Muraven et al., 2006).
Participants could rate the items on a scale from 1 (not at all) to 7 (very much). Results
Randomization check. Randomization was successful; entering several control variables into a MANOVA with ego depletion as independent variable yielded no significant differences (F (4, 47) = .47, p = .76, Wilk’s Λ = .962, partial η2 = .04) with regards to age, tiredness before and after the test, hunger, or hours of sleep participants had the night before. Also, there was no significant difference between groups with regards to gender distribution (χ2 (1, N = 52) = 1.35, p
Manipulation checks. Manipulation checks indicated that the manipulation was not successful. The ego depletion group (M = 3.15, SD = 1.49) did not indicate to fight an urge to a significantly greater extent than the control group (M = 3.32, SD = 1.70, t (50) = .40, p = .70), nor did the ego depletion group indicate to exert more control (M = 4.41, SD = 1.87) than the control group (M = 3.92, SD = 1.91, t (50) = –.93, p = .36). Given that the manipulation had been
successfully employed in previous research (Masicampo & Baumeister, 2008; Muraven et al., 2006), the nonsignificant manipulation checks came as a surprise. This might have to do with the rather small sample or possibly reactance, because the ego depletion stimulus and the subsequent manipulation check items were too obvious. Nevertheless, we proceeded with the analysis to see if the manipulation worked on a behavioral level in contrast to self-reports.
Effects on smartphone use. There was no effect of ego depletion on whether participants checked their phones during the task (χ2 (1, N = 52) = .59, p = .44), most likely due to the overall low occurrence of checking during the task (n = 6). However, there was an effect of ego depletion on smartphone checking for the procedure as a whole, that is, if participants checked their phones overall after the manipulation (χ2 (1, N = 52) = 4.99, p = .03), supporting H1b. See Table 2 for the
frequency distribution. To further look into the effect and include possible moderators and control variables, we conducted three logistic regression models (see Table 3). As model 1 demonstrates, those in the ego depleted condition had higher log odds to check their phone compared to those in the control condition (b = 1.29, SE = .59, p < .05). In terms of odds ratios, those in the ego depletion condition had 261% higher odds to check their phones during the whole procedure compared to those in the control group (b = 3.61, SE = 2.15), p < .05).
Inspecting the predicted probabilities sheds more light on the nature of the effect: At average trait self-control, those in the control condition had a predicted probability to check their phones of
.32, whereas those in the ego depletion condition had a predicted probability more than twice as large (.68).
In addition, we tested if the significant predictors from study one would also function as a moderator, such that the effect of ego depletion on smartphone checking would be stronger for those high in fear of missing out and escapism. However, as models two and three display, there was no moderating effect. Neither the main effect of escapism (b = .12, SE = .23, p = 61) nor the interaction term with ego depletion (b = –.31, SE = .36, p = .72) reached significance, failing to support H6. Similarly, neither the main effect of fear of missing out (b = –.33, SE = .82, p = .69)
nor the interaction term with ego depletion (b = –.40, SE = .96, p = .97) reached significance, failing to support H7.
Effects on guilt. Addressing H5, we investigated if those who had taken out their
smartphones during the test would also exhibit stronger feelings of guilt if they had high personal standards to do well in the test. To be able to run a moderated-mediation model, we recoded the material to duration in seconds participants checked their phones during the task (M = 3.46, SD = 14.03). Unfortunately, very few people took out their phones during the task (n = 6), and those who did (M = 2.92, SD = .72) did not display more guilt than those who did not (M = 3.01, SD = 1.04, t(50) = .20, p = .84). Subsequently, we ran a moderated mediation model using PROCESS (Hayes, 2013), model 58, testing if the manipulation would lead to more smartphone checking time during the task, and if that time, moderated by personal standards, would have an effect on guilt. Consistent with the previous analysis, this was not the case: There was no effect of ego depletion on time checking during the task (b = –9.60, SE = 9.63, 95% CI [–28.97, 9.76), t(4, 47) = –1.00, p = .32) and no interaction effect of time checking during the task and personal
Discussion
With the second study we aimed to extend the results of study one. Specifically we were interested in three extensions: First, we examined the directionality of the relationship found between loss of control and smartphone use. Second, we investigated if that relationship would be affected by the motivations identified. And last, we looked at possible affective consequences of the effect of low self-control on smartphone use. We found support for our prediction that low self-control would cause smartphone use (H1b). Consistent with self-control theory (Baumeister et
al., 2007), once participants were ego depleted, they had less willpower to resist the temptation of their smartphones, checking them more often than those who were not ego depleted. However, this was only the case for smartphone checking overall, not during the task.
Furthermore, our results suggest that the motivations identified in study one did not act as a moderator of the effect of self-control on smartphone checking (Valkenburg & Peter, 2013). We investigated if especially those with high levels of escapism (H6) and a high fear of missing
out (H7) would take out their phones when low in self-control. This was not the case, neither
motivation strengthened the effect of ego depletion on smartphone use. Further, there was also no main effect of those two variables, indicating they were not related to actual smartphone
behavior.
Last, we did not find any affective consequences of smartphone checking, neither directly nor for those with high personal standards for the test. Apparently, smartphone use as a result of ego depletion did not violate participants’ personal standards, leading to guilt. However, only six participants took out their phone during the task, limiting the validity of such a conclusion.
General Discussion
With this paper, we set out to address three issues regarding the relationship between smartphone use and self-control. First, we investigated what motivations that users have for using their phones contributed to a loss of control over smartphone use. Second, we tested the direction of the relation of control and smartphone use Third, we studied the consequences of self-control failure through smartphone use.
Answering the question of what motivations contribute to a feeling of loss of control over smartphone use, we identified escapism and fear of missing out as two important predictors. Consistent with previous research, which showed escapism to predict media addictions (Li et al., 2011; Masur et al., 2014; Whang et al., 2003), those trying to escape offline problems were also more likely to experience a loss of control over their smartphone. Just as with pathological use, where loss of control plays a central part, repeatedly giving in to the temptations of smartphones seems to be linked to decreased control over the behavior. As LaRose and colleagues (2003) reasoned for the case of the internet, giving in to the temptation of the smartphone because one aims to escape offline problems might at first be a conscious, controlled decision process. Yet, as the mechanism repeats itself and users are permanently presented with opportunity to escape (Oulasvirta et al., 2012; Thornton et al., 2014), smartphone use eludes conscious control.
A similar mechanism could explain why fear of missing out was related to a loss of control. In line with previous research relating the social functions of media to pathological use of smartphones (Lopez-Fernandez et al., 2014) or the internet (Jia, 2012; Yang & Tung, 2007), those tempted by their phone’s capability to keep up with friends and peers were more likely to lose control over their devices. Whereas individuals first fulfill their motivation to stave off alleged social exclusion by consciously using their smartphones, such a behavior may soon become automatic; users thus permanently alleviate their fear by being constantly connected
(Cheever et al., 2014). This would also explain why our results suggest that the motivation to always suspend the negative state of wanting to keep tabs on friends directly relates to a felt loss of control, even though we did not hypothesize such a direct path.
However, an interpretation of motivations for smartphones to alleviate aversive states does not hold for boredom, equally considered an aversive state (Eastwood et al., 2012). A motivation to check the phone out of boredom was extremely common in our sample (M = 6.05 on a seven-point scale), yet it was not related to a reported loss of control as we predicted. This finding is somewhat in contrast to previous research, showing that heavy users were more susceptible to boredom during leisure time (Lepp et al., 2015). One explanation for the nonsignificant relationship might be people’s (lack of) motivation to endure boredom. As Eastwood and coworkers argue (2012), only if people are motivated to endure boredom, they require self-control; if they then give in to the temptation of the smartphone, despite their motivation not to, they should experience a lack of control and distress. However, using the smartphone out of boredom was so prevalent in our sample, it stands to reason participants were not motivated to endure boring situations. Rather, the opposite might be more plausible:
Participants actually considered use out of boredom as beneficial, or at least as customary. These findings shed more light on the nature of smartphones and how they relate to self-control. Apparently, smartphones constitute no uniform temptation, but gratify different needs for different users. Giving in to those temptations, however, was related to experiencing a loss of control.
Addressing our second question of the causal relationship of self-control and smartphone use, we found not only a positive relation between loss of control and smartphone use, giving credence to the rationale of smartphones as temptations. In our experiment, we could further demonstrate that low self-control had an effect on actual behavior: Ego depleted participants took
out their phones significantly more often than non-depleted ones, even though phone use in the laboratory is strongly forbidden. This effect lends further support to a conceptualization of smartphones as temptations, which need exertion of willpower to be resisted. Consistent with self-control theory (Baumeister et al., 2007), those low in volitional control were not able to resist their smartphones, whereas those with sufficient self-control could muster enough willpower to withstand using their phones.
Extending our findings from study one, we also tested the identified predictors of loss of control as moderators (Valkenburg & Peter, 2013). However, the effect of low self-control was not stronger for those with an escapism motivation, nor for those high in fear of missing out. Apparently, ego depletion, a state, did not have an effect on smartphone use in interaction with the two motivations, which can be considered relatively stable. The lack of moderating influence could have several reasons. One reason could be the setting: Students might have been
concentrated to fare well in the test, thus blinding out unpleasant thoughts, feelings, or thoughts about trying to keep in touch with their friends. Furthermore, participants only spent around 30 minutes in the laboratory; it is thus plausible to assume there were not enough moments where participants were confronted with unpleasant thoughts or offline problems. Similarly, this might not have been long enough for them to fear missing out on their friends and peers. Another reason could be methodological, notably that a relationship between lack of control and motivations might manifest itself in a survey setting, when people reflect on their natural motivations and general behaviors. In contrast, it might well be that such motivations did not impact the behavior short-term in the non-natural setting of an experiment.
Furthermore, ego depletion only had an effect on overall smartphone checking after the manipulation, but not during the task, during which very few participants used their phones. There are two explanations for this finding. On the one hand, the task might have been ego
depleting in its own rights, because it was intellectually draining (Schmeichel et al., 2003). In this view, participants reached low levels of self-control only after both the ego depletion
manipulation and the taxing task (Baumeister et al., 2007; Hagger et al., 2010). In combination, then, they were too depleted to resist checking their phones. On the other hand, participants might have been very motivated to achieve a good score in the task, given that it allegedly was
predictive of their future success, thus conserving as much willpower as they could. In this view, they anticipated a taxing task and exerted less self-control during the manipulation. This way, they conserved some of their energy, consistent with the strength model of self-control (Baumeister et al., 2007; Muraven et al., 2006). Following such an argumentation, students should increasingly cease to resist the temptation of their smartphones the more depleted they become during the day, as they carry out effortful tasks. This would be in line with studies showing that after work, at the end of the day, many employers experience a state of ego depletion (Reinecke et al., 2014; Trougakos, Beal, Cheng, Hideg, & Zweig, 2015).
With our last question, we addressed the possible consequences of self-control failure through smartphone use. Specifically, we investigated if smartphone use during a task for which users had high personal standards would violate those standards, leading to guilt. We failed to find support for this assumption; there was no significant difference in guilt between those who had used their phones during the task and those who had not. The absence of an effect was very likely due to a methodological reason: Out of 25 participants who used their phones during the whole procedure, only six did so during the task. The skewed distribution might then be responsible for the absence of the hypothesized effect.
In addition, there are also theoretical explanations for this null finding. Guilt requires reflection and insight about one’s behavior (Kugler & Jones, 1992; Xu et al., 2012); to engage in reflection, however, requires a person to engage in self-monitoring, part of the self-regulation
process (Bandura, 1991). But as participants had already been exposed to a taxing manipulation and/or spent effort on a task, they were likely to be depleted of the energy it takes to self-monitor. Hence, they were unable to engage in the reflection necessary to experience guilt. Consistent with such an argumentation, evidence linking ego depletion, media use, and guilt has been collected in survey settings, where people have enough time to reflect about their general behavior (Panek, 2014; Reinecke et al., 2014). Furthermore, media use was only related to guilt when appraised as procrastination (Reinecke et al., 2014). Whereas procrastination involves the violation of a standard to do something more important than the media activity, more recent evidence
demonstrates that this mechanism can also be countered by self-forgiveness (Meier et al., 2015). According to this line of reasoning, participants in our experiment might have also quickly forgiven themselves for their smartphone checking, possibly because they considered it as habitual or even rewarding in a taxing situation. Alternatively, personal standards to do well in the test might not have had an impact if users were not motivated to conform to them (Baumeister & Vohs, 2007). Given that users indicated their personal standards after their performance, we did not capture their motivation to adhere to those standards.
Despite our multi-method approach to the subject, there are several limitations which should be addressed by future studies. For both studies we relied on student samples; this made sense considering that the younger generation uses smartphones the most (CBS, 2013), yet it limits the generalizability of those findings. Future research should aim to replicate these findings with representative samples. Also, for our survey we had to rely on short versions of the
measurement instruments. As a consequence, more research is needed to replicate our findings with more extensive measures. Whereas our study was the first to show an effect of low self-control on smartphone use, we were only able to address one specific part of this relationship. As previous research has shown, in a different context smartphone use can also have an effect on ego
depletion (Lanaj et al., 2014). Therefore, more research is needed investigating the relationship over time. Furthermore, we were not able to address the consequences of self-regulation failure through smartphone use. Using a combination of methods, such as a combination of experience sampling and diaries, would provide a clearer picture. Last, more qualitative work about how people experience being permanently online and connected is sorely needed to gain a deeper understanding of the processes at work.
Concluding, smartphones have become almost omnipresent in today’s life, but rather little is known about what makes people use them for what purposes, and with what consequences. As we have shown, many people are motivated to use them to alleviate negative states, but using them in such a way is related to a loss of control over their device. Moreover, a loss of control is not only related to smartphone use, within our setting we could also demonstrate an effect of low control on smartphone checking. Overall, with the present study we were able to apply self-control theory to a phenomenon of high societal importance. Further investigating the
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