Journal Article Summary # 6: Due: 4/26/20

Who’s Addicted to the Smartphone and/or the Internet?

Bernd Lachmann Ulm University

Éilish Duke University of London

Rayna Sariyska Ulm University

Christian Montag Ulm University and University of Electronic Science and

Technology of China

Over the past few years, a growing amount of research has considered the links between personality and overuse (pathological use) of the Internet. Given the partial overlap between problematic Internet and smartphone use (PIU and PSU, respectively), the present study seeks to investigate whether the same personality traits can be linked to overuse of both platforms. A total of 612 participants (177 males/435 females, mostly students) completed questionnaires assessing both PIU and PSU, and the NEO Five Factor Inventory (NEO-FFI) to measure the Five-Factor Model of Personality and the Self-Directedness scale of the Temperament and Character Inventory. Our results indicate the existence of a common personality structure linked to both PIU and PSU. Interestingly, the associations between personality and PIU were higher than those concerning PSU. Low Self-Directedness appears to be the best predictor of overuse on both digital platforms. Therefore, lower willpower anchored in the personality trait Self- Directedness may reflect the core of digital additive tendencies.

Public Policy Relevance Statement The present study suggests the presence of a common personality structure linked to both problematic Internet use and problematic smartphone use. In this regard especially, low Self-Directedness seems to be the best predictor of problematic digital use.

Keywords: personality, Self-Directedness, Internet addiction, smartphone addiction

The study of problematic smartphone use (PSU) is much younger than that of its sibling, problematic Internet use (PIU; among others, originating in the work of Young, 1998b). This is understandable, as the first commercially successful smartphone is a relatively recent introduction, originating with the launch of the Apple iPhone in 2007 by Steve Jobs. Since then, the smartphone has become a runaway success. Nearly 2 billion people worldwide currently own a smartphone (cited by Miller, 2012), and people use this powerful technical device for many daily tasks including

surfing the web, navigating a new city, communicating via classic phone calls, short message services, or newer communication channels such as Whats-App and Facebook. Given the many advantages of smartphones, it is important not to (over-) patholo- gize everyday life, including smartphone usage (e.g., see the discussion of problematic Internet use by Kardefelt-Winther, 2014). Nevertheless, a growing body of research suggests the existence of a dark side of smartphone usage (Lee, Chang, Lin, & Cheng, 2014; Montag, Kannen, et al., 2015), with some work even highlighting its potentially addictive nature (Duke & Montag, 2017a; Kwon, Kim, Cho, & Yang, 2013; Kwon, Lee, et al., 2013; Lin et al., 2015).

From this perspective, one can distinguish between generalized (addictive behavior to the Internet in general) and specific (ad- dicted to an application on the Internet) Internet addiction (Brand, 2017). Davis (2001) points out that individuals suffering from generalized Internet addiction could not have developed their dysfunctional behavior (e.g., shopping, gambling, etc.) without the Internet, that is, the problematic Internet use itself determines subsequent specific problem behaviors. On the other hand, indi- viduals suffering from specific Internet addiction are using the Internet only as instrument to satisfy their needs (e.g., shopping, gambling, and gaming) but are not dependent on the Internet per se. The same problematic behavior could exist in the real world, outside of cyberspace. The phenomenon of social or peer pressure

This article was published Online First November 20, 2017. Bernd Lachmann, Institute of Psychology and Education, Ulm Univer-

sity; Éilish Duke, Department of Psychology, Goldsmiths, University of London; Rayna Sariyska, Institute of Psychology and Education, Ulm University; Christian Montag, Institute of Psychology and Education, Ulm University, and Key Laboratory for NeuroInformation/Center for Informa- tion in BioMedicine, School of Life Science and Technology, University of Electronic Science and Technology of China.

The position of CM is funded by a Heisenberg grant, awarded to him by the German Research Foundation (DFG, MO2363/3-2). Moreover, the study is funded by a grant on computer and Internet gaming awarded to CM by the German Research Foundation (DFG, MO2363/2-1).

Correspondence concerning this article should be addressed to Christian Montag, Institute of Psychology and Education, Ulm University, Helm- holtzstr. 8/1, 89081 Ulm. E-mail: [email protected]

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Psychology of Popular Media Culture © 2017 American Psychological Association 2019, Vol. 8, No. 3, 182–189 2160-4134/19/$12.00 http://dx.doi.org/10.1037/ppm0000172

182

can further aggravate PIU (Wu, Ko, Wong, Wu, & Oei, 2016; Zhu, Zhang, Yu, & Bao, 2015), for example, when playing online games or using social network sites, mainly due to the fear of missing out (Gil, Chamarro, & Oberst, 2015).

It is also evident that smartphones often interrupt everyday life and are associated with time distortion while engaged in smart- phone use (Duke & Montag, 2017a; Lin et al., 2015). Problematic smartphone use (PSU) may also lead to a decrease in productivity (Montag & Walla, 2016). In some countries, law enforcement has banned smartphone use in situations such as driving a car, owing to the distraction of drivers from traffic and the potential for creating dangerous situations (Coben & Zhu, 2013; Falkner, 2011). Given that many users prolong their smartphone sessions even when in the relative privacy of their bedrooms (Montag, Kannen, et al., 2015), it comes as no surprise that PSU is often accompanied by poor sleep quality (Yogesh, Abha, & Priyanka, 2014) and in some cases, lower work engagement the next morning (Lanaj, Johnson, & Barnes, 2014). In the context of well-being and smart- phone use, a relatively recent study highlights the importance of including and assessing the motivation underlying people’s use of their smartphones (Ohly & Latour, 2014). Also related to well- being a recent study finds evidence for an association between PIU, life satisfaction, and commuting (during commuting the Internet will be accessed mostly via portable devices like smart- phones): A more negative attitude towards commuting was asso- ciated with higher PIU and lower life satisfaction levels (Lach- mann, Sariyska, Kannen, Stavrou, & Montag, 2017). This short summary of current literature highlights the potential negative effects of PSU in daily life and underlines the timeliness of the current research.

Two theoretical models of Internet addiction have recently been published. In their consideration of Internet gaming disorder, Dong and Potenza (2014), propose a model that emphasizes the influence of craving on the use of Internet games. Based on the work of Davis (2001), a more general model of Internet addiction has been developed by Brand, Young, and Laier (2014), which has become the basis for the Interaction of Person-Affect-Cognition-Execution (I-PACE) model (Brand, Young, Laier, Wölfling, & Potenza, 2016). In this model, the authors focus more on specific types of Internet addiction like shopping or gambling than generalized Internet addiction (of note they use the term Internet use disorder). Generalized Internet addiction may be described as a situation in which an individual is addicted to the Internet in general rather than to a specific application of the Internet (Brand, 2017). Of relevance to the current study, a key predisposing factor for the development of a generalized Internet addiction within this model is personality (Brand et al., 2016).

With respect to PIU, a large body of research has been con- ducted, which demonstrates the importance of a number of per- sonality dimensions in predicting PIU (see review by Montag & Reuter, 2015)1. The study of personality is important because it describes rather stable characteristics of a person, manifesting in typical emotional reactions, cognitive thinking patterns, and be- havior in everyday life (Montag & Panksepp, 2017). Moreover, personality is linked to important real-life variables, such as health behavior (Bogg & Roberts, 2004), longevity (Jackson, Connolly, Garrison, Leveille, & Connolly, 2015), and vulnerability for af- fective disorders (Lahey, 2009). Among the studied (and often highlighted) factors in the field of Internet addiction, high Self-

Directedness, a personality trait describing persons with high will- power and who are reasonably content with themselves, might represent a resilience factor against PIU (Montag et al., 2011; Montag, Jurkiewicz, & Reuter, 2010; Sariyska et al., 2014). Be- yond these results, several other research findings indicate that the personality dimensions Neuroticism (positively linked; Hardie & Tee, 2007) and Conscientiousness (negatively linked; Montag et al., 2010) must be mentioned to understand PIU and PSU.

Recently, a questionnaire has been published to assess smart- phone addiction: Kwon, Kim, et al. (2013) and Kwon, Lee, et al. (2013) have also demonstrated that there is an overlap between Internet and smartphone addiction but that this overlap is far from perfect. In their questionnaire, several facets of PSU are consid- ered, including daily life disturbance, positive anticipation of smartphone usage, withdrawal symptoms in absence of the smart- phone, cyberspace-oriented relationships, and problematic use of smartphone and development of tolerance (see Kwon, Lee et al., 2013, p. 5). Interestingly, in the Kwon study, it appeared that the overlap between PIU and PSU is about r � .40. Thus, 16% of the variance in both concepts overlaps (i.e., .402). Although this over- lap might not seem excessively high, it underlines a certain resem- blance between PIU and PSU (note: imagine a smartphone without access to the Internet; it virtually would be worthless). Given the high number of findings describing the association between PIU and personality, one could ask the question if the cause for the observed overlap possibly can manifest in a similar personality structure of PIU and PSU.

Therefore, the question arises whether the personality traits linked to Internet addiction are also linked to smartphone addic- tion. To answer this question, we collected data on Internet addic- tion, smartphone addiction, and personality to search for similar underlying correlation patterns. This enabled us to investigate whether the same personality variables were associated with both PIU and PSU and also allowed us to examine the strength of these associations. Beyond that, the presence of similar patterns between personality variables and PIU/PSU implicated the existence of a possible trait underlying both PIU and PSU. The personality struc- ture of this trait was further examined to see whether similar patterns emerged between the personality variables and both PIU and PSU, as any such finding would support the assumption that the same personality traits could be linked to both Internet and smartphone addiction.

Based on previous research, we predicted that low Self- Directedness, low Conscientiousness, and high Neuroticism would be linked to higher problematic Internet use. Given the partial overlap between Internet and problematic smartphone use, we expected that the same patterns would be visible between these personality traits and PSU. Finally, we assumed a common under- lying trait for PIU and PSU that should be affected by the same personality variables.

1 Please note, that there is some controversy in the research over how best to refer to problematic Internet use (PIU). We use the terms PIU and Internet addiction somewhat synonymously, given that the inventory we used to assess PIU is called the Internet Addiction Test (please see method section of the current paper). This controversy has not been made easier by the inclusion of a distinct form of PIU–Internet Gaming Disorder—in section III of DSM–5 (Petry & O’Brien, 2013; Pontes & Griffiths, 2014).

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183PERSONALITY AND DIGITAL ADDICTIVE TENDENCIES

Method

Participants

In the present study, N � 612 (177 males and 435 females) participants contributed data, whereof 572 (160 males and 412 females) owned a smartphone. All participants are part of the Ulm Gene Brain Behavior Project and part of the data has been pub- lished in the context of an Affective Neuroscience Framework earlier (Montag, Sindermann, Becker, & Panksepp, 2016; note that this paper deals with a different topic and only the smartphone addiction scale (SAS) data have been presented with respect to correlations of another questionnaire not of relevance for the present study). The mean age of the sample was 23.55 years (SD � 5.92). Participants were recruited in a university context, so most of the sample consists of students. All participants completed several questionnaires dealing with personality and technology use. For the purposes of the present study, participants provided information on their problematic Internet and smartphone use (questionnaires are described below). They also completed several questionnaires to assess personality (for more detail, see below). All participants provided written consent before participation in the study. The study was approved by the ethics committee at Ulm University.

Questionnaires

All participants completed Young’s Internet Addiction Test (IAT; Young, 1998a). This questionnaire consists of 20 items, answered on a 5-point Likert scale, ranging from rarely (1) to always (5). Items used in the IAT are, for example, “How often do you try to hide how long you’ve been online?” or “How often do you find that you stay online longer than you intended?” Our German translation of the IAT has been used in several of earlier studies, such as Montag et al. (2011) or Sariyska et al. (2014), Sariyska, Reuter, Lachmann, and Montag (2015). The internal consistency of the questionnaire in the present sample was very high (� � .88). Scoring the measure requires summing up of the 20 items. Higher scores indicate higher addictive tendencies to- ward the Internet. The possible range of scores is between 20 and 100 points.

The SAS has been published by Kwon, Lee, et al. (2013) and consists of 33 items, answered on a 6-point Likert scale, ranging from strongly disagree (1) to strongly agree (6). Items used within the questionnaire are, for example, “My life would be empty without my smartphone.” The questionnaire has been translated twice (forward- and back-translation): first from English to Ger- man and second from German to English language by two inde- pendent psychologists. The internal consistencies of our German translation are very high (� � .98). Similar to the IAT, scoring the SAS requires summing the individual items, with higher scores representing greater addictive tendencies toward the smartphone. The possible range of scores is between 33 and 198 points.

To assess the Five-Factor Model of personality, we administered the NEO Five-Factor Inventory by Costa and McCrae (1992) in German, as translated by Borkenau and Ostendorf (1993). This questionnaire consists of 60 items scored on a 5-point Likert scale, ranging from strongly disagree (1) to strongly agree (5). The Five-Factor Model of personality assesses Openness for Experi-

ence (Cronbach’s � � .75; sample item: “I am intrigued by the patterns I find in art and nature”), Conscientiousness (� � .85; “I keep my belongings neat and clean”), Extraversion (� � .79; “I like to have a lot of people around me”), Agreeableness (� � .79; “I try to be courteous to everyone I meet”), and Neuroticism (� � .86; “I often feel inferior to others”). Higher scores indicate higher scores on each dimension. Some items need to be recoded before the scores can be summed up.

Finally, given its relevance for a better understanding of Internet addiction (Sariyska et al., 2014), we asked participants to answer the items measuring Self-Directedness (e.g., “I usually am free to choose what I will do” or “My behavior is strongly guided by certain goals that I have set for my life”) from the Temperament and Character Inventory by Cloninger, Svrakic, and Przybeck 1993 (German translation by Cloninger & Richter, 1999). These items are answered with either “yes” (1) or “no” (0). Internal consistencies for the Self-Directedness scale were satisfying (� � .87). Higher scores indicate higher ratings on the dimension of Self-Directedness. As with the NEO Five-Factor Inventory, some items required recoding before the scores were added.

Statistical Analyses

Owing to skewed distributions of IAT and SAS variables, we used Spearman’s correlations to analyze the associations between the variables of interest. Gender effects were tested with Mann– Whitney U tests. Although cut-off points for the distinction of “problematic” or “addict” status have been mentioned in some work (Widyanto & McMurran, 2004), we refrain from doing so here. Debate remains over the precision of such cut-off values, and we understand the scores/diagnosis as a continuum. The correla- tions between personality variables and PIU/PSU were further investigated using Fisher’s z test. As the results indicated the particular importance of the personality dimension Self- Directedness, we conducted a hierarchical regression analysis, which included the investigation of a composite trait called prob- lematic digital use, derived from a principal component analysis (PCA). The extraction criterion for the PCA was, according to Kaiser-Guttman, an Eigenvalue greater than 1. We also analyzed the correlation patterns of the subdimensions of Self-Directedness in relation to PIU and PSU. All analyses have been computed in SPSS 22.

Results

Data Inspection

Visual inspection revealed skewed distributions for the variables IAT and SAS. Because the variables were non-normally distrib- uted, we decided to use nonparametric testing. The distributions are depicted in Figure 1. We did not find any outliers on any variables.

Age, Gender and IAT/SAS

Gender was significantly associated with IAT scores (U � 32978.50, p � .005) but not the SAS (U � 31976.00, p � .582). On the IAT scale, males reported higher scores than females (IAT: males M � 32.45; SD � 10.20 vs. females M � 29.84; SD � 7.83;

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184 LACHMANN, DUKE, SARIYSKA, AND MONTAG

SAS: males M � 66.88; SD � 27.20 vs. females M � 64.58; SD � 23.69). Age was associated with both IAT (rho � �.09, p � .031) and the SAS (rho � �.16, p � .001). Mean and median scores for the scales are as follows (SAS: M � 65.22, SD � 24.72 and Median � 61.00; IAT: M � 30.59, SD � 8.66 and Median � 28.00).

Personality and IAT/SAS

First (and in line with the works by Kwon, Kim, et al., 2013; Kwon, Lee, et al., 2013), a moderate association between the SAS and IAT was observed (rho � .53, p � .001). All other correla- tions between personality and the two technology use variables are depicted in Table 1. Fishers’ z test was used to compare the correlations between personality variables and IAT/SAS scores. Significantly higher correlations for the IAT compared with the SAS score were found for Extraversion (z � �2.4, p � .008), Agreeableness (z � 1.8, p � .039), Conscientiousness (z � 2.1, p � .023), and Self-Directedness (z � 1.8, p � .037). Openness showed a significantly inverse correlation with the SAS and was not related to the IAT score. Although some of the correlations between SAS, IAT, and personality (Neuroticism, Agreeableness, Conscientiousness and Self-Directedness) are in the same direc- tion, other correlations are unique (e.g., Extraversion and IAT). For reasons of completeness, we also provide the correlation patterns (including Fisher’s z tests) for males and females sepa- rately in Table 2 though these patterns are largely similar for both genders. The strongest correlations appear between Self- Directedness and both SAS (rho � �.33, p � .001) and IAT (rho � �.41, p � .001).

Principal Component Analysis of IAT and SAS and Regression Model

A (unrotated) PCA of IAT and SAS sum scores revealed one underlying composite trait with an Eigenvalue of � � 1.55, ex- plaining 77.6% of the variance of both addiction questionnaires (no other Eigenvalue � 1). We call this composite trait “problem- atic digital use”. As a follow-up analysis, we inserted this trait as a dependent variable in a hierarchical regression model. As inde- pendent variables, we included demographic variables (age and gender) in the first block, due to their significant associations with PIU and/or PSU and their general well-known role in both con- structs. Given the robustness of the association between Self- Directedness and PIU/PSU, this variable was entered in the second block. Big Five personality traits were inserted in the third block. Demographic variables alone explained 2.6% of the variance, Self-Directedness added a further 15.6% to the model, and the Big Five variables an increment of 5.0% of problematic digital use. The model that accounts for most of the variance (F(8,563) � 21.25, p � .001), explains a total of 23.2% variance (Low). Self- Directedness, (low) Conscientiousness, (low) Agreeableness, (high) Extraversion, (low) Openness; and (high) Neuroticism were the predictors of the model, as age and gender did not achieve significance in the final model (Table 3).

Self-Directedness and SAS/IAT: A Close Look

The analysis in this results section demonstrates the importance of (low) Self-Directedness for a better understanding of digital

Figure 1. Distibution of the Internet Addiction (left) and Smartphone Addiction (right) Test scores are presented. See the online article for the color version of this figure.

Table 1 Common Personality Relationships to Internet Addiction Test (IAT)/Smartphone Addiction Scale (SAS) Scores

Sample Neuroticism Extraversion Openness Agreeableness Conscientiousness Self-Directedness

SAS N � 572 .21�� .01 �.14�� �.11�� �.23��� �.33���

Fisher’s z ns z � �2.4, p � .008 z � �2.9, p � .002 z � 1.8, p � .039

z � 2.1, p � .023 z � 1.8, p � .037

IAT N � 612 .26��� �.13�� .03 �.21��� �.34��� �.41���

Note. Spearman correlations are presented. Significant associations common to both SAS and IAT are bold. �� p � .01. ��� p � .001.

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185PERSONALITY AND DIGITAL ADDICTIVE TENDENCIES

overuse. As Self-Directedness is composed of several subdimen- sions, we consider the subscales of this trait and their individual associations with both IAT and SAS. The character trait Self- Directedness is composed of the subscales Responsibility versus Blaming (SD1), Purposefulness versus Lack of Goal Direction (SD2), Resourcefulness versus Inertia (SD3), Self-Acceptance ver- sus Striving (SD4) and Congruent Second Nature versus Bad Habits (SD5). For a more detailed discussion, please see the work by Kose (2003). As can be seen in Table 4, all subscales are significantly associated with both forms of problematic digital use. Hence, no individual facet appears to be of special relevance, rather the complete personality dimension of Self-Directedness is an important factor in problematic digital use.

Discussion

The present study investigated whether the same personality traits are related to both PIU and PSU. This research question is of importance, because both PIU and PSU are moderately, though not perfectly, associated with each other. Therefore, we investigated whether one of the most prominent personality constructs linked to PIU—namely (low) Self-Directedness—would also predict higher PSU. Our study revealed that low Self-Directedness is indeed associated with higher PSU and PIU, therefore, clearly contribut- ing toward the shared variance of both constructs. Furthermore, we extracted a common trait (problematic digital use) underlying both PIU and PSU. This trait was determined by the same personality variables as PIU and PSU, especially by (low) Self-Directedness. People with lower Self-Directedness can be described as dissatis-

fied with their personalities, not able to achieve their planned goals and have lower will-power. Given the importance of Self- Directedness in the better understanding of PIU in previous studies (Montag et al., 2010, 2011 and Sariyska et al., 2014), the present study shows that these findings can also be extended to PSU. Moreover, the frequently observed association between PIU and Self-Directedness has been replicated again in a different sample in the present study.

Our findings highlight the importance of considering personality variables when investigating factors associated with Internet ad- diction, as outlined in the I-PACE model of Internet addiction (Brand et al., 2016). Although this model is theoretically plausible, it requires additional empirical support (Brand, 2017). With the present study, we can contribute some empirical evidence (in the context of personality) toward the validity of this model.

As with the personality-addiction associations discussed above, in the present study Fisher’s z test revealed that the associations between personality and PSU are a bit weaker compared with the relationships with PIU, which may have something to do with the slightly different topics investigated: although a smartphone with- out an online connection is rather useless, it can be used in this manner (and therefore only a moderate overlap with PIU can be expected); generalized PIU assesses, in broad terms, one’s own addictive tendencies, going beyond the rather small domain of smartphone usage. These differences are mirrored in the results of our gender analysis. As the literature has provided evidence (not uniformly, but often) for a more “male Internet addict” (Ko, Yen, Yen, Chen, & Chen, 2012; Lachmann, Sariyska, Kannen, Cooper, & Montag, 2016; Shaw & Black, 2008), the present study shows that this may again only be true for the broad term of PIU, but not PSU, where we could not find significant gender differences in our sample. This ultimately may be related to some channels being prominent on a smartphone, but not on desktop computer, such as the social communication channel WhatsApp. In a recent study, we were able to show that these channels are used more frequently by females compared with males (Montag, Błaszkiewicz, Sariyska, et al., 2015). We do not want to follow this point further because it was not the main focus of the manuscript and we did not set up a hypothesis with respect to gender issues in digital overuse.

At this point in the discussion, we also want to highlight the less prominent, though still important, links between personality traits of the Five-Factor Model of personality and both PIU and PSU. In line with earlier studies (Hardie & Tee, 2007; Montag et al., 2010,

Table 2 Personality and the Internet Addiction Test (IAT)/Smartphone Addiction Scale (SAS) Scores Distinguished by Gender

Sample Neuroticism Extraversion Openness Agreeableness Conscientiousness Self-

Directedness

(males) SAS (N � 160) .35��� �.09 �.12 �.13 �.26�� �.41���

IAT (N � 177) .39��� �.08 .06 �.19� �.44��� �.47���

Fisher’s z ns ns z � �1.6, p � .046 ns z � 1.9, p � .031 ns (females)

SAS (N � 412) .17�� .05 �.14�� �.11� �.22��� �.30���

IAT (N � 435) .26��� �.13�� .03 �.19��� �.27��� �.38���

Fisher’s z ns z � 2.6, p � .004 z � �2.5, p � .007 ns ns ns

Note. Spearman correlations are presented. Note that not everyone of the sample owned a smartphone. Therefore the SAS groups are smaller than IAT groups. � p � .05. �� p � .01. ��� p � .001.

Table 3 Hierarchical Regression Predicting the Composite Trait Problematic Digital Use

Predictors B SE � R2

Age �.02 .01 �.05 23.2% Gender �.12 .09 �.09 Self-Directedness �.04 .01 �.27���

Conscientiousness �.31 .08 �.17���

Agreeableness �.25 .08 �.13��

Extraversion .25 .08 .13��

Openness �.16 .07 �.09�

Neuroticism .16 .08 .11�

� p � .05. �� p � .01. ��� p � .001.

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186 LACHMANN, DUKE, SARIYSKA, AND MONTAG

2011; Sariyska et al., 2014), participants with higher Neuroticism, lower Agreeableness and lower Conscientiousness, reported higher scores on both the SAS and the IAT. This might lead to speculation as to whether people characterized by either high anxiety or low Agreeableness might prefer online social interactions compared with real-life interactions, a theory, which has been discussed in the context of social phobia in previous work (see Caplan, 2007; Yen, Ko, Yen, Wu, & Yang, 2007). The associations with Con- scientiousness are also in keeping with the literature, as a tendency toward being disorderly and not diligent could very well explain why people may procrastinate using their online channels (for more on the link between Conscientiousness and procrastination, see Schouwenburg & Lay, 1995). A recent study also provided supporting evidence for a relationship between lower Conscien- tiousness and greater WhatsApp usage on the smartphone by tracking real-life behavior on smartphones (Montag, Błaszkiewicz, Sariyska et al., 2015). Two of the associations between personality and both SAS and IAT scores were associated with only one of the mentioned scales: Although higher Extraversion was associated with lower IAT scores, higher Openness for Experience was re- lated to lower SAS scores. Potentially, higher Extraversion might be associated with more “real-life participation”, leading to less computer desktop use, but not lower smartphone use, as extraverts may use their smartphones as an extension of their social self (see Montag et al., 2014; Montag, Błaszkiewicz, Sariyska, 2015). In- dividuals who are highly open to experience might also be more easily bored by smartphone usage and aim to fulfil their psycho- genic needs in more artistic or intellectual settings, not dependent on smartphones. As is true of all the results observed in the present study, these findings require replication, as some of the correla- tions are rather small. As highlighted in the following limitations section, further studies also need to ask participants about the specific applications of the Internet and smartphone they most use, to enable a more complete understanding of the results of the present study. In this context, it should be noted that several studies did find similar associations between personality and other addictive behaviors (besides PIU and PSU) like, for example, alcoholism (Mezquita, Stewart, & Ruipérez, 2010) or pathologic gambling (Mann et al., 2017). This evidence points to the presence of general vulnerability factors for the development of addictive behaviors.

Some limitations of the present study need to be acknowledged. First, the nature of the study is cross-sectional. Therefore, it is not possible to predict if Internet overuse leads to higher smartphone use or the other way around. Of course, this was not the main aim of the present study—we wanted to check if the same personality

associations might be related to both PIU and PSU. In this context, no causal relationships can be derived. Second, the present study investigated a student sample. Therefore, our findings need to be extended to other populations, including the general population and clinical contexts in future work. Third, our study investigated generalized PIU and PSU. In particular, in the context of PIU, the importance of also investigating distinct features of online usage was demonstrated (Montag, Bey, et al., 2015; Müller et al., in press). The need to distinguish between generalized and specific PIU has also been highlighted by Davis (2001). This topic warrants further attention in the future. Finally, some recent theoretical work has demonstrated the importance of moving beyond self- report in the field of digital addiction research (Markowetz, Błaszkiewicz, Montag, Switala, & Schlaepfer, 2014; Montag, Reu- ter, & Markowetz, 2015; Montag, Duke, & Markowetz, 2016), which has received some initial empirical support (Lin et al., 2015; Montag, Błaszkiewicz, Lachmann, et al., 2015). Beyond the pres- ent study, in the context of PSU, future studies need to establish a theoretical framework to study this relevant research field (such as that existent in the field of Internet addiction; e.g., the model proposed by Brand et al., 2016). Finally, new questionnaires to assess smartphone addiction have been developed. These new measures need to be investigated to see which are most useful in a clinical context (Lin et al., 2014). In addition to this, future studies need a stronger focus on personality and its links to conditioning principles in the study of smartphone addiction be- cause it has been proposed recently that conditioning principles could be at the heart for a better (mechanistic) understanding of excessive smartphone use (Duke & Montag, 2017b).

In sum, the present study demonstrates an overlap between the structure of personality and both PIU and PSU. This overlap can be observed for Neuroticism (high), Agreeableness (low) and Con- scientiousness (low). Of particular importance is the association between low Self-Directedness and higher problematic smart- phone/Internet use, which seems to be very robust. With respect to the smartphone, it might be necessary in the future to also distin- guish between generalized smartphone addiction and specific forms of smartphone addiction.

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Table 4 Associations Between the Subscales of Self-Directedness and the Internet Addiction Test (IAT)/ Smartphone Addiction Scale (SAS)

Sample SD1 SD2 SD3 SD4 SD5

SAS N � 572 �.22��� �.23��� �.23��� �.26��� �.21���

Fisher’s z ns z � 1.9, p � .031 ns ns z � 1.8, p � .033 IAT N � 612 �.25��� �.33��� �.26��� �.31��� �.31���

Note. Spearman correlations are presented. The above abbreviations stand for the subscales Responsibility vs. Blaming (SD1), Purposefulness vs. Lack of Goal Direction (SD2), Resourcefulness vs. Inertia (SD3), Self- Acceptance vs. Striving (SD4) and Congruent Second Nature vs. Bad Habits (SD5). ��� p � .001.

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Received May 13, 2016 Revision received October 6, 2017

Accepted October 9, 2017 �

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