Next Article in Journal
Evaluation of Terrestrial Water Storage Changes over China Based on GRACE Solutions and Water Balance Method
Next Article in Special Issue
The Influence of Psychological Distance on the Challenging Moral Decision Support of Sports Majors in Internet of Things and Machine Learning
Previous Article in Journal
Autonomous Innovations in the Rural Communities of Developing Countries I—A Narrative Analysis of Innovations and Synergies for Integrated Natural Resource Management
Previous Article in Special Issue
Measuring College Campus Well-Being with Multidimensional Indices: Sustainability of Higher Education in Taiwan
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 14
Issue 18
10.3390/su141811643
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Temperament, Character and Cognitive Emotional Regulation in the Latent Profile Classification of Smartphone Addiction in University Students

by
Dong-Hyun Choi
1 and
Young-Su Jung
2,*
1
Department of Counseling, Inje University, Gimhae 50834, Korea
2
College of Arts and Physical Education, Dong-A University, Busan 49236, Korea
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(18), 11643; https://doi.org/10.3390/su141811643
Submission received: 4 July 2022 / Revised: 30 August 2022 / Accepted: 7 September 2022 / Published: 16 September 2022
(This article belongs to the Special Issue Organizational Behavior and Psychological Research for Sustainability)
Browse Figures
Review Reports Versions Notes

Abstract

:
The frequency of smartphone use has been increasing since COVID-19, and the problem of smartphone addiction is expected to intensify in modern society where smartphones have diverse uses. According to a recent study, cognitive emotional regulation strategies have proven to be effective in deepening or alleviating smartphone addiction. Therefore, it is necessary to understand the characteristics of smartphone addiction according to various psychosocial approaches, including the cognitive emotional regulation strategy. The purpose of this study is to classify the potential profiles of smartphone addiction and to verify the trends and differences of the classified groups. A total of 333 college students with an age range of 22–25 were targeted. All subjects were asked to take the Smartphone Addition Scale Based on Behavioral Addiction Criteria (SAS-B), Temperature and Character Inventory (TCI), and Cognitive Emotion Regulation Questionnaire (CERQ). In order to conduct a person-centered approach, Latent Profile Analysis (LPA) was used, and it was analyzed using Mplus 7. As a result, there were significant differences in the classification of potential groups for smart addiction. It was found that there is a high correlation between temperament and character in smartphone addiction and cognitive emotional regulation strategies. This study is expected to be useful as basic data for treatment and preventive approaches according to smartphone addiction in the future.

1. Introduction

In recent years, rapid scientific and technological advancements have made smartphones important devices in people’s lives [1]. Smartphones are used for various purposes in many fields because they are convenient [2]. However, concerns have recently been increasing regarding a variety of psychological and physical problems, such as obesity, decreased muscle function, and altered posture due to low physical activity in relation to stress, low self-esteem, and depression [3,4]. In particular, although college students are accustomed to using smartphones, problems with their studies and daily lives have been observed, such as loss of communication with others, lack of academic concentration, and irregular sleep patterns [5,6,7]. Nevertheless, the number of smartphone owners has continuously increased over the past decade to reach 6.64 billion worldwide, accounting for 83.96% of the global population [8,9]. Also, it is reported that the degree of use of smartphones or mobile phones after COVID-19 is higher than before [10]. Thus, it is predicted that the problem of smartphone addiction will gradually intensify in modern society [11], where the utilization of smartphones has diversified, and the boundaries of Internet use have disappeared. In fact, 16.9% of Swiss vocational school students showed smartphone addiction, which was very serious. At this time, it was found that the prevalence of smartphone addiction was higher in adolescents (15–16 years old) than in adults (19 years or older) or high stress levels [12].
Smartphone addiction can be understood as a pattern of problem behaviors based on Internet addiction and thus results in behavioral concerns [13,14,15,16]. Types of behavioral addiction include gambling addiction and Internet addiction, with withdrawal, emotional regulation, resistance, and control disorder symptoms such as obsessive behavior [17,18]. Many studies have reported that Internet addiction is related to mental health disorders [19,20,21,22,23], and an important connection between Internet addiction and personality development has been established [24,25,26]. Therefore, research on smartphone addiction has been conducted from a perspective similar to that on Internet addiction. Furthermore, smartphone addiction is closely related to personality traits, and it has been reported as a phenomenon according to the relationship between personality and emotion as a response to external stimuli [27].
In a recent previous study, it was reported that smartphone addiction behavior is related to the characteristics of Big Five personality traits [28,29]. Personality is a set of traits that determine people’s similarities and differences in thoughts, feelings, and behavior [30]. Although many individual traits have been identified, this study has focused on the so-called “Big-five personality” factors: extraversion, neuroticism, openness-to-experience, agreeableness, and conscientiousness [28]. This is because various types of behavioral addiction such as smartphone addiction are reported to be closely related to these areas [31]. Smartphone users with problems can be thought of as exhibiting personality traits in this area. This is because extroverts spend more time calling others [32].
This study suggested that personality traits including anxiety, lack of concentration, pessimism, and inferiority complex contributed to deriving negative results from the behavior of using a smartphone. This behavior can lead to a habit in individuals who use smartphones in their daily lives. These personality characteristics related to smartphone addiction may be accompanied by characteristics unique to the individual [33,34]. Individual temperament is known to both influence and be influenced by individual experiences and plays an important role in personality formation [35]. According to a study applying the Big Five personality model, it is explained that innate temperament is attributable to the biological basis of personality [36,37]. Based on this, a personality model with four human innate, biological temperament dimensions and three acquired and social character dimensions was developed [38]. It has been reported that innate temperament can overcome or alleviate any problem through character even if it is vulnerable in certain situations, and it is valuable as a preventive and therapeutic resource [39,40]. In addition, a study on individual factors of smartphone addiction suggested that the degree of addiction can be higher as emotional control difficulties are experienced or strengthened [41,42].
An emotion is a psychological state formed based on an individual’s experiences, such as joy, emptiness, and anxiety [43]. There are both static and variable aspects of emotion. Emotions are what an individual feels and expresses, and personality can be explained as a characteristic of an individual that is formed according to the degree of various emotions [44,45]. This suggests that emotions and personality are closely related. In recent studies, cognitive emotion regulation strategies have proven their effectiveness in deepening or alleviating smartphone addiction [46,47]. Based on the evidence that a human’s personality can be changed through various psychosocial approaches, it is expected that there will be more specific directions for understanding the characteristics of smartphone addiction according to cognitive and emotional regulation strategies (adaptive/maladaptive cognition and emotion regulation methods) [47,48,49]. So far, numerous studies have been conducted to verify the complex individual psychological characteristics of smartphone addiction [50]. Not all researchers agree on the understanding of smartphone addiction, because the points of view in explaining smartphone addiction may be different [51,52]. This is because, without considering the complex and overall characteristics of individuals related to smartphone addiction, the existing concept of smartphone addiction was replaced with the concept of Internet addiction. In other words, there may be limitations in that individual characteristics are not taken into account, and that the relationship between each variable can be artificially derived depending on the type of analysis used by the researcher. Therefore, smartphone addiction research should be attempted from the perspective of a variable-centered approach and a person-centered approach.
This study classified potential profiles for smartphone addiction and verified group tendencies. The purpose of this study is to analyze the potential profiles of smartphone addiction considering individual characteristics and to verify their validity. The research questions investigated in this study are as follows:
  • ① Is there a latent profile group classification for smartphone addiction?
  • ② Is there a difference between groups according to smartphone addiction latent profile classification?
  • ③ Are there differences according to temperament and character and cognitive and emotional control strategies in the group according to smartphone addiction latent profile classification?
  • ④ Are there any differences in the effect relationships between groups according to smartphone addiction latent profile classification?

2. Materials and Methods

This study was approved by the University’s Human Research Ethics Committee. All participants provided signed informed consent prior to participating in the study.

2.1. Participants

This study used convenience sampling to recruit participants from regional universities in Gwangju, Daejeon, and Busan. A total of 333 students were selected from college students aged 18 and over. For the survey, the researcher had to fill out the questionnaire in a self-filling format after sufficiently explaining to the selected college students, and after filling out the questionnaire, a small gift was provided for the applicants. Among the total participants, there were 180 males (mean age = 22.5 years, standard deviation = 2.3 years) and 153 women (mean age = 21.95 years, standard deviation = 2.7 years). Due to the COVID-19 outbreak, the target audience was limited to those who followed the quarantine rules, such as wearing a mask, checking temperature, and sanitizing hands.

2.2. Measurements

2.2.1. Smartphone Addiction Scale Based on Behavioral Addiction Criteria (SAS-B)

The Smartphone Addiction Scale Based on Behavioral Addiction Criteria (SAS-B) [53] was used to measure smartphone addiction in the current study. The questionnaire was developed considering the characteristics of Koreans when including all six criteria of behavioral addiction with respect to smartphone use. The SAS-B classifies smartphone addiction into the subdomains of salience, mood modification, tolerance, withdrawal, conflict, and relapse, each of which includes four items, for 24 items in total. Responses are rated on a five-point Likert scale (“strongly agree” = 5 points). The higher the total score, the higher the level of smartphone addiction.

2.2.2. Temperament and Character Inventory (TCI)

To measure temperament and character, this study used Cloninger’s [54] Temperament and Character Inventory (TCI), which was developed in consideration of biopsychosocial characteristics. Specifically, this study used the standardized adult Korean version of the TCI [55] from Maumsarang Co., Ltd. (Seoul, Korea) (http://www.maumsarang.kr/ (accessed on 3 July 2022)), which includes 140 questions. The TCI consists of a temperament scale comprising harm avoidance, novelty-seeking, reward dependence, and persistence, and a character scale comprising self-directedness, cooperativeness, and self-transcendence.

2.2.3. Cognitive Emotion Regulation Questionnaire (CERQ)

To measure cognitive emotion regulation, this study used the Cognitive Emotion Regulation Questionnaire (CERQ), originally developed by Garnefski [56] and adapted for use with Korean populations by Sohee Kim [57]. This self-report scale is divided into “adaptive cognitive and emotional control strategy” and “maladaptive cognitive and emotional control strategy” subfactors to identify the thoughts that generally arise when respondents experience negative or unpleasant events. The subfactors comprise five adaptive strategies, which are putting into perspective, refocus on planning, positive refocusing, acceptance, and positive reappraisal, and four maladaptive strategies, which are self-blame, blaming others, and rumination, and catastrophizing. These 36 items are rated on a five-point Likert scale, ranging from “not at all” (1 point) to “very much” (5 points). For each subscale, higher scores indicate a greater degree of adaptive or maladaptive cognitive and emotional control strategy use, respectively.

2.3. Statistical Analysis

In this study, a person-centered approach latent profile analysis (LPA) was conducted and Mplus 7 was used. The optimal number of latent profiles was determined by examining the information reference index, and the level of fit was determined based on the Akaike information criterion (AIC), Bayesian information criterion (BIC), and sample-size adjusted BIC (SABIC). Entropy was simultaneously examined to establish the validity of the latent profile classification. Then, for the clinical significance of data analysis and the best data collection, the number of random groups was set to 50 and 15, respectively. In addition, the likelihood ratio-based criteria were examined, for which we applied a saturation model to explain more complete data by including all unknowns, such as data quantity. The Lo–Mendell–Rubin adjusted likelihood ratio test (LMRT) was used to examine the likelihood-ratio-based fit index. Correlation and linear regression analyses were performed to examine the relationship between the variables of smartphone addiction, cognitive and emotional control strategy, and temperament and character in the latent classes identified in the LPA. To examine the independence of the error term during linear regression analysis, the Durbin–Watson test was conducted to check autocorrelation.

3. Results

A basic analysis was performed for smartphone addiction, temperament and character, and cognitive and emotional control strategy with the 333 study participants. Table 1 presents the means and standard deviations for each variable.

3.1. Latent Profiles of Smartphone Addiction

Table 2 presents the results of verifying the model’s goodness of fit according to the number of latent classes in the LPA. According to the model’s goodness of fit index, the four latent classes (AIC = 9611.137, BIC = 9726.805, SABIC = 9632.127) with the lowest AIC, BIC, and SABIC were classified, and the overall group index was found to be suitable.
Based on the Vuong-Lo-Mendell-Rubin (VLMR), Lo-Mendell-Rubin (LMR), and bootstrapped likelihood ratio test (BLRT) values, which verified the reduction in the entropy value and provided the basis for model comparison, the four latent classes showed the following: VLMR, p = 0.138; LMR, p = 0.0154, and BLRT, p = 0.000. However, to determine the appropriate level for the number of latent profiles, three conditions must be accepted. First, the index levels of the AIC, BIC, and SABIC should be low. Second, the entropy level should be close to 1 [58]. Third, if the LMRT value is statistically significant, k models are adopted, and if it is not statistically significant, k-1 models are adopted [59,60]. In this study, three latent class models that satisfied all these conditions were selected. These three latent class models (AIC = 9657.831, BIC = 9756.843, SABIC = 9674.369) showed very high significance levels (VLMR, p = 0.000; LMR, p = 0.000; BLRT, p = 0.000). Thus, smartphone addiction in this study was classified into three latent classes—Class 1, Class 2, and Class 3—and showed a very high fit. Among the groups, Class 1 included 135 participants (40% of the total), Class 2 included 159 (48%), and Class 3 included 39 (12%).

3.2. Validation of Group Differences according to Smartphone Addiction Latent Profiles

Table 3 shows the differences in Class 3 of smartphone addiction according to the results of the LPA and the post-hoc. The latent class was subfactor of smartphone addiction: salience (F = 150.495, p < 0.001), mood modification (F = 78.529, p < 0.001), tolerance (F = 301.316, p < 0.001), withdrawal (F = 230.849, p < 0.001), conflict (F = 259.722, p < 0.001), and relapse (F = 143.004, p < 0.001) showed very significant differences. The results of a Scheffé test showed that the mean values for each group were in the following order of highest to lowest: Class 1, Class 2, and Class 3. Regarding levels of smartphone addiction, Class 1 was the low-level group, Class 2 was the medium-level group, and Class 3 was the high-level group. This is presented in Figure 1.

3.3. Group Differences in Temperament and Character, Cognitive and Emotional Control Strategy

Table 4 shows the results of Chi-square and post hoc (Scheffé) tests for temperament and character and cognitive and emotional control strategy according to the smartphone addiction latent profile classification. First, as a result of analyzing temperament and character, novelty-seeking (F = 15.979, p < 0.001), harm avoidance (F = 4.496, p < 0.05), persistence (F = 6.308, p < 0.05), self-directedness (F = 16.66, p < 0.001), and self-transcendence (F = 9.423, p < 0.001) showed differences in the average values for each latent class. Next, as a result of analyzing cognitive and emotional control strategy, for adaptive cognitive and emotional control strategy, the average value of the latent class in refocus on planning (F = 7.095, p < 0.01) and positive reappraisal (F = 4.822, p < 0.01) showed a difference in the mean value of the latent group. For maladaptive cognitive emotion regulation strategy, there was a difference in the average value of the latent class in blaming others (F = 7.758, p < 0.01), rumination (F = 6.478, p < 0.01), and catastrophizing (F = 17.822, p < 0.001). This is presented in Figure 2 and Figure 3.

3.4. Validation of Influence Relationships according to Smartphone Addiction Latent Profile

Correlation analysis was conducted to verify the variable relationships for each latent class according to the LPA. Table 5 shows the correlation verification results between variables of Class 1. A significant correlation was established between the overall score for smartphone addiction and self-directedness (r = −0.191, p < 0.05). Table 6 shows the correlation test results between the variables of Class 2. A significant correlation was established between smartphone addiction overall score and plan rethinking, positive reappraisal, blaming others, catastrophizing, novelty-seeking, and reward dependence Table 7 shows the correlation verification results between the variables of Class 3. Significant correlations were established with smartphone addiction overall score and putting into perspective, acceptance, blaming others, rumination, novelty-seeking, reward dependence, and self-transcendence. Next, regression analysis was performed to examine the effects of each potential group on smartphone addiction.
Table 8 is the result of regression analysis on smartphone addiction of class 1. The Durbin-Watson test value for examining the residual independence of autonomy for smartphone addiction in the class 1 group was close to 2. Autonomy for smartphones has an explanatory power of 13.9% (p < 0.05).
Table 9 is the result of regression analysis on smartphone addiction in class 2. In class 2, the explanatory power of catastrophe for smartphone addiction was 45.8% (p < 0.05), the explanatory power of stimulus seeking was 20.3% (p < 0.01), and the explanatory power of social sensitivity was 18% (p < 0.01). However, the Durbin-Watson test value for examining the independence between the residuals of plan rethinking, positive reevaluation, criticism of others, catastrophe, stimuli seeking, and social sensitivity for smartphone addiction was close to 2.
Table 10 is the result of regression analysis on smartphone addiction in class3. The explanatory power of blaming others for smartphone addiction in class 3 was 96% (p < 0.01). However, the Durbin-Watson test value for examining residual independence for prospective expansion, acceptance, criticism of others, rumination, stimuli-seeking, social sensitivity, and self-transcendence was close to 2.

4. Discussion

Although smartphone use can result in positive outcomes by providing convenience in daily life, it has also led to various negative consequences throughout society. The severity of smartphone addiction has been investigated based on factors related to personality and psychological characteristics [61], psychological symptoms [62], and cognitive emotion control [63,64]. However, researchers have different opinions on smartphone addiction. Thus, the present study verified the latent profile classification for smartphone addiction.
In this study, smartphone addiction was classified into three potential classes: the low group, medium group, and high group. This showed the same latent class model as was reported in previous studies [65,66,67] on smartphone addiction latent class profiles based on the person-centered approach. Using an objective and highly accurate [68] person-centered approach, a more rational classification system was calculated by focusing on the individual and maximizing the available information [69,70]. It is necessary to pay careful attention to individual characteristics in research to lead to treatment and predictions related to smartphone addiction [66]. This suggests that the scope of research on smartphone addiction should be expanded without limitations in approaches by type and symptom. In addition, differences in the latent profile according to temperament and character and cognitive emotion regulation were found in smartphone addiction, and a relationship was established. Previous studies have recognized the validity of the relationship between smartphone addiction and temperament and character [27,71]. In the present study, significant differences were found for novelty-seeking, harm avoidance, persistence, self-directedness, and self-transcendence, and the relationship between temperament, character, and smartphone addiction was high [71,72]. Significant differences were also found between smartphone addiction and cognitive and emotional control strategy, and there was a high reciprocal correlation between these variables. Therefore, it can be helpful to appropriately use strategies related to cognitive-emotional regulation for the prevention and reduction of smartphone addiction behavior [73,74].
In this study, there were potential differences in the influence on smartphone addiction by group according to the classification of latent profiles. These results support the study results [64] suggesting that rumination, catastrophizing, and blaming of others were the most important variables to distinguish groups of non-problematic and problematic smartphone users. In addition, it is in line with the study [74] that maladaptive emotion regulation strategies such as rumination, catastrophizing, self-blame, and other blame mediate the relationship between social anxiety and problematic smartphone use (PSU). However, the results of this study differed from previous studies in the sub-factors of temperament, character, and cognitive and emotional control that affect smartphone addiction. The main results and implications of the above are summarized as follows.
First, if the subgroup is affected by self-directedness and experiences self-control difficulties, smartphone addiction tendencies may appear: the lower the self-directedness, the higher the level of smartphone addiction. However, if self-directedness is practiced as a recognized behavior, positive changes can be expected. Therefore, it should be considered whether there is a need for a progressive task performance program to strengthen self-determination [75], or if a blended learning model should be applied [76]. Second, the middle group was more vulnerable to smartphone addiction, as catastrophizing and novelty-seeking were higher, and reward dependence was lower. This suggests that for temperaments with high novelty-seeking and low reward dependence, more positive changes can be promoted if learned helplessness can be improved. Catastrophizing can also be improved through more positive emotional experiences [77,78]. Therefore, there is a need for a program to reduce learned helplessness [79] or a coaching program based on positive psychology [80] that can increase reward dependence and reduce novelty-seeking and catastrophizing. Third, smartphone addiction may be the result of a mixture of negative emotions and social anxiety factors related to blaming others. Individuals with social anxiety generally have an intense fear of social situations in which others are watching them closely [81] and tend to avoid social situations [82]. Negative emotions associated with blaming others indicate a reluctance to communicate face-to-face with people. On the other hand, they have a very strong tendency to seek interaction with others using smartphones. They show very high smartphone addiction characteristics [83]. Therefore, the therapeutic effect of the emotional intervention can be expected for the high prevalence of smartphone addiction by using the cognitive-behavioral approach according to Fitz [84]’s attribution theory and Davis [85]’s attribution modification.
In previous literature examining the relationships between psychopathological variables and smartphone addiction severity, studies using a variable-oriented approach were mainly conducted from the perspective of treatment and prevention. However, some researchers have pointed out problems in that the prior research direction does not take into account the individual characteristics, level, and type of the smartphone addiction group, and that a uniform therapeutic approach is attempted [86,87]. In this study, a person-centered approach and a variable-oriented approach were attempted to verify the mutual relationship between the temperament, personality, and cognitive and emotional regulation strategies of college students according to the classification of the potential profile of smartphone addiction [88]. This study was able to classify the latent profiles of smartphone addiction among university students. These results can serve as basic data for related policy studies. In the future, fundamental therapeutic help or behavior modification programs related to smartphone addiction should be prepared for latent smartphone addicts. In addition, various educational programs that support healthy social relationships and communication without using a smartphone should be explored. This mixed modeling is thought to be useful in providing basic data related to the prevention and treatment of smartphone addiction. Despite the significance of this study, there is a limit to applying or generalizing the research results to other regions because this study was conducted only for university students in Gwangju, Daejeon, and Busan. Therefore, in a follow-up study, it is necessary to conduct research including major areas such as Seoul and Gyeonggi.

5. Conclusions

This study verified that there are differences according to temperament, personality, and cognitive emotion regulation strategies for each potential profile group for smartphone addiction among college students. This suggests the need for a level-specific approach study rather than an analysis study for each type and symptom. This study will serve as basic data for smart addiction research by providing new information according to the classification of potential smartphone addiction groups.

Author Contributions

Conceptualization, Y.-S.J., D.-H.C.; methodology, D.-H.C.; software, D.-H.C.; validation, Y.-S.J.; formal analysis, D.-H.C.; investigation, Y.-S.J., D.-H.C.; resources, Y.-S.J., D.-H.C.; data curation, D.-H.C.; writing—original draft preparation, D.-H.C.; writing—review and editing, Y.-S.J.; visualization, D.-H.C.; supervision, Y.-S.J.; project administration, Y.-S.J., D.-H.C. All authors have read and agreed to the published version of the manuscript.

Funding

This research received no external funding.

Institutional Review Board Statement

The study was conducted in accordance with the Declaration of Helsinki, and approved by the Institutional Review Board (or Ethics Committee) of NAME OF INSTITUTE (protocol code KSU-21-07-004-0901 and 1 September 2022).

Informed Consent Statement

Informed consent was obtained from all subjects involved in the study.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Gonçalves, S.; Dias, P.; Correia, A.P. Nomophobia and lifestyle: Smartphone use and its relationship to psychopathologies. Comput. Hum. Behav. Rep. 2020, 2, 100025. [Google Scholar] [CrossRef]
  2. Kim, Y.J. Analysis of Problemic Smartphone Use and Life Satisfaction by Smartphone Usage Type. J. Korea Game Soc. 2020, 20, 23–32. [Google Scholar] [CrossRef]
  3. Özaslan, A.; Yıldırım, M.; Guney, E.; Guzel, H.S.; Iseri, E. Association between problematic internet use, quality of parent-adolescents relationship, conflicts, and mental health problems. Int. J. Ment. Health Addict. 2022, 20, 2503–2519. [Google Scholar] [CrossRef]
  4. Olson, J.A.; Sandra, D.A.; Colucci, É.S.; Bikaii, A.A.; Nahas, J.; Chmoule-vitch, D.; Veissière, S.P.L. Smartphone addiction is increasing across the world: A meta-analysis of 24 countries. Comput. Hum. Behav. 2022, 129, 107138. [Google Scholar] [CrossRef]
  5. Choi, D.W. Physical activity level, sleep quality, attention control and self-regulated learning along to smartphone addiction among college students. J. Korea Acad. Ind. Coop. Soc. (JKAIS) 2015, 16, 429–437. [Google Scholar]
  6. Choi, H.S.; Lee, H.K.; Ha, J.C. The influence of smartphone addiction on mental health, campus life and personal relations-Focusing on K university students. J. Korean Data Inf. Sci. Soc. 2012, 23, 1005–1015. [Google Scholar]
  7. Zhang, M.X.; Wu, A.M. Effects of smartphone addiction on sleep quality among Chinese university students: The mediating role of self-regulation and bedtime procrastination. Addict. Behav. 2020, 111, 106552. [Google Scholar] [CrossRef]
  8. Newzoo. Global Mobile Market Report, 2021. Available online: https://newzoo.com/insights/trend-reports/newzoo-global-mobile-market-report-2021-free-version/Google Scholar (accessed on 4 July 2022).
  9. Statiosta(2022). How Many People Have Smartphones Worldwide (Jan 2022). Available online: https://bankmycell.com (accessed on 23 February 2022).
  10. Sebire, K. The Coronavirus Lockdown is Forcing Us to View ‘Screen Time’ Differently. That’s a Good Thing. 2020. Available online: https://theconversation.com/the-coronavirus-lockdown-is-forcing-us-to-view-screen-time-differently-thats-a-good-thing-135641 (accessed on 4 July 2022).
  11. Chen, I.-H.; Pakpour, A.H.; Leung, H.; Potenza, M.N.; Su, J.A.; Lin, C.-Y.; Griffiths, M.D. Comparing generalized and specific problematic smartphone/internet use: Longitudinal relationships between smartphone application-based addiction and social media addiction and psychological distress. J. Behav. Addict. 2020, 9, 410–419. [Google Scholar] [CrossRef]
  12. Haug, S.; Castro, R.P.; Kwon, M.; Filler, A.; Kowatsch, T.; Schaub, M.P. Smartphone use and smartphone addiction among young people in Switzerland. J. Behav. Addict. 2015, 4, 299–307. [Google Scholar] [CrossRef]
  13. Griffiths, M. Internet addiction: Fact or fiction? Psychologist 1999, 12, 246–250. [Google Scholar]
  14. Kardefelt-Winther, A.D.; Heeren, A.; Schimmenti, A.; Van Rooij, P.; Maurage, M.; Carras, M.; Edman, J.; Blaszczynski, A.; Khazaal, Y.; Billieux, J. How can we conceptualize behavioural addiction without pathologizing common behaviours? Addiction. 2017, 112, 1709–1715. [Google Scholar] [CrossRef] [PubMed]
  15. Panova, T.; Carbonell, X. Is smartphone addiction really an addiction? J. Behav. Addict. 2018, 7, 252–259. [Google Scholar] [CrossRef] [PubMed]
  16. Satchell, L.P.; Fido, D.; Harper, C.A.; Shaw, H.; Davidson, B.; Ellis, D.A.; Hart, C.M.; Jalil, R.; Bartoli, A.J.; Kaye, L.K. Development of an Offline-Friend Addiction Questionnaire (O-FAQ): Are most people really social addicts? Behav. Res. Methods 2021, 53, 1097–1106. [Google Scholar] [CrossRef] [PubMed]
  17. Billieux, J. Problematic use of the mobile phone: A literature review and a pathways model. Curr. Psychiatry Rev. 2012, 8, 299–307. [Google Scholar] [CrossRef]
  18. Yu, S.; Sussman, S. Does smartphone addiction fall on a continuum of addictive behaviors? Int. J. Environ. Res. Public Health 2020, 17, 422. [Google Scholar] [CrossRef]
  19. Zhou, Y.; Li, D.; Li, X.; Wang, Y.; Zhao, L. Big five personality and adolescent Internet addiction: The mediating role of coping style. Addict. Behav. 2017, 64, 42–48. [Google Scholar] [CrossRef]
  20. Griffiths, M.D. The myth of ‘addictive personality’. Glob. J. Addict. Rehabil. Med. (GJARM) 2017, 3, 555610. [Google Scholar] [CrossRef]
  21. Lee, Y.K.; Chang, C.T.; Lin, Y.; Cheng, Z.H. The dark side of smartphone usage: Psychological traits, compulsive behavior and technostress. Comput. Hum. Behav. 2014, 31, 373–383. [Google Scholar] [CrossRef]
  22. Panda, A.; Jain, N.K. Compulsive smartphone usage and users’ ill-being among young Indians: Does personality matter? Telemat. Inform. 2018, 35, 1355–1372. [Google Scholar] [CrossRef]
  23. Jin Jeong, Y.; Suh, B.; Gweon, G. Is smartphone addiction different from Internet addiction? comparison of addiction-risk factors among adolescents. Behav. Inf. Technol. 2020, 39, 578–593. [Google Scholar] [CrossRef]
  24. Kircaburun, K.; Griffiths, M.D. Instagram addiction and the Big Five of personality: The mediating role of self-liking. J. Behav. Addict. 2018, 7, 158–170. [Google Scholar] [CrossRef] [PubMed]
  25. Błachnio, A.; Przepiorka, A.; Senol-Durak, E.; Durak, M.; Sherstyuk, L. The role of personality traits in Facebook and Internet addictions: A study on Polish, Turkish, and Ukrainian samples. Comput. Hum. Behav. 2017, 68, 269–275. [Google Scholar] [CrossRef]
  26. Servidio, R. Exploring the effects of demographic factors, Internet usage and personality traits on Internet addiction in a sample of Italian university students. Comput. Hum. Behav. 2014, 35, 85–92. [Google Scholar] [CrossRef]
  27. Kim, B.R.; Oh, H.S.; Jo, M.H. The Relationship among Temperament, Ambivalence over Emotional Expressiveness, Stress Coping Style, and Smartphone Addiction. Korean Journal of Health Psychology. Korean J. Psychol. Health 2018, 23, 271–292. [Google Scholar]
  28. Takao, M. Problematic mobile phone use and big-five personality domains. Indian J. Community Med. Off. Publ. Indian Assoc. Prev. Soc. Med. 2014, 39, 111. [Google Scholar] [CrossRef]
  29. Maddi, S.R. Personality Theories: A Comparative Analysis, 5th ed.; Dorsey: Homewood, IL, USA, 1989. [Google Scholar]
  30. Digman, J.M. Personality structure: Emergence of the five- factor model. Annu. Rev. Psychol. 1989, 41, 417–440. [Google Scholar] [CrossRef]
  31. Butt, S.; Phillips, J.G. Personality and self reported mobile phone use. Comput. Hum. Behav. 2008, 24, 346–360. [Google Scholar] [CrossRef]
  32. Lane, W.; Manner, C. The impact of personality traits on smartphone ownership and use. Int. J. Bus. Soc. Sci. 2011, 2. Available online: https://www.researchgate.net/profile/Chris-Manner-2/publication/265480996_The_Impact_of_Personality_Traits_on_Smartphone_Ownership_and_Use/links/586f8caa08ae329d6215ff48/The-Impact-of-Personality-Traits-on-Smartphone-Ownership-and-Use.pdf (accessed on 4 July 2022).
  33. Hussain, Z.; Griffiths, M.D.; Sheffield, D. An investigation into problematic smartphone use: The role of narcissism, anxiety, and personality factors. J. Behav. Addict. 2017, 6, 378–386. [Google Scholar] [CrossRef]
  34. Monacis, L.; Griffiths, M.D.; Limone, P.; Sinatra, M.; Servidio, R. Selfitis behavior: Assessing the Italian version of the Selfitis Behavior Scale and its mediating role in the relationship of dark traits with social media addiction. Int. J. Environ. Res. Public Health 2020, 17, 5738. [Google Scholar] [CrossRef]
  35. Rothbart, M.K.; Ahadi, S.A.; Evans, D.E. Temperament and personality: Origins and outcomes. J. Personal. Soc. Psychol. 2000, 78, 122. [Google Scholar] [CrossRef]
  36. Walker, D.F.; Gorsuch, R.L. Forgiveness within the Big Five personality model. Personal. Individ. Differ. 2002, 32, 1127–1137. [Google Scholar] [CrossRef]
  37. Eysenck, H.J. The Dynamics of Anxiety and Hysteria: An Experimental Application of Modern Learning Theory to Psychiatry; Routledge & Kegan Paul: London, UK, 1957. [Google Scholar]
  38. Stallings, M.C.; Hewitt, J.K.; Cloninger, C.R.; Heath, A.C.; Eaves, L.J. Genetic and environmental structure of the Tridimensional Personality Questionnaire: Three or four temperament dimensions? J. Personal. Soc. Psychol. 1996, 70, 127. [Google Scholar] [CrossRef]
  39. Cloninger, C.R.; Svrakic, D.M.; Przybeck, T.R. A psychobiological model of temperament and character. Arch. Gen. Psychiatry 1993, 50, 975–990. [Google Scholar] [CrossRef] [PubMed]
  40. Oh, H.S. Problems of temperament and attention deficits affecting autonomy-mediated juvenile delinquency. Educ. Cult. Res. 2018, 24, 351–372. [Google Scholar]
  41. Fu, L.; Wang, P.; Zhao, M.; Xie, X.; Chen, Y.; Nie, J.; Lei, L. Can emotion regulation difficulty lead to adolescent problematic smartphone use? A moderated mediation model of depression and perceived social support. Child. Youth Serv. Rev. 2020, 108, 104660. [Google Scholar] [CrossRef]
  42. Zanjani, Z.; Moghbeli Hanzaii, M.; Mohsenabadi, H. The relationship of depression, distress tolerance and difficulty in emotional regulation with addiction to cell-phone use in students of Kashan University. Feyz J. Kashan Univ. Med. Sci. 2018, 22, 411–420. [Google Scholar]
  43. Ekman, P. An argument for basic emotions. Cogn. Emot. 1992, 6, 169–200. [Google Scholar] [CrossRef]
  44. Corr, P.J. Reinforcement sensitivity theory (RST): Introduction. In The reinforcement sensitivity theory of personality; Cambridge University Press: Cambridge, UK, 2008. [Google Scholar]
  45. Revelle, W. Personality processes. Annu. Rev. Psychol. 1995, 46, 295–328. [Google Scholar] [CrossRef]
  46. Kim, J.Y. The relationship between mother’s psychological control and smartphone addiction tendency perceived by elementary school students: Mediating effect of cognitive emotion regulation strategy. East-West Psychiatry 2020, 23, 27–47. [Google Scholar]
  47. Lee, J.Y. The effect of smartphone addiction in adults on subjective well-being: The double-mediated effect of executive function deficits and adaptive cognitive emotion regulation strategies. Digit. Converg. Res. 2019, 17, 327–337. [Google Scholar]
  48. Lim, J.Y. The mediating effect of maladaptive cognitive emotion regulation strategy and negative emotions on the relationship between perceived stress and smartphone addiction. J. Korean Contents Assoc. 2018, 18, 185–196. [Google Scholar]
  49. Senior, S. The effect of parental attachment on smartphone addiction: The mediating effect of adaptive and maladaptive emotion regulation. Youth Stud. 2017, 24, 131–154. [Google Scholar]
  50. Hart, D.; Atkins, R.; Fegley, S.; Robins, R.W.; Tracy, J.L. Personality and development in childhood: A person-centered approach. Monogr. Soc. Res. Child Dev. 2003, 68, 1–109. [Google Scholar] [CrossRef]
  51. Abendroth, A.; Parry, D.A.; Roux, D.B.L.; Gundlach, J. An analysis of problematic media use and technology use addiction scales–what are they actually assessing? In Conference on E-Business, E-Services and e-Society; Springer: Cham, Switzerland, 2020; pp. 211–222. [Google Scholar]
  52. Davidson, B.I.; Shaw, H.; Ellis, D. Fuzzy Constructs: The Overlap between Mental Health and Technology ‘Use’; School of Management, University of Bath: Bath, UK, 2020. [Google Scholar]
  53. Lee, J.H.; Lim, J.M.; Son, H.B.; Kwak, H.W.; Chang, M.S. Development and Validation of a Smartphone Addiction Scale Based on Behavioral Addiction Criteria. Korean J. Couns. Psychother. 2016, 28, 425–443. [Google Scholar] [CrossRef]
  54. Akmal, N. The role of temperament in human behavior. Web Sci. Int. Sci. Res. J. 2021, 2, 60–74. [Google Scholar]
  55. Min, B.B.; Oh, H.S.; Lee, S.Y. Temperament and Character Inventroy Menual. Soul Maumsarang. 2007, 6, 15–33. [Google Scholar]
  56. Garnefski, N.; Kraaij, V.; Spinhoven, P. Negative life events, cognitive emotion regulation and emotional problems. Personal. Individ. Differ. 2001, 30, 1311–1327. [Google Scholar] [CrossRef]
  57. Kim, S.H. Study on Relationships Among the Stressful Events, Cognitive Emotion ReguIation Strategies and Psycholocal Well-Being. J. Stud. Guid. Couns. 2008, 26, 5–29. [Google Scholar]
  58. Helsen, K.; Jedidi, K.; DeSarbo, W.S. A new approach to country segmentation utilizing multinational diffusion patterns. J. Mark. 1993, 57, 60–71. [Google Scholar] [CrossRef]
  59. Chung, K.H.; Kang, E.N. A Study of Social Network Type among Korean Older Persons: Focusing on Network Size, Frequencies of Contact, and Closeness. J. Korea Gerontol. Soc. 2016, 36, 765–783. [Google Scholar]
  60. Lo, Y.; Mendell, N.R.; Rubin, D.B. Testing the number of components in a normal mixture. Biometrika 2001, 88, 767–778. [Google Scholar] [CrossRef]
  61. Pearson, C.; Hussain, Z. Smartphone addiction and associated psychological factors. Addicta Turk. J. Addict. 2016, 3, 1–15. [Google Scholar] [CrossRef]
  62. Elhai, J.D.; Dvorak, R.D.; Levine, J.C.; Hall, B.J. Problematic smartphone use: A conceptual overview and systematic review of relations with anxiety and depression psychopathology. J. Affect. Disord. 2017, 207, 251–259. [Google Scholar] [CrossRef]
  63. Elhai, J.D.; Tiamiyu, M.F.; Weeks, J.W.; Levine, J.C.; Picard, K.J.; Hall, B.J. Depression and emotion regulation predict objective smartphone use measured over one week. Personal. Individ. Differ. 2018, 133, 21–28. [Google Scholar] [CrossRef]
  64. Extremera, N.; Quintana-Orts, C.; Sánchez-Álvarez, N.; Rey, L. The role of cognitive emotion regulation strategies on problematic smartphone use: Comparison between problematic and non-problematic adolescent users. Int. J. Environ. Res. Public Health 2019, 16, 3142. [Google Scholar] [CrossRef]
  65. Elhai, J.D.; Rozgonjuk, D.; Yildirim, C.; Alghraibeh, A.M.; Alafnan, A.A. Worry and anger are associated with latent classes of problematic smartphone use severity among college students. J. Affect. Disord. 2019, 246, 209–216. [Google Scholar] [CrossRef]
  66. Yue, H.; Zhang, X.; Sun, J.; Liu, M.; Li, C.; Bao, H. The relationships between negative emotions and latent classes of smartphone addiction. PLoS ONE 2021, 16, e0248555. [Google Scholar] [CrossRef]
  67. Yeum, D.M. Latent Profile Analysis on Smart Phone Dependence of Elementary School Students. J. Rehabil. Welf. Eng. Assist. Technol. (J. RWEAT) 2017, 11, 107–114. [Google Scholar]
  68. Wang, M.C.; Deng, Q.; Bi, X.; Ye, H.; Yang, W. Performance of the entropy as an index of classification accuracy in latent profile analysis: A monte carlo simulation study. Acta Psychol. Sin. 2017, 49, 1473–1482. [Google Scholar] [CrossRef]
  69. Li, J.B.; Wu, A.M.; Feng, L.F.; Deng, Y.; Li, J.H.; Chen, Y.X.; Lau, J.T. Classification of probable online social networking addiction: A latent profile analysis from a large-scale survey among Chinese adolescents. J. Behav. Addict. 2020, 9, 698–708. [Google Scholar] [CrossRef] [PubMed]
  70. Nylund-Gibson, K.; Choi, A.Y. Ten frequently asked questions about latent class analysis. Transl. Issues Psychol. Sci. 2018, 4, 440. [Google Scholar] [CrossRef]
  71. Duke, É.; Montag, C. Smartphone addiction and beyond: Initial insights on an emerging research topic and its relationship to Internet addiction. In Internet Addiction; Springer: Cham, Switzerland, 2017; pp. 359–372. [Google Scholar]
  72. Lachmann, B.; Duke, É.; Sariyska, R.; Montag, C. Who’s addicted to the smartphone and/or the Internet? Psychol. Pop. Media Cult. 2019, 8, 182. [Google Scholar] [CrossRef]
  73. Mohta, R.; Halder, S. A comparative study on cognitive, emotional, and social functioning in adolescents with and without smartphone addiction. J. Indian Assoc. Child Adolesc. Ment. Health 2021, 17, 44–65. [Google Scholar] [CrossRef]
  74. Zsido, A.N.; Arato, N.; Lang, A.; Labadi, B.; Stecina, D.; Bandi, S.A. The role of maladaptive cognitive emotion regulation strategies and social anxiety in problematic smartphone and social media use. Personal. Individ. Differ. 2021, 173, 110647. [Google Scholar] [CrossRef]
  75. Ha, Y.S.; Choi, Y.H. The effectiveness of a Autonomous Regulation Improvement Smoking Cessation Program on the Amount of Daily Smoking, Perceived Motivation, Cotinine in Saliva, and Autonomous Regulation for Girls High School Students who Smoked. J. Korea Acad. -Ind. Coop. Soc. (JKAIS) 2015, 16, 6169–6179. [Google Scholar]
  76. Adinda, D.; Marquet, P. Effects of Blended Learning Teaching Strategies on Students’ Self-Direction. In Proceedings of the 13th International Conference on e-Learning, Cape Town, South Africa, 5–6 July 2018; pp. 1–9. [Google Scholar]
  77. Van Tilburg, W.A.; Igou, E.R. Boredom begs to differ: Differentiation from other negative emotions. Emotion 2017, 17, 309. [Google Scholar] [CrossRef] [Green Version]
  78. Bench, S.W.; Lench, H.C. Boredom as a seeking state: Boredom prompts the pursuit of novel (even negative) experiences. Emotion 2019, 19, 242. [Google Scholar] [CrossRef]
  79. Elhai, J.D.; Vasquez, J.K.; Lustgarten, S.D.; Levine, J.C.; Hall, B.J. Proneness to boredom mediates relationships between problematic smartphone use with depression and anxiety severity. Soc. Sci. Comput. Rev. 2018, 36, 707–720. [Google Scholar] [CrossRef]
  80. Yang, X.J.; Liu, Q.Q.; Lian, S.L.; Zhou, Z.K. Are bored minds more likely to be addicted? The relationship between boredom proneness and problematic mobile phone use. Addict. Behav. 2020, 108, 106426. [Google Scholar] [CrossRef]
  81. American Psychiatric Association. Diagnostic and Statistical Manual of Mental Disorders (DSM-5®); American Psychiatric Pub: Washington, DC, USA, 2013. [Google Scholar]
  82. Asher, M.; Aderka, I.M. Gender differences in social anxiety disorder. J. Clin. Psychol. 2018, 74, 1730–1741. [Google Scholar] [CrossRef] [PubMed]
  83. Peterka-Bonetta, J.; Sindermann, C.; Elhai, J.D.; Montag, C. Personality associations with smartphone and internet use disorder: A comparison study including links to impulsivity and social anxiety. Front. Public Health 2019, 7, 127. [Google Scholar] [CrossRef]
  84. Fritz, H. The Psychology of Interpersonal Relations; John Wiley & Sons Inc.: Hoboken, NJ, USA, 1958. [Google Scholar]
  85. Davis, P.E. Cognitive and behavioural approaches to changing addictive behaviours. In Addictive Behaviour; Palgrave Macmillan: London, UK, 1996; pp. 158–175. [Google Scholar]
  86. Liu, S.; Xiao, T.; Yang, L.; Loprinzi, P.D. Exercise as an alternative approach for treating smartphone addiction: A systematic review and meta-analysis of random controlled trials. Int. J. Environ. Res. Public Health 2019, 16, 3912. [Google Scholar] [CrossRef] [PubMed]
  87. Malinauskas, R.; Malinauskiene, V. A meta-analysis of psychological interventions for Internet/smartphone addiction among adolescents. J. Behav. Addict. 2019, 8, 613–624. [Google Scholar] [CrossRef]
  88. Laursen, B.; Hoff, E. Person-centered and variable-centered approaches to longitudinal data. Merrill-Palmer Q. 2006, 52, 377–389. [Google Scholar] [CrossRef]
Sustainability 14 11643 g001 550
Figure 1. Differences in Personality according to Smartphone addiction groups classified as Latent Profile Analysis.
Figure 1. Differences in Personality according to Smartphone addiction groups classified as Latent Profile Analysis.
Sustainability 14 11643 g001
Sustainability 14 11643 g002 550
Figure 2. Differences in Personality (Temperament, Character) according to Smartphone addiction groups classified as Latent Profile Analysis.
Figure 2. Differences in Personality (Temperament, Character) according to Smartphone addiction groups classified as Latent Profile Analysis.
Sustainability 14 11643 g002
Sustainability 14 11643 g003 550
Figure 3. Differences in Cognitive Emotion Regulation Strategy according to Smartphone addiction groups classified as Latent Profile Analysis.
Figure 3. Differences in Cognitive Emotion Regulation Strategy according to Smartphone addiction groups classified as Latent Profile Analysis.
Sustainability 14 11643 g003
Table
Table 1. Basic Analysis of Smartphone addiction, Personality, Cognitive Emotion Regulation Strategy of Study Participants.
Table 1. Basic Analysis of Smartphone addiction, Personality, Cognitive Emotion Regulation Strategy of Study Participants.
VariablesM (SD)
SexMale age (N = 180)22.5 (2.3)
Female age (N = 153)21.95 (2.738)
Smartphone addictionSalience9.07 (3.417)
Mood modification11.52 (3.707)
Tolerance11.68 (3.324)
Withdrawal10.33 (3.012)
Conflict9.42 (3.738)
Relapse9.72 (3.870)
PersonalityTemperamentNovelty-seeking NS39.66 (9.804)
Harm Avoidance HA42.02 (10.360)
Reward Dependence RD44.25 (8.331)
Persistence PS44.18 (10.428)
CharacterSelf-Directedness SD42.6 (10.428)
Cooperativeness CO51.37 (11.574)
Self-Transcendence ST30.88 (11.595)
Cognitive Emotion Regulation StrategyAdaptive Cognitive and Emotional Control StrategyPutting into perspective14.10 (3.219)
Refocus on planning15.22 (32.146)
Positive refocusing13.44 (3.817)
Acceptance14.88 (2.683)
Positive reappraisal14.86 (3.458)
Maladaptive Cognitive and Emotional Control StrategySelf-blame12.90 (3.260)
Blaming others10.24 (3.924)
Rumination13.35 (3.021)
Catastrophizing10.35 (3.679)
Table
Table 2. Model-Fit Index Compare Analysis.
Table 2. Model-Fit Index Compare Analysis.
Classification CriteriaNumber of Latent Profiles
23456
Classification QulityEntropy0.8480.8480.8050.8310.850
Information Reference IndexAIC9797.2369657.8319611.1379578.4839554.185
BIC9869.5909745.8439726.8059730.8099773.167
SABIC9809.3219674.3699632.1279604.9269564.08
Model Comparison ValidationVLMRp0.000 ***0.000 ***0.0138 *0.47710.1924
LMRp0.000 ***0.000 ***0.0154 *0.48860.1981
BLRTp0.000 ***0.000 ***0.000 ***0.000 ***0.000 ***
N (%)Class1167 (0.49)135 (0.4)57 (0.18)62 (0.19)55 (0.16)
Class2170 (0.51)159 (0.48)135 (0.4)21 (0.06)102 (0.31)
Class3 39 (0.12)109 (0.32)92 (0.28)12 (0.04)
Class4 32 (0.1)127 (0.38)15 (0.05)
Class5 31 (0.09)31 (0.09)
Class6 118 (0.35)
* p < 0.05, *** p < 0.001.
Table
Table 3. Differences in Smartphone addiction by group according to Latent Profile Analysis.
Table 3. Differences in Smartphone addiction by group according to Latent Profile Analysis.
Class1
(N = 135)		Class2
(N = 159)		Class3
(N = 39)		FScheffé
M (SD)M (SD)M (SD)
Salience9.156 (0.349)12.527 (0.263)15.126 (0.604)150.495 ***3 > 2 > 1
Mood modification6.139 (0.254)10.586 (0.323)14.441 (0.393)78.529 ***3 > 2 > 1
Tolerance6.762 (0.261)10.535 (0.299)14.527 (0.402)301.316 ***3 > 2 > 1
Withdrawal9.362 (0.202)12.614 (0.207)14.925 (0.354)230.849 ***3 > 2 > 1
Conflict8.32 (0.257)10.784 (0.221)14.458 (0.439)259.727 ***3 > 2 > 1
Relapse14.509 (0.296)13.926 (0.283)13.601 (0.464)143.004 ***3 > 2 > 1
*** p < 0.001.
Table
Table 4. Differences in Cognitive Emotion Regulation Strategy, Personality (Temperament, Character) according to Smartphone addiction groups classified as Latent Profile Analysis.
Table 4. Differences in Cognitive Emotion Regulation Strategy, Personality (Temperament, Character) according to Smartphone addiction groups classified as Latent Profile Analysis.
Class1Class2Class3FScheffé
M (SD)M (SD)M (SD)
TemperamentNovelty-Seeking NS36.35 (10.025)40.74 (8.058)45.41 (11.407)15.979 ***2, 3 > 1
Harm Avoidance HA40.09 (11.857)41.96 (8.646)45.41 (7.622)4.496 *3 > 1, 2
Reward Dependence RD45.21 (8.474)43.36 (8.095)44.44 (8.494)1.813
Persistence PS46.64 (11.567)52.89 (8.902)41.69 (10.509)6.308 **1 > 2, 3
CharacterSelf-Directedness SD46.16 (11.172)41.44 (8.385)37.23 (7.436)16.66 ***1 > 2 > 3
Cooperativeness CO50.94 (14.025)51.53 (9.82)53.03 (7.72)0.499
Self-Transcendence ST8.32 (2.43)10.61 (1.966)14.64 (1.646)9.423 ***2, 3 > 1
Adaptive Cognitive
and Emotional Control Strategy		Putting into perspective14.5 (3.138)13.92 (3.286)13.62 (2.917)1.787
Refocus on planning15.92 (3.093)14.87 (3.05)14.1(3.05)7.095 **1 > 2, 3
Positive refocusing13.53 (3.501)13.51 (3.883)12.92 (3.601)0.426
Acceptance15.28 (2.553)14.61 (2.797)14.64 (2.182)2.562
Positive reappraisal15.56 (3.204)14.52 (3.563)14.05 (3.103)4.822 **1 > 2, 3
Maladaptive Cognitive and Emotional Control StrategySelf-blame12.63 (3.202)12.94 (3.347)13.44 (2.77)1.015
Blaming others9.41 (4.095)10.5 (3.561)12 (3.742)7.758 **2, 3 > 1
Rumination12.8 (3.243)13.39 (2.815)14.72 (2.449)6.478 **3 > 1, 2
Catastrophizing9.09 (3.707)10.73 (3.308)12.64 (3.674)17.822 ***3 > 2 > 1
* p < 0.05, ** p < 0.01, *** p < 0.001.
Table
Table 5. Class1 Correlation Analysis.
Table 5. Class1 Correlation Analysis.
Variables1234567891011121314151617
1. Putting into perspective1
2. Refocus on planning0.513 **1
3. Positive refocusing0.421 **0.4 **1
4. Acceptance0.520 **0.606 **0.429 **1
5. Positive reappraisal0.648 **0.721 **0.513 **0.556 **1
6. Self-blame0.356 **0.363 **0.171 *0.377 **0.329 **1
7. Blaming others−0.256 **−0.373 **−0.057−0.284 **−0.368 **−0.191 *1
8.Rumination0.0500.238 **0.345 **0.325 **0.1240.31 **0.0191
9.Catastrophizing−0.264 **−0.275 **0.056−0.163−0.32 **0.0320.547 **0.328 **1
10.Novelty-Seeking NS0.062−0.0340.1080.074−0.0800.1210.271 **0.0650.201 *1
11.Novelty-Seeking NS−0.0250.0820.0470.032−0.052−0.114−0.0890.253 **0.063−0.0271
12.Reward Dependence RD0.1430.0060.184 *0.173 *0.228 **0.0330.0750.0940.0040.132−0.327 **1
13.Persistence PS0.1030.193 *0.233 **0.0660.244 **−0.0160.053−0.0430.023−0.047−0.465 **0.357 **1
14.Self-Directedness SD0.0630.1090.0110.0820.195 *0.004−0.115−0.181 *−0.145−0.255 **−0.752 **0.308 **0.578 **1
15.Cooperativeness CO−0.019−0.0480.1540.0380.073−0.017−0.0470.0500.015−0.231 **−0.452 **−0.506 **0.547 **0.564 **1
16.Self-Transcendence ST0.305 **0.223 **0.312 **0.3550 **0.194 *0.182 *0.0470.333 **0.0990.184 *0.265 **0.056−0.083−0.239 **−0.219 *1
17.Total score for smartphone addiction0.024−0.1160.0620.126−0.08−0.0040.0430.1400.1010.0880.0830.109−0.169−0.191 *−0.0380.1661
* p < 0.05, ** p < 0.01.
Table
Table 6. Class2 Correlation Analysis.
Table 6. Class2 Correlation Analysis.
Variables1234567891011121314151617
1. Putting into perspective1
2. Refocus on planning0.594 **1
3. Positive refocusing0.575 **0.497 **1
4. Acceptance0.753 **0.651 **0.582 **1
5. Positive reappraisal0.666 **0.714 **0.711 **0.72 **1
6. Self-blame0.466 **0.373 **0.305 **0.563 **0.36 **1
7. Blaming others−0.23 **−0.2 *−0.035−0.263 **−.244 **−0.1511
8.Rumination0.289 **0.408 **0.293 **0.403 **0.329 **0.416 **0.171 *1
9.Catastrophizing−0.218 **−0.172 *−0.008−0.159 *−0.17 *0.0030.696 **0.696 **1
10.Novelty-Seeking NS0.193 *0.171 *0.224 **0.1360.224 **0.0600.193 *0.201 *0.1231
11.Novelty-Seeking NS0.022−0.146−0.0630.055−0.0620.135−0.0160.0650.060−0.0361
12.Reward Dependence RD−0.0300.109−0.103−0.048−0.035−0.1520.17 *0.1200.1010.141−0.293 **1
13.Persistence PS−0.0800.167 *0.155−0.0530.132−0.1300.325 **0.2 *0.27 **0.169 *−0.299 **0.2 3**1
14.Self-Directedness SD0.0370.198 *0.0810.0130.138−0.171 *−0.018−0.012−0.101−0.199 *−0.636 **0.269 **0.394 **1
15.Cooperativeness CO0.0550.173 *0.0010.1290.0400.0060.0690.158 *0.078−0.040−0.216 **0.421 **0.468 **0.388 **1
16.Self-Transcendence ST0.16 *0.252 **0.285 **0.1050.306 **0.0180.1480.187 *0.219 **0.378 **−0.044−0.1170.22. **−0.064−0.213 **1
17.Total score for smartphone addiction−0.120−0.222 **−0.018−0.103−0.175 *0.0360.197 *0.0150.255**0.185 *0.108−0.169 *−0.021−0.113−0.0440.1271
* p < 0.05, ** p < 0.01.
Table
Table 7. Class3 Correlation Analysis.
Table 7. Class3 Correlation Analysis.
Variables1234567891011121314151617
1. Putting into perspective1
2. Refocus on planning0.2651
3. Positive refocusing0.325 *0.3101
4. Acceptance0.528 **0.433 **0.2511
5. Positive reappraisal0.36 *0.528 **0.455 **0.391 *1
6. Self-blame0.1940.3060.2540.418 **0.1441
7. Blaming others0.039−0.0830.2830.0230.233−0.1351
8.Rumination0.0030.339 *0.403 *0.448 **0.0470.403 *−0.0491
9.Catastrophizing−0.084−0.0860.324 *0.1310.0940.468 **0.415 **0.377 *1
10.Novelty-seeking NS0.0880.1350.384 *0.1280.372 *0.2860.494 **−0.1190.48 **1
11.Novelty-seeking NS0.064−0.226−0.0850.090−0.35 *0.0270.1840.1190.38 *0.0851
12.Reward Dependence RD0.1170.421 **0.2560.1920.404 *0.300−0.0170.2570.2400.256−0.0491
13.Persistence PS0.0110.2510.444 **0.0670.408 **0.2250.547 **−0.0180.437 **0.717 **−0.0490.091
14.Self-Directedness SD−0.0600.015−0.103−0.1310.245−0.254−0.141−0.281−0.524 **−0.303−0.739 **−0.161−0.1821
15.Cooperativeness CO−0.129−0.090−0.007−0.017−0.0090.045−0.2070.2090.1720.0300.0660.32 *−0.194−0.0571
16.Self-Transcendence ST0.1890.1240.567 **0.0940.391 *0.1980.476 **0.260.584 **0.569 **0.2360.1870.61 **−0.373 *−0.0221
17.Total score for smartphone addiction0.329 *0.2610.2480.424 **0.3120.1550.492 **0.328 *0.2810.342 *0.2470.322 *0.296−0.292−0.1860.429 **1
* p < 0.05, ** p < 0.01.
Table
Table 8. The results of regression analysis on smartphone addiction in Class 1.
Table 8. The results of regression analysis on smartphone addiction in Class 1.
Dependent
Variable		Independent VariableβRR 2FDurbin-Watson Test
Total score for smartphone addictionSelf-Directedness SD−0.139 *0.1910.0295.058 *2.027
* p < 0.05.
Table
Table 9. The results of regression analysis on smartphone addiction in Class 2.
Table 9. The results of regression analysis on smartphone addiction in Class 2.
Dependent VariableIndependent VariableβRR 2FDurbin-Watson Test
Total score for smartphone addictionRefocus on planning−0.2750.4240.1470.545 ***1.864
Positive reappraisal−0.225
Blaming others−0.039
Catastrophizing0.458 *
Novelty-seeking NS0.203 **
Reward Dependence RD−0.18 **
* p < 0.05, ** p < 0.01, *** p < 0.001.
Table
Table 10. The results of regression analysis on smartphone addiction in Class 3.
Table 10. The results of regression analysis on smartphone addiction in Class 3.
Dependent VariableIndependent VariableβRR 2FDurbin-Watson Test
Total score for smartphone addictionPutting into perspective0.4750.7240.5254.889 **1.924
Acceptance0.658
Blaming others0.96 *
Rumination0.639
Novelty-Seeking NS 0.008
Reward Dependence RD0.192
Self-Transcendence ST0.038
* p < 0.05, ** p < 0.01.
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Choi, D.-H.; Jung, Y.-S. Temperament, Character and Cognitive Emotional Regulation in the Latent Profile Classification of Smartphone Addiction in University Students. Sustainability 2022, 14, 11643. https://doi.org/10.3390/su141811643

AMA Style

Choi D-H, Jung Y-S. Temperament, Character and Cognitive Emotional Regulation in the Latent Profile Classification of Smartphone Addiction in University Students. Sustainability. 2022; 14(18):11643. https://doi.org/10.3390/su141811643

Chicago/Turabian Style

Choi, Dong-Hyun, and Young-Su Jung. 2022. "Temperament, Character and Cognitive Emotional Regulation in the Latent Profile Classification of Smartphone Addiction in University Students" Sustainability 14, no. 18: 11643. https://doi.org/10.3390/su141811643

APA Style

Choi, D.-H., & Jung, Y.-S. (2022). Temperament, Character and Cognitive Emotional Regulation in the Latent Profile Classification of Smartphone Addiction in University Students. Sustainability, 14(18), 11643. https://doi.org/10.3390/su141811643

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop