Psychological Capital, College Adaptation, and Internet Addiction: An Analysis Based on Moderated Mediation Model

Using data from a baseline survey of college students, this study examined the possible mechanism by which psychological capital influences college students' internet addiction through the mediation effect of their individual college adaptability. The study constructed a parallel multiple mediation effect model to help understand the effect mechanisms among these factors. The results indicated that psychological capital had a triple effect on internet addiction: (1) Psychological capital had a direct effect of helping reduce college students' internet addiction; (2) emotional, learning, and interpersonal adaptation not only reduced internet addiction directly but also played mediating roles in the relationship between psychological capital and internet addiction; and (3) the mediation effects of emotional and interpersonal adaptation were moderated by psychological capital, leading to two different modes of mediation effects. As a whole, psychological capital imposes a quadratic effect on internet addiction. The campus policy implications of these findings are discussed.

Introduction

With the widespread popularity of the Internet, computers, smartphones, and other terminal hardware, being networked has become a trend among contemporary college students. Networks provide convenience for college students' learning and life, but the excessive use of networks may also cause problems. Internet addiction (IA) is one such problem. Numerous studies have found that IA is closely correlated with unhealthy mental states (1, 2), academic failure and social isolation (3, 4), and even suicidal ideation (5, 6). In particular, as Berardis et al. (7, 8) pointed, IA might be associated with the development/concurrence of psychiatric disorders especially in a context of an emotional dysregulation.

The concept of IA was first proposed by American psychiatrist Goldberg (9). Young (10) further developed this concept, and defined it as an impulse control disorder and a significant impairment of social and psychological functioning of the individual caused by the excessive use of the Internet. Although IA was not included in DSM-5 due to some doctrinal and clinical disagreements, Internet gaming disorder (IGD) has long been in the DSM-5 appendix as part of IA (11). In practice, this situation has not prevented a large number of country-specific surveys and studies on IA from proliferating [see (12)].

Recently, IA-related problems among youth have become an academic focus. Scholars have widely highlighted the following factors in their empirical studies: (1) psychological factors, such as personality traits [e.g., (13)] and coping styles [e.g., (14)]; (2) social factors, such as social support [e.g., (15)] and family socioeconomic status [e.g., (16)]; (3) demographic characteristics, such as gender [e.g., (17)]; and (4) situational factors, such as class climate [e.g., (18)].

It is somewhat puzzling that few studies have been performed on IA in relation to psychological capital (hereinafter referred to as “PsyCap”). PsyCap is one of the core concepts of the positive psychology movement of recent years. PsyCap refers to psychological resource beyond human capital, social capital, and a positive state of mind, and it can promote personal growth and improve performance in the process of an individual's development. PsyCap is composed of four operational dimensions: self-efficacy, hope, optimism, and tenacity (19). Empirical studies have shown that PsyCap is related to occupational performance, attitude, and behavior in the general occupational population (20) and plays an active role in college students' learning and daily life by improving academic performance (21), relieving psychological pressure (22), and promoting physical and mental health (23).

Logically, high PsyCap should also help college students use networks properly and reduce addiction orientation. Using 300 Chinese college students' data, Shen and Wang (24) found a negative relationship between PsyCap and IA. Their study confirmed that PsyCap had some explanatory power for IA but did not clarify the specific mechanism between them. Simsek and Sali (25) found the same relationship between the two variables among a sample of 211 college students in Turkey. Khera's study on 130 Indian college students had similar findings (26). However, these studies were also unable to clearly explain the relationship mechanism due to the lack of considering the mediating variables in their models.

In this regard, college adaptation (hereinafter referred to as “ColAda”) is a noteworthy concept. As a special form of social adaptation, ColAda refers to the process in which college students' behavior and ideas change regarding life, learning, and social interaction when they live in the new environment of a university campus. College students are prone to psychological discomfort and even adjustment disorder, partly due to their adolescent/early adult psychological development characteristics. Entering a university means escaping from their parents' monitoring and obtaining more freedom of choice. Moreover, with easier access to the internet and the stronger incentive of computer/network usage, college students are more fragile and more vulnerable to pathological internet use (PIU) (27). It is no exaggeration to conclude that the university life facilitates the risk factors of IA [(28–30): 34–35]. Therefore, it is of practical and theoretical significance to study IA from the perspective of ColAda. It can even deduce that IA is a reflection of college maladaptation. However, until now, only limited number of studies explore the relationship between ColAda and IA. In one of the few existing studies on this topic, Lanthier and Windham (31) suggested that there was a negative relationship between network use and college adjustment. Several studies also found that IA was significantly negatively correlated with all dimensions of the ColAda scale among Chinese college students (32–34).

The relationship between PsyCap and ColAda has also been confirmed (35). However, in the current research on the pathogenesis of IA, no study has integrated PsyCap, ColAda, and IA in the same analytical framework and considered the complex causal mechanisms among these factors, such as possible mediation or moderation effects. This study attempts to explore the influence of PsyCap on the IA of college students considering the possible mediating mechanism via ColAda.

As described in a recent review article (36), the mechanism of PsyCap has not been very clear and systematically expounded in theory. Besides the direct effect, there are also two ways in relevant empirical research: the buffering effect (the mediating effect between PsyCap and the outcome variable, or the relationship between them moderated by some specific variables) [e.g., (37, 38)] and the moderating effect [e.g., (39, 40)].

In this study, these effects will be taken into consideration. Based on the findings reviewed above and practical considerations, the relationships among variables of IA (Y), PsyCap (X), and ColAda (M) are hypothesized to be the following: H-1: PsyCap (X) is negatively related to IA (Y); H-2: ColAda (M) is negatively related to IA (Y); H-3: ColAda(M) mediates the relationship between PsyCap (X) and IA (Y); H-4: PsyCap (X) moderates the relationship between ColAda (M) and IA (Y). The reason for H-4 is that individuals with high PsyCap may avoid indulging in networks, even in the face of high college maladaptation problems. In the model, the superposition of indirect and moderating effects (through ColAda) reflects the quadratic effect of PsyCap on IA.

To describe the relationships among all variables according to the above assumptions, this study built a parallel multiple mediation effect model considering the moderation effect of the independent variable, PsyCap (X). The conceptual model is shown in Figure 1¹.

FIGURE 1

Figure 1. Model diagram of moderated mediation model.

Methodology

Survey and Sample

The data used in this study come from a survey of college students in the China University of Political Science and Law conducted in November 2015². The sample was obtained through random cluster sampling, taking classes as the clusters and students as the elements. Overall, 26.3% of university students were included in the sample. After data cleaning procedures to remove invalid cases, the effective sample size was 2,133³. Comparing the structures of the population and the sample, the main relevant indicators of the sample were consistent with the overall structure of the university population (except for a slightly lower proportion of seniors for some practical reasons), indicating good sample representativeness. Because it is inconvenient to apply weights with the bootstrap method, this study did not perform a weighting adjustment for the data.

Instruments

IA (Y)

The Internet Addiction Test (IAT) is most widely used to screen for problematic internet use. The scale, developed by Young (10), is one of the most widely used tools for screening IA. It consists of 20 items. Studies have shown that the scale has good reliability, validity, and unidimensionality (44, 45). This paper used it to measure the level of IA. The Cronbach's α coefficient of the IAT was calculated to be 0.9209 for this sample, indicating a high degree of internal consistency reliability.

ColAda (M)

A localized and simplified version of Students Adaptation Adaptability Questionnaire (SACQ) (46, 47) was used to measure the ColAda of college students. The revised scale has 36 items and contains six dimensions including personal–emotional adaptation, learning adaptability, interpersonal adaptation, life adaptation, university satisfaction, and overall assessment. To build a parsimonious model and considering the research topic, this study selected three dimensions and 17 items, i.e., emotional adaptation (M1, 8 items) (hereinafter called “EmoAda”), learning adaptation (M2, 4 items) (hereinafter called “LeaAda”), and interpersonal adaptation (M3, 5 items) (hereinafter called “IntAda”; see Table 1). Items (stared) expressing a negative response were reverse-coded so that a higher total score indicates better adaptation. Based on these data, the Cronbach's α coefficients of three dimensions were calculated to be 0.8012, 0.6836, and 0.7205, respectively, indicating high internal consistency reliability.

TABLE 1

Table 1. Three dimensions of college adaptation and all items' proportion distribution (%).

PsyCap (X)

The Positive Psychological Capital Questionnaire (PPQ) (48), a Chinese-localized edition, was used to measure college students' psychological capital. It contains four dimensions including self-efficacy, resilience, hope, and optimism, and 26 items in total. Studies have demonstrated that the co-motivating force of PsyCap is more obvious when regarded as a holistic concept (higher-order factor) (49). Scores on all items in the scale are summed, and a higher score indicates higher psychological capital. The Cronbach's α coefficient of this scale was 0.9038 based on the survey data, showing a high degree of internal consistency reliability.

The variables PsyCap and three dimensions of ColAda were all centered around the grand-mean to make the model estimate more robust, avoid collinearity to some degree, and interpret the output of the model more easily. There are no zero-point responses in these scales in the raw data.

The Testing Method of Indirect Effect

Indirect effects were tested by a bias-corrected bootstrap method. Because the indirect effect of product terms (ab) is not usually subject to the normal distribution, especially in a small sample, the traditional method of indirect effects, i.e., the Sobel Test (50), might be inaccurate. It is difficult to perform an indirect effect test in some complex models, and the bootstrap method is one of the best ways to do so (51, 52). The bootstrap was set to 1,000 times, and the ML (maximum likelihood) estimation method was used in the process of modeling with Mplus.

The Structure of Model

Without a loss of generality, taking the single mediation effect model with the independent variable moderation as an example, the corresponding equations are below (42, 53):

$\begin{array}{ll}\text{M}=a_0+a_1\text{X}+e_m& (1)\end{array}$

$\begin{array}{ll}\text{Y}=b_0+b_1\text{M}+c_1{}^{\prime }\text{ X}+c_2{}^{\prime }\text{ XM}+e_m& (2)\end{array}$

After bringing Equations (1) into (2) and rearranging all terms, the final equation is:

$\begin{array}{l}\text{Y}=b_0+b_1(a_0+a_1\text{X}+e_m)+c_1{}^{\prime }\text{X}+c_2{}^{\prime }\text{X}(a_0+a_1\text{X}+e_m)\\ +e_y=(b_0+a_0{b}_1)+[(c_1{}^{\prime }+a_0{c}_2{}^{\prime })+a_1(b_1+c_2{}^{\prime }\text{X})]\text{X}\\ +(b_1{e}_m+c_2{}^{\prime }X{e}_m+e_y)& (3)\end{array}$

In Equation (3), (b₀ + a₀b₁) is the simple intercept, and $[(c_1{}^{\prime }+a_0{c}_2{}^{\prime })+a_1(b_1+c_2{}^{\prime }\text{X})]$ is the simple slope, wherein $(c_1{}^{\prime }+a_0{c}_2{}^{\prime })$ is the simple direct effect and $a_1(b_1+c_2{}^{\prime }\text{X})$ is the simple indirect effect. According to this equation, there is no ordinary linear moderation but a quadratic moderation in the model. The regression coefficient of X to Y is also the linear combination of X.

Findings

Descriptive Statistics

The survey revealed that in accordance with the grading standard (i.e., score 40–60 for mild IA, score 60–80 for moderate IA, and score 80–100 for severe IA), 33.2% of students had mild IA, 9.3% had moderate IA, and 0.6% had severe IA. The statistical descriptions of relevant variables in the model are shown in Table 2.

TABLE 2

Table 2. Statistical descriptions and relation coefficient matrix.

Moderated Mediation Effect Model

By including PsyCap (X), ColAda (M_i), and IA (Y) and the interaction terms (XM_i) between PsyCap (X) and ColAda (M_i), and by setting covariances among M1, M2, and M3, this study built a multiple mediation effect model with the moderation effect of the independent variable. Relevant indicators in the output suggest that the model fits the data well. Being estimated using Mplus, the results of the model are shown in Table 3.

TABLE 3

Table 3. The output of multiple mediation model with IDV moderation.

PsyCap Has a Significant Impact on ColAda

As shown in rows 4 and 5 of Table 3, PsyCap promotes EmoAda, LeaAda, and IntAda. Each of their coefficients is statistically significant (p < 0.001, the same below). According to the standardized output, the relationship between PsyCap and LeaAda is the strongest (the standardized path coefficient is 0.435, SE = 0.020). The proportion of variance explained is also slightly higher. The coefficients of the other two mediating variables are nearly equal.

All Dimensions of ColAda Have Noticeable Impacts on IA

The relationship between ColAda and IA is statistically significant. Strong adaptation can reduce IA. In other words, maladaptation corresponds to a higher risk of IA. Specifically, the effects of LeaAda and IntAda are higher than those of EmoAda, as revealed by a comparison of the standardized output (row 5 of Table 3). The difference test is statistically significant.

ColAda Partially Mediates the Relationship Between PsyCap and IA

ColAda's mediating role is affirmed according to the model output (rows 8 and 9 in Table 3). The three dimensions of ColAda together consist of multiple mediating paths between PsyCap and IA. The mediation effects of EmoAda, LeaAda, and IntAda are all statistically significant. The values are −0.041, −0.109, and −0.087 under the condition that PsyCap is set at the average level. The 99% confidence intervals of the bias-corrected bootstrap method do not include zero points, which indicates the robustness of the results. The difference among three mediation effects is statistically significant. The corresponding test statistic is 0.046 (SE = 0.013). The total indirect effect, i.e., $\sum a_{1i}(b_{1i}+{c^{\prime }}_{2i}\mathrm{\text{X}})$, is equal to −0.236 and also passes the test of the bias-correction bootstrap method. The percentage of each mediation effect in the total indirect effect is 17.4, 46.2, and 36.9%.

After integrating ColAda as a mediating variable into the model, the direct effect between PsyCap and IA, i.e., ${c^{\prime }}_1+\sum a_{0i}{c}_{2i}^{\prime }$ remains significant. Each value of ColAda is centralized, so a_0i = 0, and the direct effect is just ${c^{\prime }}_1$. The value is −0.103 and is statistically significant, indicating that ColAda plays only a partial mediating role in this model. The total effect is −0.103–0.236 = −0.339, in which the indirect effect accounts for 69.6%. The result indicates that in increasing the score by a unit of 1 score relative to the average level, PsyCap can directly reduce the IA score of 0.103 and indirectly reduce the IA score of 0.236 through the mediation effect of the three dimensions of ColAda.

PsyCap Also Moderates the Relationship Between ColAda and IA

Considering the independent variable (IDV), i.e., PsyCap, as the moderating variable at the same time, the formula for the mediation effect is equal to $a_{1i}(b_{1i}+c_{2i}{}^{\prime }\text{X})$. The existence of X in the formula reflects PsyCap as the IDV also moderates the relationship between ColAda and IA, i.e., the second stage of the indirect effect. According to the corresponding p-value of the coefficients of the interaction terms (XM_i) between the mediating variables and IDV, it can be inferred that PsyCap is also a moderating variable that influences the relationships among EmoAda, IntAda, and IA separately, but is not a moderator for LeaAda and IA.

The moderation effects of the three dimensions of the ColAda are demonstrated in the coefficients of row 8 in Table 3. In this case, they are actually the conditional indirect effects under the condition that PsyCap is equal to zero. When PsyCap, as a continuous variable, takes two values separately, i.e., plus or minus one standard deviation (±13.111), the corresponding conditional mediation effects of EmoAda, LeaAda, and IntAda can be calculated separately. They are presented in rows 11 and 12 of Table 3. Overall, all of the effects are statistically significant, because none of their 99% bias-corrected confidence intervals includes zero points, which indicates that the results are highly robust⁴. According to the results of this model, because the relationship between LeaAda and IA is not moderated by PsyCap, all conditional indirect effects through LeaAda are close in value⁵.

The conditional mediation effects of the three dimensions of ColAda, as well as the relationships among EmoAda, IntAda, and IA individually moderated by PsyCap (horizontal axis takes ±SD), are shown in Figures 2–4⁶. Figure 2, referring to results of Table 3, reveals that, in general, an increase in PsyCap diminishes the risk of IA indirectly through the mediation effect of ColAda, but there are different modes in each path.

FIGURE 2

Figure 2. Moderated mediation effect of PsyCap on IA.

FIGURE 3

Figure 3. Moderated effect of PsyCap on EmoAda and IA.

FIGURE 4

Figure 4. Moderated effect of PsyCap on IntAda and IA.

Since not being moderated by PsyCap in the second stage of this model, the relationship between LeaAda and IA is linear, which means the indirect effect of PsyCap reducing IA via LeaAda has stable mode for students as a whole. The flip side of that is LeaAda can invariably influence IA in this causal path, or partially mediate the effect of PsyCap on IA on its own.

By contrast, the effects that PsyCap imposes on IA through the other two dimensions of ColAda are characterized by two different quadratic curves when PsyCap plays moderating roles simultaneously. Specifically, among college students as a whole, the indirect effect via EmoAda changes the effect from being smooth to being accelerated. Figure 2 shows that the protective effect against IA is not apparent when PsyCap is low, but it becomes more obvious in the group with high PsyCap as PsyCap increases, which suggests that it is relatively difficult to reduce IA by adjusting EmoAda in college students with low PsyCap. The mode is just the opposite for the mediation effect of IntAda. In the group with low PsyCap, along with the increase in PsyCap, the indirect effect via IntAda becomes more distinct. However, in the group with high PsyCap, the antagonism of PsyCap against IA is flattened. That is, in a relative sense, even for the group with low PsyCap, better IntAda can also reduce IA substantially.

Figures 3, 4 also reveal moderation effects of PsyCap on the second stage of the mediating process; that is, the respective relationships between EmoAda, IntAda, and IA. According to these figures, in the group with high PsyCap, the effect of EmoAda on reducing IA is more observable, and for IntAda, the situation is the opposite. Relative to the group with high PsyCap, in the group with low PsyCap, along with the rising of level of IntAda, it is more obvious that IA is reduced. It thus seems that good IntAda can help offset the disadvantage of low PsyCap for reducing college students' IA.

Conclusion and Discussion

Consistent with the findings of several previous studies, this study confirmed the negative relationships between PsyCap and IA (24) and the relationship pathways among PsyCap, ColAda, and IA (31, 32, 35).

Additionally, this study built a moderated multiple mediation effect model and found that when taking ColAda as mediating mechanism, PsyCap plays a triple role in the relationship with IA: (1) PsyCap has a direct effect on IA (of course, it does not exclude the possibility of isolating other mediating factors within it). (2) Through EmoAda, LeaAda, and IntAda, PsyCap also indirectly influences IA. A high level of PsyCap, through the mediation effect of ColAda, helps reduce the risk of IA. (3) PsyCap moderates the relationships among EmoAda, IntAda, and IA, respectively. For college students as a whole, LeaAda constitutes an invariable factor to reduce IA, and it is not subject to the moderation of PsyCap.

On the whole, PsyCap imposes a quadratic effect on IA. In other words, even not considering the direct effects, psychological capital not only influences certain other factors and thus indirectly affects IA, but also moderates the relationship between these factors and IA. The result echoes the findings described above, namely, the buffering and moderating effects that psychological capital has. The above results provide some clues to IA interventions for the college students population from a positive psychology perspective.

For college students, emotional and interpersonal maladjustment are critical risk factors for IA. The relationship between emotional patterns and IA has been identified, and high emotional stability and emotional intelligence (EI) can be helpful to decrease IA (54). Taking college students as a whole, this study found that reducing IA through emotional adjustment is conditional; it is dependent on the basic psychological qualities of the individual. Relatively speaking, a good EmoAda has a more pronounced effect against IA in the group with high PsyCap.

Moreover, it is the conclusion of many studies that social dysfunction as well as related loneliness, shyness, and other symptoms are important predictors of IA (55–57). The current study illustrates that keeping PsyCap unchanged, good IntAda indeed corresponds to a lower IA level, the protective function of IntAda against IA is higher, and the improvement is more evident even in the group with low psychological quality.

Confronting the widespread problem of IA among college students, these results suggest that psychological and/or social work interventions can start with the cultivation of positive PsyCap (i.e., PCI) and focus on enhancing the emotional, learning, and interpersonal adaptation of this population [(58): 213–216; (59)]. In the short term, assuming that PsyCap is relatively stable, a differentiation strategy should be adopted in the fight against IA: In a holistic sense, for college students with high PsyCap, the emotional adjustment intervention mode can be more well-targeted, while for those with low PsyCap, social skills training interventions are more suitable, as suggested by relevant evaluation studies (60, 61).

Finally, it is important to stress some limitations of this study. First of all, the data used for this study were collected from college students of a university, who were not assessed for any previous or current addictive or psychiatric disorders, or their family history, other than indicators of psychological capital, IA, school adjustment, etc. Further research will need to introduce and control these factors and more background variables to test the robustness of the model, form a more formal theoretical overview of PsyCap and IA, and design some experiments to evaluate the effects of emotional and social interaction interventions.

Data Availability Statement

The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.

Ethics Statement

The studies involving human participants were reviewed and approved by China Communist Youth League Committee at China University of Political Science and Law. Written informed consent for participation was not required for this study in accordance with the national legislation and the institutional requirements.

Author Contributions

JJ: conceptualization, methodology, writing–review and editing, and supervision. XB: design of study, investigation, data analysis, and writing–original draft. Both authors contributed to the article and approved the submitted version.

Conflict of Interest

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

Publisher's Note

All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.

Supplementary Material

The Supplementary Material for this article can be found online at: https://www.frontiersin.org/articles/10.3389/fpsyt.2021.712964/full#supplementary-material

Footnotes

1. ^This is a multiple mediation effect model with the independent variable moderation. A simplified form of this model can be found in Hayes [(41): 398] and Stride et al. (42). For practical examples, see (43).

2. ^The survey is completely anonymous and based on informed consent, with the Youth League Committee at the university as the executive agency responsible for supervising the process and ensuring that no harm is done to the participants. For details, see the certification and authorization letter submitted to the editorial board.

3. ^After data cleaning, the effective sample size meeting the requirements of this analysis was 2,133. Among them, 64.6% were girls and 35.4% were boys; the mean age was 19.6 years, with a standard deviation of 1.3 years; 20.2% were from rural and 79.8% were from urban; 31.6, 23.6, 31.8, and 13.0% were first-year, second-year, third-year, and fourth-year college students, respectively.

4. ^Partially, the conditional mediation effect of EmoAda is not statistically significant when PsyCap takes –SD, which suggests that the relationship between EmoAda and IA is not moderated by PsyCap for those students with a low level of EmoAda. However, overall, psychological capital has a moderating effect on the relationship between emotional adaptation and Internet addiction.

5. ^In order to test the robustness of this model's results across genders, we have run the model separately for male and female students. The results show that the mediation effect is robust. Moderation effects are largely robust, except for the emotional adaptation dimension, which was not statistically significant among male college students.

6. ^All three figures were exported by Mplus coding. See the relevant attachment.

References

1. Kraut R, Patterson M, Lundmark V, Kiesler S, Mukophadhyay T, Scherlis W. Internet paradox: a social technology that reduces social involvement and psychological well-being? Am Psychol. (1998) 53:1017–31. doi: 10.1037/0003-066X.53.9.1017

PubMed Abstract | CrossRef Full Text | Google Scholar

2. Young KS, Rogers RC. The relationship between depression and Internet addiction. CyberPsychol Behav. (1998) 1:25–8. doi: 10.1089/cpb.1998.1.25

CrossRef Full Text | Google Scholar

3. Sanders CE, Field TM, Miguel D, Kaplan M. The relationship of Internet use to depression and social isolation among adolescents. Adolescence. (2000) 35:237–342. doi: 10.1016/S0001-6918(00)00038-X

PubMed Abstract | CrossRef Full Text | Google Scholar

4. Windham RC. The Changing Landscape of Adolescent Internet Communication and Its Relationship to Psychosocial Adjustment and Academic Performance [Dissertations and Theses-Gradworks]. The George Washington University (2007).

Google Scholar

5. Kim K, Ryu E, Chon MY, Yeun EJ, Choi SY, Seo JS, et al. Internet addiction in Korean adolescents and its relation to depression and suicidal ideation: a questionnaire survey. Int J Nurs Stud. (2006) 43:185–92. doi: 10.1016/j.ijnurstu.2005.02.005

PubMed Abstract | CrossRef Full Text | Google Scholar

6. Kurta DG. Suicide risk in college students: the effects of internet addiction and drug use. Educ Sci Theory Pract. (2015) 4:841–8. doi: 10.12738/estp.2015.4.2639

CrossRef Full Text | Google Scholar

7. Berardis DD, D'Albenzio A, Gambi F, Sepede G, Valchera A, Conti CM, et al. Alexithymia and its relationships with dissociative experiences and internet addiction in a nonclinical sample. CyberPsychol Behav. (2009) 12:67–9. doi: 10.1089/cpb.2008.0108

PubMed Abstract | CrossRef Full Text | Google Scholar

8. Berardis DD, Fornaro M, Orsolini L, Ventriglio A, Vellante F, Giannantonio MD. Emotional dysregulation in adolescents: implications for the development of severe psychiatric disorders, substance abuse, and suicidal ideation and behaviors. Brain Sci. (2020) 10:1–5. doi: 10.3390/brainsci10090591

PubMed Abstract | CrossRef Full Text | Google Scholar

9. Goldberg I. Internet Addictive Disorder (IAD) Diagnostic Criteria. (1994). Available online at: https://web.archive.org/web/20160305015446/; http://psycom.net/iadcriteria.html (accessed July 1, 2019).

Google Scholar

10. Young KS. Internet addiction: the emergence of a new clinical disorder. Cyber Psychol Behav. (1998) 1:237–44. doi: 10.1089/cpb.1998.1.237

CrossRef Full Text | Google Scholar

11. Rumpf HJ, Tao R, Rehbein F, Petry NM. Internet addiction: a future addictive disorder? In: Petry N, editor. Behavioral Addictions: DSM-5^® and Beyond. New York, NY: Oxford University Press (2015). p. 71–99.

Google Scholar

12. Young KS, Yue XD, Ying L. Prevalence estimates etiologic models of Internet addiction. In: Young KS, De Abreu CN, editors. Internet Addiction: A Handbook and Guide to Evaluation and Treatment. Hoboken, NJ: John Wiley and Sons (2011). p. 3–17.

Google Scholar

13. Young KS, Rodgers RC. Internet addiction: personality traits associated with its development. In: 69th Annual Meeting of the Eastern Psychological Association. (1998). p. 40–50. Available online at: http://www.netaddiction.com/articles/personality_correlates-1.pdf (accessed July 1, 2019).

PubMed Abstract | Google Scholar

14. Brand M, Laier C, Young KS. Internet addiction: coping styles, expectancies, treatment implications. Front Psychol. (2014) 5:1256. doi: 10.3389/fpsyg.2014.01256

PubMed Abstract | CrossRef Full Text | Google Scholar

15. Hardie E, Tee MY. Excessive Internet use: the role of personality, loneliness and social support networks in Internet addiction. Aust J Emerg Technol Soc. (2007) 5:34–47.

Google Scholar

16. Hur MH. Demographic, habitual, and socioeconomic determinants of Internet addiction disorder: an empirical study of Korean teenagers. Cyberpsychol Behav. (2006) 9:514–25. doi: 10.1089/cpb.2006.9.514

PubMed Abstract | CrossRef Full Text | Google Scholar

17. Ko CH, Yen JY, Chen CC, Chen SH, Yen CF. Gender differences and related factors affecting online gaming addiction among Taiwanese adolescents. J Nervous Ment Dis. (2005) 193:273–7. doi: 10.1097/01.nmd.0000158373.85150.57

PubMed Abstract | CrossRef Full Text | Google Scholar

18. Stavropoulos V, Kuss D, Griffiths M, Motti-Stefanidi F. A longitudinal study of adolescent internet addiction: the role of conscientiousness and classroom hostility. J Adolesc Res. (2016) 31:442–73. doi: 10.1177/0743558415580163

CrossRef Full Text | Google Scholar

19. Luthans F, Youssef CM. Human, social, and now positive psychological capital management: investing in people for competitive advantage. Organ Dyn. (2004) 33:143–60. doi: 10.1016/j.orgdyn.2004.01.003

CrossRef Full Text | Google Scholar

20. Avey JB, Reichard RJ, Luthans F, Mhatre KH. Meta-analysis of the impact of positive psychological capital on employee attitudes, behaviors, and performance. Hum Resour Dev Q. (2011) 22:127–52. doi: 10.1002/hrdq.20070

PubMed Abstract | CrossRef Full Text | Google Scholar

21. Jafri MH. A study of the relationship of psychological capital and students' performance. Busin Perspect Res. (2013) 1:9–16. doi: 10.1177/2278533720130202

CrossRef Full Text | Google Scholar

22. Riolli L, Savicki V, Richards J. Psychological capital as a buffer to student stress. Psychology. (2012) 3:1202–7. doi: 10.4236/psych.2012.312A178

CrossRef Full Text | Google Scholar

23. Selvaraj PR. Using Positive Psychological Capital to Predict Mental Health in College Students: Implications for Counseling and Higher Education [Doctoral dissertation]. Ohio University (2015).

Google Scholar

24. Ya S, Wang L. The relation between psychological capital and internet addiction in college students. Chin School Doctor. (2013) 27:93–6.

Google Scholar

25. Simsek E, Sali JB. The role of internet addiction and social media membership on university students' psychological capital. Contemp Educ Technol. (2014) 5:239–56. doi: 10.30935/cedtech/6127

CrossRef Full Text | Google Scholar

26. Khera A, Kaushik S, Sharma SK. Impact of psychological capital on internet addiction: a study among university students in India. Int Res J Hum Resour Soc Sci. (2017) 4:55–65.

Google Scholar

27. Kandell JJ. Internet addiction on campus: the vulnerability of college students. Cyber Psychol Behav. (1998) 1:11–7. doi: 10.1089/cpb.1998.1.11

CrossRef Full Text | Google Scholar

28. Chou C, Chou J, Tyan NN. An exploratory study of internet addiction, usage and communication pleasure: the Taiwan's case. Int J Educ Telecommun. (1999) 5:47–64.

Google Scholar

29. Chou C, Condron L, Belland JC. A review of the research on Internet addiction. Educ Psychol Rev. (2005) 17:363–88. doi: 10.1007/s10648-005-8138-1

CrossRef Full Text | Google Scholar

30. Kuss DJ, Griffiths MD. Internet Addiction in Psychotherapy. Basingstoke: Palgrave Macmillan (2014).

Google Scholar

31. Lanthier RP, Windham RC. Internet use and college adjustment: the moderating role of gender. Comput Hum Behav. (2004) 20:591–606. doi: 10.1016/j.chb.2003.11.003

CrossRef Full Text | Google Scholar

32. Mei SL, Ge LJ, Kou CG, Zhang D, Chen YF, Yu YQ. Life events and adjustment of college students with internet addiction. Chin Ment Health J. (2008) 22:206–9.

Google Scholar

33. Yao B, Han W, Zeng L, Guo X. Freshman year mental health symptoms and level of adaptation as predictors of internet addiction: a retrospective nested case-control study of male Chinese college students. Psychiatry Res. (2013) 210:541–7. doi: 10.1016/j.psychres.2013.07.023

PubMed Abstract | CrossRef Full Text | Google Scholar

34. Bo A, Deng X, Fu H. Zhang R. The relationship between internet addiction and college adaptation. Study Monthly. (2011) 14:110–1.

Google Scholar

35. Wang Z, Xiao M, Kang Y, Chen X. Study on the relationship between the psychological capital and school adaptation of university students. Sci Educ Article Cult. (2015) 5:150–1.

Google Scholar

36. Li B, Ma HY, Guo YY. The retrospect and prospect of the mechanism of psychological capital. Psychol Res. (2014) 7:53–63.

Google Scholar

37. Avey JB, Luthans F, Jensen SM. Psychological capital: a positive resource for combating employee stress and turnover. Hum Resour Manag. (2009) 48:677–93. doi: 10.1002/hrm.20294

PubMed Abstract | CrossRef Full Text | Google Scholar

38. Walumbwa FO, Peterson SJ, Avolio BJ, Hartnell CA. An investigation of the relationships among leader and follower psychological capital, service climate,and job performance. Pers Psychol. (2010) 63:937–63. doi: 10.1111/j.1744-6570.2010.01193.x

CrossRef Full Text | Google Scholar

39. Cheung F, Tang CSK, Tang S. Psychological capital as a moderator between emotional labor, burnout, and job satisfaction among school teachers in China. Int J Stress Manag. (2011) 18:348–71. doi: 10.1037/a0025787

PubMed Abstract | CrossRef Full Text | Google Scholar

40. Wang H, Sui Y, Luthans F, Wang D, Wu Y. Impact of authentic leadership on performance: role of followers' positive psychological capital and relational processes. J Organ Behav. (2014) 35:5–21. doi: 10.1002/job.1850

PubMed Abstract | CrossRef Full Text | Google Scholar

41. Hayes AF. Introduction to Mediation, Moderation, and Conditional Process Analysis. A Regression-based Approach. New York, NY: Guilford Press (2018).

Google Scholar

42. Stride CB, Gardner S, Catley N, Thomas F. Mplus Code for the Mediation, Moderation, and Moderated Mediation Model Templates From Andrew Hayes' PROCESS Analysis Examples. (2015). Available online at: http://www.offbeat.group.shef.ac.uk/FIO/mplusmedmod.htm (accessed July 1, 2019).

Google Scholar

43. Gordon MN, Kosberg JI, Durkin DW, Keith DW, Jamie DC, Williamson GM. Caregiver mental health and potentially harmful caregiving behavior: the central role of caregiver anger. Gerontologist. (2010) 50:76–86. doi: 10.1093/geront/gnp099

PubMed Abstract | CrossRef Full Text | Google Scholar

44. Panayides P, Walker MJ. Evaluation of the psychometric properties of the Internet Addiction Test (IAT) in a sample of Cypriot high school students: the Rasch measurement perspective. Eur J Psychol. (2012) 8:327–51. doi: 10.5964/ejop.v8i3.474

CrossRef Full Text | Google Scholar

45. Widyanto L, McMurran M. The psychometric properties of the internet addiction test. Cyberpsychol Behav. (2004) 7:443–50. doi: 10.1089/cpb.2004.7.443

PubMed Abstract | CrossRef Full Text | Google Scholar

46. Baker RW, Siryk B. Student Adaptation to College Questionnaire (SACQ). Los Angeles, CA: Western Psychological Services (1989).

PubMed Abstract | Google Scholar

47. Juan O. The Revised and Application of Student Adaptation to College Questionnaire (SACQ) [Master Degree Dissertation]. Hunan Normal University (2012).

Google Scholar

48. Zhang K, Zhang S, Dong Y. Positive psychological capital: measurement and relationship with mental health. Stud Psychol Behav. (2010) 8:58–64.

PubMed Abstract | Google Scholar

49. Avey JB, Patera JL, West BJ. The implications of positive psychological capital on employee absenteeism. J Leadersh Organ Stud. (2006) 13:42–60. doi: 10.1177/10717919070130020401

PubMed Abstract | CrossRef Full Text | Google Scholar

50. Sobel ME. Some new results on indirect effects and their standard errors in covariance structural models. Sociol Methodol. (1986) 16:159–86. doi: 10.2307/270922

CrossRef Full Text | Google Scholar

51. MacKinnon DP, Lockwood CM, Hoffman JM, West SG, Sheets V. A comparison of methods to test mediation and other intervening variable effects. Psychol Methods. (2002) 7:83–104. doi: 10.1037/1082-989X.7.1.83

PubMed Abstract | CrossRef Full Text | Google Scholar

52. MacKinnon DP, Lockwood CM. Williams J. Confidence limits for the indirect effect: distribution of the product and resampling methods. Multivariate Behav Res. (2004) 39:99–128. doi: 10.1207/s15327906mbr3901_4

PubMed Abstract | CrossRef Full Text | Google Scholar

53. Preacher KJ, Rucker DD, Hayes AF. Addressing moderated mediation hypotheses: theory, methods, and prescriptions. Multivariate Behav Res. (2007) 42:185–227. doi: 10.1080/00273170701341316

PubMed Abstract | CrossRef Full Text | Google Scholar

54. Engelberg E, Sjöberg L. Internet use, social skills, and adjustment. Cyber Psychol Behav. (2004) 7:41–7. doi: 10.1089/109493104322820101

PubMed Abstract | CrossRef Full Text | Google Scholar

55. Yuen CN, Lavin MJ. Internet dependence in the collegiate population: the role of shyness. Cyber Psychol Behav. (2004) 7:379–83. doi: 10.1089/cpb.2004.7.379

PubMed Abstract | CrossRef Full Text | Google Scholar

56. Shepherd RM, Edelmann RJ. Reasons for internet use and social anxiety. Pers Indiv Differ. (2005) 39:949–58. doi: 10.1016/j.paid.2005.04.001

CrossRef Full Text | Google Scholar

57. Kim J, LaRose R, Peng W. Loneliness as the cause and the effect of problematic Internet use: the relationship between Internet use and psychological well-being. Cyber Psychol Behav. (2009) 12:451–5. doi: 10.1089/cpb.2008.0327

PubMed Abstract | CrossRef Full Text | Google Scholar

58. Luthans F, Youssef CM, Avolio BJ. Psychological Capital: Developing the Human Competitive Edge. Oxford, UK: Oxford University Press (2007).

Google Scholar

59. Dello Russo S, Stoykova P. Psychological Capital Intervention (PCI): a replication and extension. Hum Resour Dev Q. (2015) 26:329–47. doi: 10.1002/hrdq.21212

PubMed Abstract | CrossRef Full Text | Google Scholar

60. Batigün AD. Internet addiction: an evaluation in terms of loneliness and interpersonal relationship styles. Anatolian J Psychiatry. (2010) 11:213–9.

Google Scholar

61. Karimzadeh N. Investigating the relationship between Internet addiction and strengthening students' social skills. Educ Res Rev. (2015) 10:2146–52. doi: 10.5897/ERR2015.2338

CrossRef Full Text | Google Scholar