Role of self-compassion and online/offline integration on internet addiction, aggression, and psychological well-being: A mediation analysis

Abstract

Context:

Internet addiction is known to harmfully affect psychological health. However, few researches have examined its plausible related factors and respite from its effects.

Aim:

This study aims to examine the relationship between internet addiction, aggression, psychological well-being, and the mediating effects of self-compassion and online/offline integration, on them.

Materials and Methods:

Data from 459 university students aged between 18 and 21 years were purposively selected from various disciplines and locations in India. Data were collected using an online demographic survey and standardized psychological measures.

Statistical Analysis:

Data analysis was performed using Partial Least Squares (PLS) path analysis. Direct and indirect effects and path coefficients were observed using PLS structural equation modeling.

Results:

The study indicated a possible influence of internet addiction on psychological well-being and aggression. It seems to increase aggression levels and lower psychological wellbeing. Online/offline integration and self-compassion partially mediated and dampened its adverse effects.

Conclusions:

Online/offline integration and self-compassion have a possibility to therapeutically diminish the ill-effects of internet addiction, lower aggression levels and promote psychological health of students who use internet extensively. This study provides a basis for further research to establish causal inferences.

INTRODUCTION

The indispensability of internet for commerce, academics, information, communication, and entertainment has, while easing/enriching life, rendered us dependent on it. Excessive dependence leads to its addiction, which in turn predisposes to loneliness, low self-esteem, erratic sleep schedules, impulsivity, depression, and anxiety, thus, lowering life quality/satisfaction.[1] Internet addiction disorder (IAD) is found commoner among students.[2] It adversely impacts their academic, social, occupational, and mental health.[3]

Internet addiction, aggression and psychological wellbeing

Some key manifestations of IAD, such as compulsive use, withdrawal/disinterest in real-life social interactions/functions, are associated with antisocial behaviors that derail their real-life/offline goals.[3,4,5] It assumes sinister overtones when aggression transforms into physical violence, cyberbullying, and passive-aggressive behavior, such as trolling, stalking and online verbal abuse, either overtly or anonymously.[4] Evidence suggests a correlation between internet addiction and aggressive behavior with a possibility of common neurobiological mechanism.[6] Consequently, the overall psychological well-being of an individual is adversely affected by IAD.[7] Thus, it is important to find its remedy.

Self-compassion and online-offline integration as mediators

Self-compassion and online-offline integration are psychological concepts which aim to mitigate the above ill-effects of IAD through self-care mechanisms and harmonious integration of an individual’s online and offline lives.[8,9] The concept of self-integration principle emphasizes on the merger of an individual’s online and offline lives for enriched development and prosperity.[10] By prioritizing offline self and maintaining communication, merger of self-identity, relationships and social function is probable. Higher level of online/offline integration leads to increased life satisfaction and lower internet addiction.[9] Self-compassion refers to a well-meaning, kind way of coping with one’s personal failures, disappointments and acceptance of one’s shortcomings.[11] The virtues of self-kindness, common humanity and mindfulness are roofed by this term.[11] Previous research on this establishes its buffering effect on cyberbullying and cyber victimization, depression, anxiety, loneliness, and addictions.[12,13,14,15] Self-compassion leads to incremental changes in psychological wellbeing.[16] Lack of regard for self and others, finding refuge online to hide from harsh realities of life and a preference to the online self-identity over real identity are probable causative factors of online aggression and psychological ill health. However, there are limited studies exploring the role of the above-mentioned concepts on IAD and its manifestations among students. This study is a preliminary effort in addressing this paucity.

The objective of the study was to study the mediating role of online/offline integration and self-compassion on aggression and psychological wellbeing of excessive internet users through the following hypotheses: (H1) There would be a direct relationship between internet addiction and aggression among students; (H2) There would be an indirect effect of internet addiction on aggression through self-compassion; (H3) There would be an indirect effect of internet addiction on aggression through online/offline integration; (H4) There would be a direct relationship between internet addiction and psychological wellbeing among students; (H5) There would be an indirect effect of internet addiction on psychological wellbeing through self-compassion; (H6) There would be an indirect effect of internet addiction on psychological wellbeing through online/offline integration; (H7) The relationship between internet addiction and aggression would be mediated by both self-compassion and online-offline integration; (H8) The relationship between internet addiction and psychological wellbeing would be mediated by both self-compassion and online-offline integration among students.

In the hypothesized model, the latent constructs along with their indicators represented on the periphery formed the outer model. The inner model signified the direct and indirect relationship among the latent constructs. IAD is the independent variable that affects the dependent variables, i.e., aggression and psychological wellbeing, and the relationship among them are mediated through online/offline integration and self-compassion.

MATERIALS AND METHODS

Sampling and data collection

For this study, purposive sampling was undertaken to recruit participants through an online survey during the COVID shutdown period April-June 2020. A Google document link through E-mail or WhatsApp was sent to only those willing college students who registered their E-mail ids and acknowledged usage of internet for more than 4 h per day apart from their academic purposes. The form consisted of basic demographic queries such as age, institution of study/work, education, family income in international normalized ratio, duration of study in online classes, and time spent in leisure activities online. Before the administration of tests, clear instructions stating the purpose of the study was provided, confidentiality was assured and informed consent was taken. Only completely filled forms were recorded. The tests were not translated in any other language and no incentives were given. The protocol was approved by the research and ethics committee of the institute. The sample size was calculated and found to be sufficient based on Cochran’s formula at 95% confidence interval, precision of 5% at prevalence rate of 19%, as done in previous studies.[7,17]

Measures

Young’s scale of internet addiction is a reliable and valid scale for evaluating the level of addiction.[18] It consists of 20 items, with each item scored on a five-point Likert scale. It covers six factors, namely, salience, excessive use, neglect of work, anticipation, lack of control, and neglect of social life. Cronbach’s Alpha (a) in this study was found to be 0.835.

Online-offline integration scale is a short 15 item questionnaire which maps the level of integration between an individual’s online and offline selves.[9] It has three subscales, self-identity integration, relationship integration, and social function integration. Its scoring is based on four-point Likert scale. In the present study, Cronbach’s a coefficient was 0.87, indicating good internal consistency.

Self-compassion scale is a five-point Likert questionnaire with 26 items to measure the level of compassion an individual experiences for himself.[11] It has three dichotomies, self-kindness versus self-judgement; common humanity versus isolation; mindfulness versus over identification. Cronbach’s a coefficient for this scale was calculated to be 0.85.

Ryff’s scale of psychological well-being is a 42-item questionnaire for the assessment of psychological well-being consisting of six dimensions, namely, autonomy, environmental mastery, personal growth, positive relations with others, purpose in life, and self-acceptance.[19] The Cronbach’s a coefficient of the scale was found to be 0.882.

Buss Perry aggression questionnaire is a five-point Likert scale questionnaire consisting of 29 items that measure the overall aggression and its individual components, namely, physical aggression, verbal aggression, anger, and hostility.[20] The Cronbach’s a coefficient for this scale was found to be 0.9.

The subscales of the standardized measures form the indicators of their respective latent constructs.

Statistical analysis

Descriptive statistics such as mean, standard deviation, frequencies, and percentages for all variables and their correlations were calculated using SPSS statistics for Windows. Version 25.0. IBM Corp. Armonk, NY, USA; 2017. The research model was tested and mediation analysis conducted using partial least square Partial Least Squares-structural equation modeling (PLS-SEM) on SmartPLS.[21]

Assessment of the measurement and structural model

For the assessment of measurement model, the following were evaluated: Reflective indicators, indicators’ construct reliability along with convergent validity and discriminant validity.[22] The average variance extracted (AVE) of the constructs and outer loadings were used for assessing convergent validity. In order to check the discriminant validity, Fornell-Larcker criterion and Heterotrait-Monotrait (HTMT) 0.9 criterion were considered.[23] Apart from Cronbach’s a, the composite reliability (CR) and rho indexes for internal consistency were checked.

After the analysis of the measurement model, the structural model was examined by evaluating the path coefficients, coefficient of determination (R²), and the effect size (f²). This method is appropriate for a smaller sample size because it does not rely on the normality assumption.[22] To test the mediation effects the direct and indirect effects were observed and then the type of mediation was examined.

If the direct effect is insignificant whereas the indirect effect is significant, then a full mediation is observed, however, if both direct and indirect effects remain significant then, the mediation is assumed to be partial.[21]

RESULTS

In this study, 462 forms were collected from various locations in India; however, 459 forms were analyzed as the rest were found to have invalid responses.

Sample characteristics

Of the 459 students, males comprised almost twice as much as females (167 [63.6%]: 262 [36.4%]). Age distribution was even (48.4% between 18 and 19 years: 51.6% between 20 and 21 years). There was a preponderance of middle (39.9%) and high family income groups (34.9%) compared to low family income group (25.3%). Students hailed predominantly from Delhi (67.7%), West Bengal (16.3%), and Punjab (10.2%). Almost half of the students were enrolled in undergraduate programs and the other were in postgraduate programs (234 [51%]: 225 [49%]). Although the average internet usage among students for academics was 6.3 h per day, however, the internet usage for nonacademic activities ranged widely: 43% for 4–5 h, 53.6% between 5 and 7 h and 3.5% for above 7 h. The descriptive characteristics of participants for all variables in the model are shown in Table 1.

Table 1.

Characteristics of participants for variables in the model

Variable	n	Internet addiction		Online offline integration		Self- compassion		Aggression		Psychological wellbeing
		Mean	SD	Mean	SD	Mean	SD	Mean	SD	Mean	SD
Gender
Female	167	1.2	0.42	40.79	12.69	3.82	0.59	65.24	19.60	79.86	12.60
Male	292	1.8	0.50	24.06	13.83	2.85	0.90	90.95	20.66	65.27	16.97
Education
UG	234	1.4	0.51	37.55	14.18	3.56	0.88	70.74	22.13	78.38	15.17
PG	225	1.8	0.52	22.45	13.22	2.83	0.83	92.88	19.80	62.46	14.95
Age
18	122	1.2	0.44	42.84	10.82	4.07	0.31	61.84	12.40	86.70	9.65
19	100	1.5	0.50	30.74	12.77	3.59	0.45	74.92	17.90	74.90	8.46
20	119	1.6	0.49	29.24	11.64	3.00	0.70	85.92	18.50	65.25	11.90
21	118	2.0	0.52	17.44	15.20	2.19	0.79	103.30	22.19	55.62	17.13
Family Income
Low	116	1.4	0.60	35.12	18.16	3.46	0.98	71.44	28.67	75.88	16.73
Medium	183	1.5	0.52	34.16	14.44	3.37	0.93	77.64	20.76	75.75	15.82
High	160	1.8	0.48	21.95	11.14	2.82	0.76	93.48	17.33	60.82	14.10

n – number of participants; SD – Standard deviation; UG – Undergraduate; PG – Postgraduate

To test the hypothesized model [Figure 1a], a two-stage approach was applied. First, the measurement model, representing the outer model, was assessed followed by the structural model, representing the inner model as prescribed.[24]

Figure 1.

Hypothesized model and structural model. (a) Hypothesized model. (b) Structural model with path coefficients and R² values

Measurement model

In this study, Cronbach’s a for the constructs of the model ranged between 0.835 and 0.905 and CR ranged between 0.864 and 0.916, which indicate good reliability. Convergent validity was established with AVE having value exceeding the recommended value of 0.5 of the constructs, rho values above 0.7 and the factor loadings above 0.65 [Table 2].[25]

Table 2.

Results of reflective measurement model

Latent constructs	Values	Indicators and outer loadings
		Lack of control	Neglect of social life	Salience	Anticipation	Neglect of work	Excessive use
IAT
α	0.835	0.894	0.89	0.813	0.606	0.614	0.603
rho_A	0.864
CR	0.891
AVE	0.675
Latent constructs	Values	Social function integration	Self-identity integration	Relationship integration
Online and offline integration
α	0.872	0.854	0.914	0.907
rho_A	0.882
CR	0.921
AVE	0.796
Latent constructs	Values	Isolation	Mindfulness	Common humanity	Over identification	Self-judgement	Self-kindness
SCS
α	0.859	0.801	0.806	0.868	0.639	0.734	0.834
rho_A	0.880
CR	0.897
AVE	0.596
Latent constructs	Values	Environmental Mastery	Autonomy	Personal growth	Purpose in life	Positive relation with others	Self-acceptance
PWB
α	0.882	0.847	0.866	0.737	0.736	0.720	0.851
rho_A	0.888
CR	0.911
AVE	0.632
Latent constructs	Values	Physical Aggression	Anger	Hostility	Verbal Aggression
AGR
α	0.905	0.799	0.934	0.941	0.852
rho_A	0.916
CR	0.934
AVE	0.78

CR – Composite reliability; AVE – Average variance extracted; α – Cronbach alpha coefficient; rho_A – Reliability estimate; AGR – Aggression; PWB – Psychological well-being; SCS – Self-Compassion Scale; IAT – Internet addiction

The Fornell–Larcker test was calculated using the square root of AVE and was observed to be higher in value in comparison to the correlations between constructs. The HTMT values for all pairs of variables were <0.9, thus, establishing the discriminant validity between indicators of the same construct and between indicators of different constructs.[25] The confirmatory factor analysis of the outer model reaffirms the reliability and validity of the well-established scales used in the study.

Structural model

The hypothesized associations between the constructs of the proposed structural

model were evaluated by bootstrapping resampling process (5000 subsamples) to study the significance of confidence intervals and t-values [Table 3]. The significance of reflective indicators’ loadings was found to be significant at P < 0.1 level. The VIF values for all variables in the study were observed to be <3, recommended value being <5.[22] Thus, there was no multicollinearity observed among the exogenous constructs in the model.

Table 3.

Results of structural model

Path	Direct effect	Coefficient	t	2.50%	97.50%	f2
H1=c’	IAD -> AGR	0.274	9.496	0.219	0.331	0.185
b1	IAD -> OOIS	−0.729	32.83	−0.771	−0.684	1.134
H4=d’	IAD -> PWB	−0.168	3.439	−0.265	−0.075	0.032
a1	IAD -> SCS	−0.536	13.372	−0.613	−0.457	0.413
b2	OOIS -> AGR	−0.195	7.467	−0.245	−0.142	0.113
e2	OOIS -> PWB	0.139	3.34	0.056	0.218	0.026
f1	OOIS -> SCS	0.343	8.007	0.258	0.427	0.169
a2	SCS -> AGR	−0.538	20.062	−0.591	−0.485	0.704
e1	SCS -> PWB	0.588	13.608	0.505	0.671	0.381

P<0.001; For column 1 please refer Figure 1a. IAD – Internet addiction; OOIS – Online/offline integration; SCS – Self-compassion; AGR – Aggression; PWB – Psychological well-being

The model’s fit was evaluated based on the recommended values of Standardized Root Mean Square Residual (SRMR), Normed Fit Index (NFI), and rms theta.[23] The value of SRMR was 0.052, i.e., <0.08 and NFI was observed to be >0.9 representing a good model fit, along with other fit indicators that were noted to be acceptable. To measure of the model’s predictive accuracy, R² was examined where values of 0.25, 0.50, and 0.75 refer to weak, moderate, and strong, respectively.[25]

The R² values of the model were noted to be moderate and substantial for online/offline integration, self-compassion, aggression, and psychological wellbeing, with values of 0.531, 0.673, 0.866, and 0.703, respectively [Figure 1b].[22]

The predictive relevance (Q²) values were noted to be 0.444, 0.626, 0.563, 0.487 for the endogenous variables, i.e., self-compassion, aggression, online/offline integration, and psychological wellbeing, respectively.[22] This indicates that the model has good predictive relevance. Further, according to the results of the path analysis, 86.6% of the variance of aggression and 70.3% of the variance of psychological wellbeing of the study population is explained by IAD, online/offline integration and self-compassion [Figure 1b]. The f² evaluates the usefulness of each construct to the model adjustment and its values of 0.02, 0.15 and 0.35 are considered as small, medium and large effect size.[22] IAD showed a moderate effect size (f² = 0.185) on aggression, but a small effect size (f² = 0.032) on psychological wellbeing. A large effect size of 0.413 was noted on self-compassion and 1.134 on online/offline integration [Table 3].

Mediation analysis

Mediation analyses were performed to assess the roles of online/offline integration and self-compassion on the following relations: IAD and aggression; IAD and psychological wellbeing [Table 4].

Table 4.

Results of mediation analysis

Path	Specific indirect effect	Point estimate	t	Bootstrap CI (2.5%-97.5%)
H2: a1a2	IAD -> SCS -> AGR	0.289*	10.88	0.237-0.341
H3: b1b2	IAD-> OOIS -> AGR	0.142*	7.1	0.102-0.181
H5: a1e1	IAD -> SCS -> PWB	−0.316*	9.42	−0.384-−0.252
H6: b1e2	IAD -> OOIS -> PWB	−0.101*	3.33	−0.16-−0.041
H7: b1f1a2	IAD -> OOIS -> SCS -> AGR	0.135*	7.38	0.1-0.172
H8: a1f1e1	IAD -> OOIS -> SCS -> PWB	−0.147*	6.78	−0.191-−0.107
Total effects	IAD-> AGR	0.839*	70.78	0.814-0.862
	IAD-> PWB	−0.732*	34.12	−0.774-−0.688

*P<0.001, abbreviations for column 1 please refer to Figure 1. IAD – Internet addiction; OOIS – Online/offline integration; SCS – Self-compassion; AGR – Aggression; PWB – Psychological well-being; CI – Confidence interval

The results revealed a direct effect of IAD on aggression (b =0.274, t = 9.49, P < 001) and on psychological wellbeing (b =-0.168, t = 3.43, P < 001) [Table 3] proving H1 and H4. Total effect of IAD on aggression and on psychological wellbeing was found to be b=0.839, t = 70.781, P < 001 and b =-0.732, t = 34.121, P < 001 respectively. With the inclusion of online/offline integration in the relationship as a mediator between IAD and aggression, indirect effect was found (b =0.142, t = 7.098, P < 001), similarly so, on the inclusion of self-compassion as a mediator (b =0.289, t = 10.884, P < 001). Further, results demonstrated partial mediation effect of online/offline integration in the relationship between IAD and psychological wellbeing (b =-0.101, t = 3.331, P < 001) and likewise when self-compassion was considered as a mediator (b =-0.316, t = 9.421, P < 001) supporting hypotheses H2, H3, H5 and H6.

When both online/offline integration and self-compassion were taken together as mediators, sequentially, IAD still exerted a lesser yet an indirect effect on aggression (b =0.135, t = 7.386, P < 001) and on psychological wellbeing (b =-0.191, t = 6.783, P < 001) which prove H7 and H8 [Table 4]. This implies that online/offline integration and self-compassion partially effect the relationships individually and also in combination as mediators.

Results demonstrate that all paths are significant; however, the paths of IAD to aggression and IAD to psychological wellbeing through self-compassion have the highest path coefficients in the model. Partial mediating effects of the mediators (online/offline integration and self-compassion) explain the relationships (IAD and aggression; IAD and psychological wellbeing) to an extent as direct and indirect relationships remain statistically significant.

DISCUSSION

The study was premised on understanding the relationships between internet addiction, aggression, and psychological wellbeing of students who use internet intensively and whether their levels of online-offline integration and self-compassion have an influence on these relationships thus mentioned. The study was done through an online mode due to the ongoing pandemic making data collection in-person exigent. In order to avoid bias, the usage of internet for academics and nonacademic purposes was distinguished as online classes during pandemic required internet as a default. College students as young adults are known to use internet maximally and oftentimes are more prone to addiction.[26,27] However, all intensive users cannot be generalized as being addicted to internet. Thus, a focus on intensive users who use internet for above 3.5–5 h[28] apart from academics was considered for the study to collect a homogenous sample.

Further, for mediation analysis, PLS-SEM provides an appropriate inference framework.[25] It facilitates interpretation, the estimation of complicated mediation models in a single analysis. As variables in a causal relationship can be both cause and effect in a hypothesized mediation process, the simultaneous nature of indirect and direct effects, the dual role of the mediator, and effect of the intervention are appropriately expressed using SEM instead of standard regression analysis.[29] SEM also provides model-fit information to test the consistency of the mediational model. In addition, PLS-SEM does not rely on strict data assumptions such as sample size and distribution as compared to covariance-based SEM.[22] In the sample of 459 students who are heavy internet users, 24.1% were found to be prone to addiction and 3.1% were probably addicted severely as per the scale range of internet addiction test by Young.[18] This finding is in line with previous studies that provide prevalence rate in the range of 2%–24.8% of internet addiction.[7] India being second among the internet using nations, with maximum proportion being new millennials, such a high proportion represents alarming numbers.[26,27] The study reinforces few facts as well as adds to the repository of knowledge of mediating factors that may emerge as intervention strategies to reduce the ill effects of excessive internet usage. The previously established facts that internet addiction promotes aggression and inferior psychological health; widens the divide between online and real self; and lowers compassion toward self are supported/reinforced by this study.[30,31,32,33,34,35] Hitherto, unexplored association between online/offline integration level and several other psychological factors unraveled by this study are elaborated below.

Low-integrated individuals, with widened online/offline divide, tend to avoid real-life situations, hide behind virtual identities, and mask loneliness.[9] This may trigger dissatisfaction with their real lives leading them to engage in toxic disinhibition, aggressive and addictive internet behavior.[35,36] This study suggests that online/offline integration is negatively associated with and may, thus, aid in reducing aggression levels of students who use internet excessively, a finding hitherto unreported in literature. Higher levels of integration were also seen to have a positive effect on the psychological well-being of young internet users, a finding in line with previous researches.[9] According to the suggested model in this study, the mediating influence of self-compassion seems to lower aggression levels among youth and may impact their psychological well-being positively.[37,38] This implies that when individuals begin lessening their online/offline divide, become mindful during online activities, feel connected to other humans and commence focus on self-care, then they may be able to restrain their overtly aggressive online behavior, such as, cyberbullying raucous commenting, trolling or cyber stalking; and consequently improve their mental health.[11,36,39]

Furthermore, IAD widens the gap between online and offline (true) identity of individuals, their social relationships, and interactions by lowering online/offline integration, which is reconfirmed by this study.[9,10] IAD also seems to lower all the positive components of self-compassion namely self-kindness, common humanity, and mindfulness.[11,34] This may explain aggression among youth who reflect their low compassion levels by increased verbal abuse and hostility on online platforms.[5] These indirect effects of internet addiction on aggression are significant, sustained, and self-propagating. Therefore, in students using internet for extended hours, this study reveals, intervention by enhancing self-compassion and online/offline integration levels may thwart overtly aggressive and abusive online behavior to a certain extent. This interesting observation may help provide a remedial opportunity to reduce aggression observed among youth. However, these effects outlined above are mitigated partially and fail to fully explain how IAD influences aggression. This suggests that there are other psychological factors beyond self-compassion and integration that may buffer the negative impact of IAD.

Online/offline integration and self-compassion are perceived to enhance psychological health both independently and synergistically. Higher integration level across online and offline domains, and self-compassion appears to incline towards enhanced life satisfaction and positive perceptions of the internet may possibly alleviate depression and anxiety when incorporated into mental health intervention strategies.[8,9,40] This implies that self-compassion tends to promote acceptance of shortcomings in others whilst nurturing deeper sense of community, positive relationships and interactions both online and offline, and overall wellbeing.[41]

This is an initial study focusing on college students in India that have examined the role of online/offline integration and self-compassion as mediators in the relationship between internet addiction, aggression, and psychological well-being using PLS-SEM.

Internet addiction and aggression may be reduced using few interventional therapeutic strategies. Integrated internet use by time tracking applications, auto prompts, and reminders of real-life goals may aid in controlling the problem of immersion among internet addicts.[9] The use of applications that have audio-visual interactive features akin to real-life interactions, such as LinkedIn, WhatsAapp, Google Meet, may aid in social bonding and resolve withdrawal from real-life social interaction.[42] Further, virtual reality game developers could design games that incorporate such integration strategies.[43]

Identification and tagging of cyberbullies, cyber stalkers, and anonymous accounts tend to thwart toxic disinhibition as internet users tend to behave cautiously and well when identified.[35] The practice of mindfulness, guided meditations, focus-diaries, self-acceptance of being a fallible human and as a unit of common humanity may reduce the sense of failure from real life, hostility, and low self-esteem.[41] Identity integration, social interface integration may bring a radical shift in purpose in life, personal growth, and overall wellbeing among heavy internet users prone to its addiction.[9,34]

Limitations and future research

This study provides preliminary evidence of self-compassion and online-offline integration as partial mediators lowering the effect of problematic internet use on aggression levels and psychological wellbeing. However, the present study has few limitations. Owing to its cross-sectional design, possible selection bias due to purposive sampling, and the voluntary enrolment of participants for the study, causal inferences could not be firmly established. In addition, this study relies on self-report measures and focused sampling of English-speaking college students which may affect its generalizability to other populations. There may be other factors that can explain the relationships which were not in the scope of the study and thus form the basis for further research in this domain.

The above limitations may be overcome by longitudinal and experimental studies to establish cause-effect relationships implied in this research. It is vital to further this study by observing the mitigating effects of online/offline integration and self-compassion on other psychological outcomes, such as perceived loneliness, perceived social support, online gaming addiction, and personality factors. Further research with other sociodemographic variables, such as population of IT professionals, gamers, and other communities may prove interesting.

CONCLUSIONS

This study establishes the negative influence of online-offline integration and self-compassion on internet addiction and aggression among youth and positive influence on psychological wellbeing among them. It provides the basis for the inclusion of the above factors in intervention programs that aim at lowering addiction and problematic internet usage.

Financial support and sponsorship

Nil.

Conflicts of interest

There are no conflicts of interest.