Turning Lemons into Lemonade: Social Support as a Moderator of the Relationship Between Technostress and Quality of Life Among University Students

Introduction

Pervasive use of information and communication technologies has reached to an extent that is causing health and wellbeing deterioration¹ by affecting the overall quality of life of youth. Excessive use of internet-based technologies to reduce the stress caused by the use of those technologies to a level of addiction is taking a toll on the quality of life and health of the younger generation.² Their cutoff from social life leading to physical and mental health deterioration can probably be cured by using social support as an antidote to internet addiction. The young generation all over the world is almost equally affected by internet addiction and technological stress.^3,4 The average time adolescents spend online has significantly increased, from about 8 hours weekly in 2005 to 18.9 hours in the last decade.⁵ This substantial engagement, particularly during the crucial stages of adolescence, raises questions about its association with potential declines in mental, quality of life, and social well-being.⁶ Recent data highlights an alarming trend in the pervasive use of information and communication technologies, particularly among the youth, which is raising concerns about health, quality of life, and wellbeing deterioration. For instance, in Taiwan, a staggering 99.5% of individuals aged 15–24 are regular internet users, as reported by Chern, Huang.⁷ This widespread use has been paralleled by a noticeable increase in internet addiction among adolescents and youths, including university students. Studies have indicated that internet addiction prevalence in this demographic ranges from 5.9% to 26.8% in Taiwan.⁸ Furthermore, a comprehensive review revealed that the incidence of internet addiction among adolescents in various global regions, both Western and non-Western, spans from 1.98% to 35.8%.⁹ These figures underline the growing concern that extensive engagement with information and communication technologies is contributing to a rise in health and quality of life issues among young people worldwide. The youth in universities which embodies a significant portion of this affected population faces distorted educational performance outcomes in addition to social challenges.^10,11 It is also evidenced that quality of life and wellbeing of the university students is undermined by technostress.¹² Given the high level of engagement with technology in youth it is necessary to find linkage between technostress, internet addiction and quality of life of university students. In developing countries like India, younger people are equally suffering from the stress caused by internet-based technologies. Therefore, it warrants a deep probe into the causes of stress caused by excessive use of technologies, its effects on behavior (eg, internet addiction), and quality of life (physical, mental and social health) while considering the role of society in overcoming these negative effects from the life of miserable youth through social support. This study is an attempt to empirically test the technostress and internet addiction as sequential antecedents of quality of life (QoL) in the presence of social support as a moderator that can help in alleviating the deteriorating quality of life because of internet addiction.

Technostress is an unrecognized disorder of the modern digital world that has several implications for the physical and mental health and well-being of people, particularly young people.^13,14 The over-dependence of people on internet-based devices causes stress referred to as the technostress¹⁵ that results from an inability to properly manage the time spent on the use of internet-connected technologies^16,17 leading to addictive behavior in users. Technostress indicates the negative mental state of people in the modern digital world.¹⁸ People suffering from technostress are characterized by some cognitive symptoms such as memory disorders, irritability, and poor concentration.¹⁹ The studies have found that technostress leads to job dissatisfaction, poor job performance, memory problems, and sleep disorders²⁰ that influence the mental and physical health of people.²¹

Addiction to any substance and/or behavior such as internet usage is an important outcome of stress.²² The technostress generates addictive behavior in people toward internet usage called internet addiction¹ which is similar to the findings of studies in the field of medical sciences.²² However, very few research studies have examined the impact of technostress on the IA of people.²³ Internet addiction that is characterized by inability of individuals to avoid internet use is adversely affecting their QoL.²⁴ This is a mental disorder that reduces the life satisfaction attained by the interaction with friends, family, and the natural environment.²⁵ IA leads to physical inactivity, insomnia, social disorders, and depression in most people.²⁶ Studies have found that IA causes many health problems which deteriorate physical and mental health causing multi-dimensional impairments to QoL, particularly among younger adults.²⁷ Such issues in younger adults may affect their career and social support system in future life as these are negative experiences that may accumulate over one’s life and may adversely impact QoL in later phases of life. Hence, proper management of stress caused by a technology-dependent lifestyle leading to IA may help in improving the QoL of people.

In general, the QoL indicates the well-being of societies and the physical and mental health of individuals in particular. The concept of QoL is used in a wider context and often includes social, psychological, and physical relationships, and environmental features.²⁸ The technostress along with IA significantly influences the mental and physical quality of life of people. Previous studies have found that the QoL of people is critically influenced by stress-producing human behavior such as IA.²⁹ However, the awareness of most people, particularly young people regarding the health consequences of excessive usage of the internet is low which causes several health issues during the later stages of life.³⁰ Additionally, young people are less concerned with healthy lifestyle behavior such as social interaction which further accelerates the deterioration of mental and physical health affecting QoL.³¹

Social support has been found to be an important factor that plays a crucial role in minimizing the negative impact of IA³² and in the improvement of QoL.³³ Therefore, it is important to identify the impact of technostress-induced internet addiction on the QoL of people and how their influence can be moderated by the social support system. The negative consequences of internet addiction on the mental, physical, and social health of people causing deterioration of all aspects of their QoL³⁴ can be managed through appropriate social support.³⁵ Understanding these relations may help in identifying the conditions that improve and/or lower mental and physical health, which is important for making strategies for promoting the QoL of people. Hence, it feels necessary to understand the relationship between technostress, IA, and QoL, and the moderating effect of social support on improving the QoL of people. As per our knowledge, no study has examined the impact of technostress on QoL while considering the IA as a mediator between them and social support as a moderator between IA and QoL (See Figure 1).

Figure 1 Conceptual model of the study.

The purpose of the present study is to find the effect of technostress on quality of life through internet addiction considering the moderation effect of social support between internet addiction and quality of life on Indian youth. The sample population of the study comprised of a students enrolled in the graduation and post-graduation programs in various universities located in state of Gujarat, India. The study advances the research by Brooks, Longstreet, Califf¹ by answering the question does the technostress which is a stressor affect the strain ie, internet addiction. In the past most of the studies have endeavored to find the effect of internet addiction on stress in context of developed countries as well as the reciprocal effect of technostress on internet addiction. Another innovation in this study is the use of social media support as a moderator between internet addiction and quality of life. The study is also a contribution to the literature on stressor-strain-outcome (SSO) framework. The structure of this study is woven around SSO framework which is a suitable framework to explore the problem understudy.

Theoretical Background and Hypothesis Development

Stressor-Strain-Outcome (SSO) Framework

In the current research, the stressor-strain-outcome (SSO) framework has been applied to discover the impact of technostress on IA and its influence on QoL. The SSO theory explains how environmental stimuli affect users’ psychological and behavioral activities through generated strains.³⁶ It is noteworthy that the SSO framework suggests that a stressor has an indirect effect on the outcome and that the strain typically works as a mediator between stressors and outcome variables. A stressor is defined as an external or environmental stimulation that a person experiences and that affects their internal states,³⁷ and which is typically regarded as irritating, troublesome, and disruptive.³⁸ Strain can be considered as both an internal process and an effect of external stimuli, causing the outcome of the stressor and strain interaction together. Hence, stressors cause strain, which puts the individual under stimulation at risk for adverse consequences. Therefore, both outcome and strain can be considered behavioral or psychological reactions to stressors.

The excessive use of internet-based technologies works as a significant stressor to persuade technostress¹³ which further increases the use of the internet causing IA.³⁹ Previous studies have found that technostress has a significant impact on strain causing fatigue⁴⁰ that affects the quality of sleep and cognitive dysfunction.⁴¹ The technostress and IA affect mental and physical health⁴⁰ which has several behavioral consequences and significantly influences the QoL of people.³⁸ Accordingly, based on the SSO model, the current research considers the technostress as a stressor, IA as strain, and QoL as an outcome.

Technostress and Internet Addiction

Earlier studies have found that stress propels people toward addictive behavior.⁴² The technostress caused by the excessive use of internet-dependent devices can be reduced by decreasing or stopping the use of stress-causing technologies.³⁹ However, this is not the case as research found that people continue to use the technology even when it is causing stress.⁴³ Thus, the use of internet-based technology resembles the symptoms of compulsive behavior like alcohol and drug addiction.⁴⁴ On the same line, Lee, Chang, Lin, Cheng,⁴² identified that technostress leads to compulsive behavior which is found by Tice, Bratslavsky, Baumeister,⁴⁵ as positively linked to addiction. In the field of medical sciences, researchers have identified that drug usage increases with an increase in stress⁴⁶ which may resemble the symptoms produced in the case of technostress. As Kuss, Griffiths,⁴⁷ noted that social network addicts exhibit mood disorder, conflict, withdrawal symptoms, and relapse which are similar to classic addiction symptoms. Therefore, technostress encourages people to use internet-based technologies as people consider them more useful, user-friendly, and full of fun leading to IA. Therefore, we propose that

H1: Technostress has a significant positive effect on internet addiction.

Internet Addiction and Quality of Life

IA has a significant impact on different aspects of people’s health that encompasses the state of mental, physical, and social well-being⁴⁸ which defines the QoL of people.⁴⁹ The studies found that internet addicts display the symptoms of poor QoL⁵⁰ because the excessive use of the internet leads to a higher level of anxiety and depression. People spending more time on the internet have reported lower perceived QoL induced by insomnia, physical problems, lack of concentration, and reduced social interaction with family and friends.⁵¹ Few studies reported that the use of the internet for long hours affects social and psychological well-being and QoL.⁵² Therefore, we hypothesize that.

H2: Internet addiction is related to the QoL of people.

The Mediating Role of Internet Addiction

Technostress, arising from excessive use of internet-dependent devices, does not necessarily decrease when individuals reduce or stop using stress-inducing technologies.⁵³ Hsiao, Shu, Huang⁵⁴ found that technostress can lead to compulsive behavior, a factor positively associated with addiction according to Brooks, Longstreet, Califf.¹ Furthermore, individuals addicted to social networks exhibit mood disorders, conflict, withdrawal symptoms, and relapse, which resemble classic addiction symptoms.⁵⁵ Consequently, technostress can drive people to intensify their use of internet-based technologies, as they perceive them as more useful, user-friendly, and enjoyable, ultimately leading to IA. Using sense from the SSO framework, it can be suggested that technostress is a stressor that influences people working with stress-causing technologies and thus induces strain in them which may take the form of internet addiction leading to excessive use of the internet to cope with the stress caused by stress-causing technologies [H1], which in turn develops symptoms of physical and mental distress (anxiety, stress, insomnia, lack of concentration, loneliness, less socialization, solitude, etc.) in those people making them suffer a lower quality of life [H2]. Therefore, we hypothesize that:

H3: Internet addition mediates the relationship between technostress and quality of life.

The Moderating Role of Social Support

Social support serves as a crucial protective force in the context of IA, offering both tangible and psychological assistance to mitigate the adverse effects of IA on quality of life.⁵⁶ Individuals who have a strong network of social support are better equipped to manage the detrimental consequences of IA, which often include depression and negative emotions.³⁵ This highlights the pivotal role of social support in enhancing mental and physical well-being.³³ Moreover, prior research underscores that social support significantly influences the impact of IA on QoL.⁵⁷ Essentially, a strong social support system serves as a protective shield against the adverse effects of IA, promoting enhanced overall wellbeing and mental health. It has been shown that individuals grappling with internet addiction can experience an improvement in their QoL when they have access to increased social support.⁵⁸ Conversely, some empirical studies have indicated that IA can lead to a decline in social support, subsequently contributing to a deterioration in QoL.⁵⁹ Furthermore, the presence of robust social support can also act as a preventive factor, reducing the likelihood of individuals developing internet addiction and, in turn, contributing to a higher QoL.⁷ These findings underscore the intricate interplay between social support, internet addiction, and QoL, highlighting the vital role of a supportive social network in mitigating the negative impacts of excessive internet use on individuals’ overall wellbeing. Therefore, we hypothesize that.

H4: Social support moderates the relation between internet addiction and quality of life such that the relation is stronger among higher social support than among lower social support.

In our proposed model, we introduce a comprehensive understanding of the complex interplay between technostress, quality of life, internet addiction, and the moderating influence of social support. We suggest an integrated model, wherein internet addiction plays a central role in mediating the link between technostress and quality of life. This implies that as individuals experience technostress due to their excessive use of technology, it triggers internet addiction, which, in turn, adversely affects their overall quality of life. Notably, we incorporate the role of social support as a moderator in this relationship. Social support is expected to exert its influence by either mitigating or exacerbating the impact of internet addiction on quality of life. Furthermore, building on Edwards, Lambert⁶⁰ approach, we argue that the moderating variable, in this case, social support, also moderates the intensity of the mediating effect of internet addiction within the technostress-quality of life connection. This multifaceted model aims to provide a more nuanced and comprehensive understanding of the intricate dynamics involved in the technostress, internet addiction, and quality of life relationship, considering the important role of social support in this context. As a result, we suggest the following hypothesis.

H5. Social support moderates the indirect effect of technostress on quality of life via internet addiction, such that the indirect effect is stronger for people with high levels of social support than for those with low levels of social support.

Methods

Data Collection and Procedure

In order to achieve the purpose of the study we collected data from students enrolled in the universities of the Gujarat State of India. The target population which is extensively engaged in use of internet-based devices and faces problems caused by technostress and internet addiction comprises of students mostly enrolled in various graduation, post-graduation and vocational programs offered in the universities. For the purpose of the study, we considered the universities as clusters of the sample population. The questionnaires were administered to the university students in three waves. Applying the cluster sampling, we first randomly selected the educational institution from a frame of all the universities located in the Gujarat State of India and then we contacted officials in universities to obtain permission to conduct the study. Out of all the universities (Cluster) located in the state only 10 universities were selected randomly and from each university we contacted at least 100 respondents to participate in the study and took their consent and commitment to participate in the study. This study adheres to the ethical principles outlined in the Declaration of Helsinki and the written informed consent has been duly obtained from all participants involved in the research. All respondents were briefed about the purpose of the study, and we exchanged contact details for contact in second and third wave. In this study, a time-lagged research design was employed to facilitate data collection at distinct intervals. This method, increasingly popular in contemporary research, enables the execution of multiple surveys tailored to the specific objectives of the research, as evidenced in recent studies.⁶¹ One significant advantage of this approach is its ability to gather data from a variety of sources, thereby significantly diminishing the risk of common source bias, a concern highlighted in several studies.⁶² Additionally, the time-lagged design inherently reduces the potential for common method bias by spacing data collection over several time points.⁶³ Another benefit of this method is that it allows participants ample time to reflect upon and modify their behaviors before finalizing their responses. This feature adds depth and reliability to the data collected. Regarding the validity of the theoretical constructs, a rigorous content validation process was undertaken. The validation involved two critical steps: initially, the purification of items through corrected item-total correlation, followed by the assessment of unidimensionality. The latter was conducted using exploratory factor analysis with varimax rotation and principal components extraction. Furthermore, the intraclass correlation coefficient was calculated for all items, with values exceeding the threshold of 0.5, which denotes satisfactory inter-rater agreement. The reliability of the scales was confirmed with Cronbach’s alpha values exceeding the acceptable benchmark of 0.7.⁶⁴ To mitigate the impact of social desirability bias and further reduce common method variance,⁶² data collection was strategically structured into three waves. At first wave questions related to technostress and social support were filled by respondents out of 1000 respondents we successfully obtained 795 completely filled questionnaires. In the second wave questionnaire related to internet addiction was administered using emails. From 795 respondents we obtained 703 complete filled responses. In the third wave questions related to quality of life were asked. Out of 703 respondents, we collected 677 completely filled questionnaires. The detailed demographic information of the respondents is reported in Table 1.

Table 1 Demographics Information of Sample

Before initiating data collection, a comprehensive statistical power analysis was undertaken to determine the necessary minimum sample size for precise estimation of the proposed model.^65–69 Considering an expected effect size of 0.150, a target statistical power of 0.80, the inclusion of three predictors, and a confidence level set at 95%, it was determined that a minimum sample size of 132 was indispensable for model estimation. Remarkably, our collected sample encompassed 677 participants, surpassing the minimum requirement and thereby providing ample capacity for the estimation of the proposed model. This comprehensive analysis underscores the robust statistical power of our study in detecting the intended effects. Additionally, it is noteworthy that the efficacy of confirmatory composite analysis in detecting a wide array of model misspecifications with sample sizes of approximately 100 or greater.^70–73

Measures

In this study, measurements of key variables were derived from existing literature and administered bilingually in English and Hindi. To maintain linguistic fidelity and conceptual integrity, Brislin’s⁷⁴ back-translation methodology was employed. The initial translation of survey questionnaires from English to Hindi was conducted by an expert in organizational behavior. A bilingual management professional then carefully revised both the English and Hindi versions, addressing any minor discrepancies. Additionally, professors specializing in organizational behavior from the surveyed service establishments validated the appropriateness of these translations for the specific context of our study, as supported by the findings of Khan, Mehmood, Khan,⁷⁵ and Mehmood, Jabeen & others.⁷⁶ Technostress was measured using a scale developed by Lee, Chang, Lin, Cheng,⁴² consisting of six items on a seven-point Likert scale ranging from “1” (strongly disagree) to “7” (strongly agree). An example item was: “I feel my personal life is being invaded by social media and internet-based information and communication technologies”. The reliability of this scale was confirmed with α value of 0.913. Quality of life was assessed using a four-item scale adapted from Kim, Schmöcker, Nakamura & others,⁷⁷ with a seven-point range from “1” (very dissatisfied) to “7” (fully satisfied). A representative item was: “Overall, I am very satisfied with my current life”. The scale’s reliability was indicated by a α value of 0.892. Social support was evaluated using a twelve-item scale, adapted from Zimet, Dahlem, Zimet & others⁷⁸ and subsequently used by Osman, Lamis, Freedenthal & others.⁷⁹ Responses were recorded on a seven-point Likert scale, from “1” (strongly disagree) to “7” (strongly agree), with an example item being: “I can talk about my problems with my friends”. The scale demonstrated high reliability with a α value of 0.931. Internet addiction was assessed through an eight-item scale, derived from Elphinston, Noller,⁸⁰ and later employed by Wang, Wang, Wu & others.⁸¹ The scale used a seven-point Likert scale from “1” (never true) to “7” (always true). An illustrative item was: “I lose track of how much I am using social networking sites (eg, Facebook, Twitter, Instagram, TikTok, etc.)”, and the scale showed good internal consistency with a α value of 0.907.

Control Variables

In this study, gender, age, education, income, and marital status were incorporated as control variables, due to their potential influence on the studied variables within the model, as supported by existing literature.^82,83 Gender was dichotomously coded, with male assigned a value of 1 and female a value of 0. Educational attainment was categorized into three levels: technical/vocational degree (coded as 1), undergraduate degree (coded as 2), and postgraduate degree (coded as 3). The age of the respondents was quantified in years. Marital status was coded as unmarried (0) and married (1). Regarding income, it was classified into four categories based on the amount in Indian rupees: less than or equal to 10,000 (coded as 1), between 10,001 and 15,000 (coded as 2), between 15,001 and 20,000 (coded as 3), and 20,001 and above (coded as 4).

Analytical Techniques

In the analysis of the collected data, a comprehensive array of statistical techniques was meticulously applied. This suite included factor analysis, correlation analysis, as well as the computation of means and standard deviations. Additionally, analyses of direct effects, mediating effects, moderation, and moderated mediation were conducted. Confirmatory factor analysis (CFA), using Mplus 7.0 software,⁸⁴ was employed to assess the distinctiveness of the variables under study. This CFA played a crucial role in establishing discriminant validity among the variables. The reliability of the measures was ascertained through the calculation of Cronbach’s alpha values. Correlation analysis was utilized to explore the relationships between various variables under study. For a comprehensive and robust analysis of moderated mediation, the bootstrapping method was employed. This method involved the use of Model 14 of the PROCESS Macro, drawing upon 10,000 bootstrapped samples and 95% bias-corrected confidence intervals. This approach provided a thorough and nuanced understanding of the intricate relationships among the variables.

Results

Non-Response Bias

We addressed the potential influence of non-response bias within our collected dataset through a two-step approach. Initially, during the data collection phase, efforts were made to reassure respondents regarding the confidentiality of their personal information and responses on the provided scale, thus mitigating the impact of non-response.^85–87 Subsequently, we employed statistical measures, in accordance with the recommendations of Lambert, Harrington.⁸⁸ To this end, we conducted a Mann–Whitney U-test, comparing data from respondents in the first and third waves concerning the study variables. The first 90 data entries were considered early participants, while the last 90 were designated as late participants. The statistical analysis revealed no statistically significant differences between the data of initial and late participants. In summary, it can be inferred that non-response bias did not pose a significant concern within the scope of this research.

Common Method Variance

To address the potential issue of common method bias (CMB), a multifaceted approach was employed, encompassing both procedural and statistical strategies. Initially, procedural measures were enacted to foster unbiased responses. Complete respondent anonymity was ensured, as explicitly outlined in the survey cover letter, thus reducing the likelihood of socially desirable responses.^62,89–92 In this research, rigorous measures were taken to enhance clarity and minimize ambiguity in survey items. Scale items were defined with precision, and questions were crafted to be both direct and specific, aligning with the methodologies suggested by prior scholars.^62,93,94 To mitigate response biases, variables were not labelled according to their reported constructs, and items were not grouped by variables, thus reducing the likelihood of respondents making educated guesses or deliberately associating variables, as advised by Parkhe.⁹⁵ Additionally, participants were informed that the survey questions had no definitive right or wrong answers, highlighting the confidentiality of their responses. Alongside these methodological safeguards, a statistical strategy was implemented to assess the presence of common method bias (CMB). CFA was conducted to evaluate the potential existence of CMB. The analysis of a single-factor model revealed a suboptimal fit: χ² = 3369.851, df = 464, χ²/df = 7.262, CFI = 0.599, TLI = 0.576, and RMSEA = 0.109. These indices collectively suggested the absence of significant CMB in the study. Further examination for CMB was carried out using Harman’s single-factor test, a widely recognized technique.^96–98 This test was applied to all ten constructs in the research model. The principal axis factoring method revealed that the first factor accounted for only 25.83% of the variance, indicating a minimal impact of CMB, as corroborated by prior studies.^99–102 Furthermore, the sample adequacy measure, with a value of 0.917, surpassed the acceptable threshold of 0.80, confirming the adequacy of the sample size for this study.

Confirmatory Factor Analysis

We employed CFA through Mplus 7.0 software⁸⁴ to evaluate the distinctiveness of the following constructs: quality of life (QoL), technostress (TS), internet addiction (IA), and social support (SS). The baseline model, encompassing these four variables, underwent meticulous scrutiny. To assess the discriminant validity of the model, we compared the baseline model with three alternative models. As delineated in Table 2, the four-factor model (baseline model) demonstrated a satisfactory fit, characterized by the following statistics: χ² = 759.567, df = 458, χ²/df = 1.658, CFI = 0.961, TLI = 0.957, and RMSEA = 0.057. Importantly, the fit indices of the baseline model surpassed those of the alternative models like three-factor model in which IA, QoL, and TS were combined (χ² = 1652.851, df = 461, χ²/df = 3.585, CFI = 0.923, TLI = 0.917, and RMSEA = 0.073), two-factor model in which SS and QoL were combined (χ² = 2485.126, df = 463, χ²/df = 5.367, CFI = 0.856, TLI = 0.834, and RMSEA = 0.092), and single-factor model in which all factor were combined (χ² = 3369.851, df = 464, χ²/df = 7.262, CFI = 0.599, TLI = 0.576, and RMSEA = 0.109), providing robust evidence for the distinctiveness of the four variables and thereby confirming discriminant validity. As a consequence, our model is deemed appropriate for subsequent hypothesis testing. In Table 3, the majority of factor loadings exceeded the recommended threshold of 0.60 for all items and there is no poor loading and/or evidence of cross-loading, corroborating the convergent validity of the constructs. Additionally, all composite reliabilities (CRs) values exceeded the recommended threshold of 0.80, as per the criteria proposed by prior scholars.^103–105 Furthermore, the average variance extracted (AVE) values for all the constructs under investigation exceeded the recommended benchmark of 0.50, in accordance with prior research.¹⁰³ Moreover, as portrayed in the diagonal of Table 4, the reliability of the measurement instruments was assessed using Cronbach’s alpha (α). Reliability levels above 0.80 are indicative of good reliability, while those falling within the range of 0.70 are considered acceptable, and values below 0.60 are categorized as poor, following the criteria outlined by prior research.¹⁰³ Notably, Cronbach’s alpha coefficient estimates for all four variables exceeded 0.70, signifying an acceptable level of reliability.

Table 2 Confirmatory Factor Analyses Results

Table 3 Variables Reliabilities and Validities

Table 4 Correlation Matrix and Summary Statistics

Descriptive Statistics

The findings presented in Table 4 indicate several significant correlations among the variables under investigation. Firstly, technostress exhibited a positive correlation with internet addiction (r = 0.24, p < 0.01). Secondly, there was a significant negative correlation between internet addiction and quality of life (r = −0.27, p < 0.01). Lastly, social support displayed a noteworthy positive correlation with quality of life (r = 0.46, p < 0.01). These results serve as preliminary evidence supporting our hypotheses.

Hypothesis Testing

We conducted a series of tests on the hypothesized model using Mplus,⁸⁴ with the results presented in Tables 5 and 6. These findings lend support to our hypotheses. In Table 5, the impact of control variables on the variables under study is examined. However, it is noteworthy that all control variables in our research exhibited insignificant effects. As shown in Table 6, specifically, H1, which proposed a positive association between TS and IA, is substantiated [unstandardized β = 0.295, SE = 0.024, 95% CI (0.2316, 0.3303)]. Moreover, H2, suggesting an association between IA and QoL, is validated [unstandardized β = −0.383, SE = 0.047, 95% CI (−.5346, −0.2860)]. To evaluate Hypothesis 3, Hypothesis 4, and Hypothesis 5, we employed the bootstrapping method approach Preacher, Hayes¹⁰⁶ with Model 14 based on 10,000 bootstrapped samples and 95% bias-corrected confidence intervals.¹⁰⁷ Hypothesis 3 posited that IA mediates the link between TS and QoL. As illustrated in Table 7, the PROCESS analysis revealed a significant indirect effect of TS on QoL through IA [indirect effect = −0.1132, 95% CI (−.2011, −0.0463)], thus supporting Hypothesis 3.

Table 5 Coefficients of Control Variables

Table 6 Hypotheses Results

Table 7 Bootstrap Analysis of Mediating Effect

Hypothesis 4, which proposed that SS moderates the association between IA and QoL, received support, as the interaction term (SS * IA) was significant (β = 0.311, p < 0.01) (see Table 5). Furthermore, the plotted interaction (SS * IA) (see Figure 2) demonstrated a stronger link with a high level of SS, thereby corroborating H4. Hypothesis 5 proposed that SS would moderate the indirect impact of TS on QoL via IA. Specifically, it was hypothesized that this indirect effect would be more pronounced in individuals exhibiting higher levels of SS. The empirical findings, detailed in Table 5, corroborate this hypothesis. For individuals with high levels of SS (+1 standard deviation), the indirect effect of TS on QoL through IA was statistically significant [estimate = −0.0907, Standard Error (SE) = 0.048, 95% Confidence Interval (CI) with lower limit (LL) = −0.1138 and upper limit (UL) = −0.0148]. Similarly, at low levels of SS (−1 standard deviation), this mediating effect remained significant [estimate = −0.1762, SE = 0.036, 95% CI (LL = −0.2263, UL = −0.1064)]. The moderated-mediation index also revealed a significant result [Index: −0.0507, Bootstrap SE = 0.048, 95% CI (−.1138, −0.0148)], as it did not include zero. This outcome substantiates the moderating role of SS in the relationship between TS and QoL through IA, thereby supporting Hypothesis 5.

Figure 2 Interactive effects of Internet addiction and Social Support on Quality of Life.

Discussion

The current research aimed to identify the impact of technostress on IA and its influence on QoL. The study also analyzes the moderating role of social support on the relationship between IA and QoL. We found a significant positive correlation between technostress and IA which shows that technostress increases the arousal of people to use internet-connected devices that further increases the stress level of people as found by the previous studies.^57,108 The results also demonstrate that technostress does not directly influence the QoL. The possible reason for this result may be that some people may feel anxiety in online activities, whereas other users may find relief from the use of internet-linked devices.^109,110 However, this type of relief may persist only for a short duration leading to higher online activities to gain relief for a higher duration that triggers compulsive behavior which results in loss of control of internet usage.¹¹¹ This may explain why, in this research, technostress predicts a lower QoL only after considering the influence of IA.

The outcomes indicate that IA negatively affects QoL, showing that different aspects of QoL decline as the use of the internet increases. Earlier studies have shown that IA significantly influences the psychological, physical, and social QoL of people.⁵⁷ The impact of IA on physical QoL is manifested through lower sleep time, poor diet, physical inactivity, and drinking behavior.¹¹² IA lowers self-actualization, deteriorates daily activities, and increases anxiety and depression, thus lowering the mental QoL of people.¹¹³ Internet addict people were found to be associated with lower satisfaction with family, home environment, and friends, and lesser participation in social interaction and recreational activities that adversely affect their social QoL.⁷ Hence, higher IA lowers all aspects of QoL that require special attention to prevent people to become internet addict. The strategies should be developed for enhancing the capability of people to control their time and effort spent on the internet as internet addicts have lower time and energy management power.¹¹⁴

Additionally, we have considered social support as a moderator and the results indicate that the impact of IA on QoL can be minimized by proper social support. While higher social support decreases the impact of IA on QoL, lower social support increases the impact of IA on QoL. This means that people can lower their addictive behavior towards internet usage by increasing their social interaction with surrounding people such as family and friends which is consistent with the findings of earlier studies.⁷ Furthermore, behavioral problems, social withdrawal, and impaired QoL are often associated with internet addiction leading to social exclusion which restricts social interaction and support.^115,116 Thus, social support can be a valuable resource for improving all aspects of QoL¹¹⁷ as lesser social support makes a person unappreciated that works as a distressing factor for QoL.¹¹⁸ Social support can be used as a protective measure to lower the addictive behavior of people toward the internet.

Theoretical and Practical Implications

The present research contributes to literature on quality of life and wellbeing. This study examines the impact of technostress, IA, and social support on QoL using the SSO framework, which is, as per our knowledge, not done by any previous research. The findings show the significance of these attributes being evaluated using one theory and framework, which is a significant addition to the existing literature on technostress as well. We found that technostress significantly enhances the IA which leads to severe damage to the QoL of people. However, technostress may influence the QoL of different people differently which can be discovered by future research using the framework applied in the study. Previous studies have shown that technostress itself has the power to damage the QoL as stress leads to depression and anxiety leading to poor mental QoL.¹

The findings of the present research have several implications for practitioners. The clinicians may consider information on the use of internet-connected devices while dealing with internet-addicted patients and giving attention to technostress in their treatment and diagnostic assessment. Technostress should be considered as a comorbidity of internet addiction. Considering the overall impact of technostress on development of internet addiction can help practitioners suggest better remedy for internet addicts. Understanding the linkage between the stressor and strain may help clinicians better diagnose the root cause of internet addiction and comorbidities. Better diagnosis is most likely to filter out the base problem which should be treated first to get better results of any remedy. As this study has applied SSO model to understand the effect of technostress on internet addiction so it must be considered whether a person is internet addict and he is using technology to relieve stress generated as a result of internet addiction or it is otherwise that a person is feeling stress because of social media and internet based information and communication devices and to relieve that stress he is excessively using those device ie, he/she has got addiction as use of internet gives him/her feel of relieve from technostress.

Furthermore, medical practitioners should offer additional support to persons suffering from technostress taking into consideration the compulsivity dimension and its potential impact on QoL. Specific medications should be given to susceptible groups of patients, especially those suffering from compulsive features so that they approach and handle the internet-linked devices without hampering their QoL. Proper use of health-related information may be vital for the self-care of people, prevention efforts should not only recognize the challenges but also the opportunities. This kind of information should be applied to make people aware of their QoL and the required medical care for its improvement.¹¹⁹

As proven by our research the use of social support as a moderator reveals the positive role of friends, family and close people social support in recovery from internet addiction and thus having quality of life back. Internet addicts must be provided social support as it is necessary in getting rid of any other addiction. People around an internet addict should understand a person’s stressful lifestyle. Students who used to work on call centers or similar workplaces in addition to their university activities had more technostress and thus sever internet addiction. Friends and family of such students need to understand the load of their excessive and compulsive use of information and communication devices. They must be provided with a healthy social environment to relieve their technostress and internet addiction. Such support from people may not only reduce their internet addiction but also positively influence their quality of life and wellbeing.

Furthermore, universities and educational policymakers play a pivotal role in mitigating IA and fostering a healthy balance between online and offline life for students. To this end, implementing educational seminars and workshops is crucial. These programs should focus on raising awareness about the indicators and ramifications of IA. They ought to educate students about prudent internet use, emphasizing the significance of striking a balance between digital and real-world activities, and imparting self-regulation techniques. Additionally, integrating digital literacy into the curriculum is vital. This includes courses that cover managing digital footprints, comprehending the impact of technology on mental health, and nurturing critical thinking in the digital realm. Such educational initiatives can significantly enhance students’ QoL and equip them with skills to navigate the digital world responsibly. In addition to educational efforts, providing robust support systems is essential. Universities should ensure the availability of counseling services for students grappling with internet addiction. These services should offer individualized strategies for behavior modification and coping mechanisms, as well as group therapy sessions, creating a supportive space for shared experiences and mutual learning. Beyond therapeutic interventions, universities should actively foster an engaging campus environment that reduces dependence on digital devices. This can be achieved by organizing a variety of offline activities, clubs, and events catering to a wide range of interests, including sports, arts, and community service. Such initiatives not only offer healthy alternatives to excessive internet use but also enhance social support among students. Lastly, collaboration with tech companies to develop software that monitors and restricts internet use on campus networks can further aid in maintaining a healthy digital balance. By adopting these comprehensive measures, universities can significantly contribute to preventing internet addiction, thereby supporting students’ overall QoL and academic success.

Limitations and Future Research

The outcomes of the study should be interpreted cautiously as it has some limitations. The findings on the relationship between technostress, internet addiction, and QoL may not be applicable to the entire population of internet users. Furthermore, the sampling technique may have left out some specific people, making the possibility of sampling people overusing the internet-connected technologies, hence, more suited to a study aimed at understanding the mechanisms underlying technostress. The future works may find it interesting to identify the direct relationship between technostress and QoL. The framework presented in this study can also be used to identify the negative and positive consequences of technostress on IA and QoL as a study has demonstrated that technostress generates both positive and negative psychological responses to job satisfaction and turnover intention of employees.¹²⁰ Despite its limitations, our findings offer intriguing avenues for future research. One such opportunity is the exploration of how various social factors can impact individuals’ quality of life in the context of technostress. Subsequent studies could deliberately incorporate the collection of cultural and country-level variables, examining their potential moderating effects. Researchers should be attuned to these cultural nuances when delving into disparities among nations or regions across the world. Even in a moderating role, Hofstede’s cultural dimensions can offer valuable insights into the underlying reasons for observed differences.

Conclusion

The current research highlights that IA can be used to understand the mechanisms that link the technostress to decreased QoL. The outcomes of the study add information to the available literature on technostress by exploring its relations with IA and QoL that were overlooked by the previous studies. Moreover, the results of the study point out an important aspect of technostress, which is compulsive behavior. The findings demonstrate that technostress and IA should be considered serious health issues and included in health awareness and intervention programs. The negative association between IA and QoL shows the importance of the development of the self-regulating ability of people to prevent the excessive use of the internet. Social support can be a possible resource to lower the IA as it significantly influences the relationship between internet addiction and QoL. The strategies developed for improving the QoL of internet addict people must emphasize elevating social support from family, friends, and society.