The availability and accessibility of smartphones and the internet have enhanced the lives of everyone allowing individuals to communicate or develop social interaction, leveraging usage among 6.3 billion users worldwide (Ling & Lai, 2016; Hsieh & Tseng, 2017; Morabi et al., 2023; Statista, 2022). One of the biggest advantages these smartphones offer to their users is the downloading facilities of different apps providing valuable services like email, messaging, social media platforms, web browsing, and gaming (Kim et al., 2014; Benson et al., 2015; Mameli et al., 2018; Pellegrini et al., 2012; Rowe, 2020) which is very useful for working professionals. Research conducted in the field of information technology and its application in different contexts like the workplace concurs with the relation between the use of information technology in organizational settings, and performers' working capabilities. Some authors have asserted the positive side of the use of IT to work effectively and efficiently (Koay, 2018). However, few other researchers believe that apart from its functional benefits, concerns have been raised on the smartphone usage gradually building a psychological dependency leading to adverse consequences of addiction (Barnes et al., 2019; Moqbel et al., 2023; Zhong et al., 2022). Although past research has found consequences of smartphone addiction on employees' behavioral issues. Yet, the research on the internet application in the workplace neglected the intervening mechanism through which these negative effects arise (Tarafdar et al., 2015). Additionally, the dark side of these technological enhancements brought a number of problematic behavioral (Mantello et al., 2023; Rohwer et al., 2022; Sun et al., 2022), and inappropriate use of smartphone devices or addiction for smartphone usage in everyday works’ life is one of them (Moqbel et al., 2023). Many researchers argued that excessive usage and habitual checking of smartphones to check official mails, messages, calls or missed calls, resulted in behavioral issues and even lead to mobile phone addiction for smartphone users (Banerjee & Gupta, 2024, Koay, 2018; Fossen et al., 2023). The excessive use of smartphones can crop instant gratification; inducting volitional control and tenacious activity (Chen, & Wang, 2024; Harper & Lee, 2023; Park & Kim, 2023; Smith, & Johnson, 2023; Zhang & Wu, 2024). Few other authors propose that this compulsive usage of smartphones for work related activities turns out generating technostress among the users (Lee et al., 2014; Duke & Montage, 2017; Tarafdar et al., 2020; Hessari & Nategh, 2022). Many authors, concluded that empathic surveillance in the workplace also catalyzes the levels of stress, aversion (Bondanini et al., 2020; Brougham and Haar, 2018; Mantello and Ho, 2023)

Past research on smartphones addiction has primarily conducted unidimensional where author(s) focus on examining the effect of smartphone addiction on wellbeing or work-related performances (Takahashi, & Kitamura, 2009;Mantello et al., 2023; Rohwer et al., 2022; Sun et al., 2022; Tarafdar et al., 2020). Subsequently, through this proposed research authors have made a substantial move further by intending to understand the multidimensional influence of excessive use of smartphones by employees on their behavioral issues as a whole. Authors like to answer the research questions like:

• R1: Does excessive use of smartphones build addiction for the same?
• R2: Does smartphone addiction create technostress?
• R3: Does technostress directly or indirectly influence employees' behavioral issues?

Taking the support of in-depth study of the literature available to conclude the relationship between smartphone addiction, technostress and behavioral issues, authors proposed an investigation to build an understanding on the same. Thus, this research up to some extent partially tries to address the ambiguous state of mind by systematically examining the results of smartphone addiction whether only technostress is its outcome, or influence the behavioral issues also. 

The main objective of the study is to find out the path relationship between smartphone addiction, technostress and behavioral issues. Secondly, authors like to determine whether technostress directly leads to behavioral issues or it mediates the relationship between smartphone addiction and employees’ behavioral issues. Thirdly, we also wanted to know that if technostress influences employee behavioral issues, then in which dimension namely; inter, intra or professional is heavily influenced by this. In the coming section, hypotheses are proposed through an extensive review of literature in the fields of technology, smart phone, technostress and behavioral issues . The following section will discuss the literature explaining the relationship of applied constructs with the hypothesis formulated in the study.

Smartphone Addiction.

The world has become one platform; possibly the way technology has introduced itself on various fronts. Eventually, mass media was one of them. Mobile Phones have connected people from different walks of life to one platform. Conversely, there are growing apprehensions for smartphones, which sometimes take away, rather than support social interactions. A report published by Statista (2020) shows that the number of mobile phone users in the world is approx. 5 to 6 billion, comprising 79.9 percent of the world’s population. Technology has its advantages and disadvantages, and one of them is that excessive use can inculcate addiction. Addiction is observed as an individual’s approach to managing relationship difficulties and insecure attachment styles. This behavior is likely to result in addiction where an individual fails to regulate the emotions effectively (Flores, 2004).  However, mobile applications, smartphones are presented with social media tools and games with internet facilities increase the usage rate fast (Hsieh & Tseng, 2017; Zhong et al., 2022). Few antagonistic have strong support for smartphone usage, according to them smartphones complement many of the latter’s functions of everyday life, are portable, and offer more enthralled “user value” to consumers (Park & Han, 2013). Massive use of Smartphones has become the potential driver to develop an addictive behavior among users, which is inhibiting their lives. At the individual level, severe problems in everyday life may be the habit-forming nature of smartphone usage (Oulasvirta et al., 2012). Many researchers have diagnosed how addiction symptomology even applies to excessive smartphone use like; distortion of time spent on the phone, behavioral conflicts, and negative effects on our social and work lives (Lin et al., 2015; Rohwer et al., 2022; Mantello et al., 2023). Many studies have concluded on the effects of excessive use of the smartphone on their mental and physical health (Jenaro et al., 2007; Chen & Wang, 2024; Smith & Johnson, 2024). The research proposed by Sim et al., (2012) that, increasing concern among people regarding suffering from pathological technology use with symptoms that resemble that of an addiction. Many works specify the vital characteristics of everyday life can be affected by the use of the smartphone (Harari et al., 2017; Langener et al., 2024; Priya & Subramaniyam, 2022). In the past, several studies proposed a theory stating that the usage of smartphones can interfere with an individual’s life to an extent where there is a gradual loss of a relationship (Miller-Ott et al., 2012; McDaniel & Coyne, 2016) also resulting in addiction, reduced capacity to enjoy leisure (Jankovic et al., 2016; Lepp et al., 2015; Mok et al., 2014). It is very common in day’s that excessive usage and dependency on technology have proven to be unhealthy for human beings. One of the emerging causes of being physically or mentally unfit gives rise to many physical as well as psychological diseases.

Technostress.

There are several apprehensions related to technology adoption and usage. As a result, there is a firm belief that work-life boundaries and roles have become vague causing behavioral issues. Results in the past have proven that technostress has a significant influence on work-life conflict. ( Hessari & Nategh, 2022; Marchiori et al.,2019; Tarfdar et al., 2020) in their research concluded that, excessive use of smartphone or web-based technology results in behavioral conflicts. A study conducted by (Oh et, al. 2016), concluded in their research that, technostress may result from the use of new technologies even after work and during holidays (e.g., using a smartphone, checking emails, or continuing work through a messenger after working hours), influencing job satisfaction and work-life conflict. This research intends to explore how smartphone addiction leads to Stress (technostress) as a result of the massive use of technology. (An exhaustive study explored that excessive use of a smartphone can lead to technostress and is measured in various dimensions (Ragu-Nathan et al; 2008). The popularity of the smartphone has emerged as one of the basic phenomena for one and all across the globe. Technostress is ‘‘a modern disease of adaptation caused by an inability to cope with the new computer technologies in a healthy manner (Brod, 1984). Technostress is the phenomenon of end users experiencing stress due to information and communication overload (Rouge et al, 2008). The explosive growth of end-user computing and networking technologies enhances the severity of technostress (Brillhart, 2004).). In 1984 clinical psychologist Craig Brod derived a disease called technostress as an outcome of over usage of technology, inability to deal with Information Communication Technology (ICT) in a healthy manner further resulting in disparaging forces for the employee as well as for a company also (Ayyagari, Grover, & Purvis, 2011; Banerjee & Gupta, 2024; Fossen et al., 2023; Koay, 2018). Technostress can be defined as the outcome of excessive usage of information technology and creates stress among users. Unlike the coin, everything has two sides, so as information technology. Arnetz and Wiholm (1997) also defined, “Technostress as a state of excitement experienced in certain people, heaving dependent on computers in their work.” Contradictory to this Figueiredo (1994) defined technostress as a kind of computer literacy and acceptance of digital technologies. In prior research, technostress is defined as one of the aspects of stress about technological usage.

In later studies, Technostress is also defined as a phenomenon of end users facing stress due to information and communication overload (Ragu-Nathan, et al., 2008).  In the past, several studies related to behavioral issues towards technology have primarily focused on determining the personality and psychological variables and their outcomes (e.g., Mueller et al., 2011; Roberts & Pirog, 2013; Takao et al., 2009), testifying that certain psychological traits might influence the ability to endure stress or make a person vulnerable to stress (Ebstrup et al., 2011). An exhaustive literature study done by (Srivastava et at., 2015) highlighted that technostress is very common among employers because of a heavy workload, many dependencies on technology, higher expectations from employers for being more productive, a constant need to be accustomed to emerging ICT applications, functionalities, and other workflows. Many studies have signified the effect of technostress varies across individuals.  Brod (1984) anticipated, “When human beings fail to cope with more advanced and upgraded technologies, the probability of having the new modern disease is possible to be encountered by them in the form of technostress. Adding to this, he also averred that technostress is a difficult situation for adaptation, caused by the use of recent technology by either people or organizations. Another definition of technostress is the state of (Ayyagari, R. et. Al; 2011) identified characteristics of technology which are directly proportionate to stress like heavy usability, intrusiveness, and dynamism.

H1: Smartphone addiction positively leads to Technostress.

2.3 Behavioral Issues.

As extracted from the literature one can profoundly say that heavy usage of a smartphone has its repercussions in life. Smartphone addiction has consequences on an individual’s personal and professional life, on his/her health and sometimes seriously it influences mental or psychological status. Human interaction with information technology may result in many consequences probably with more negative outcomes such as attitude phobia and anxiety about smartphone usage, behavioral disorders, etc. (T.A. Wright et, al; 1998).   However, this study tries to investigate in what terms smartphone addiction can hamper the physical and psychological status of human beings. The core of the study refers to the heavy usage of smartphones influencing an individual’s behavioral issues as well as influencing different dimensions of conflict behavior. Anticipating this, many pieces of research have stated that heavy usage of the smartphone also leads to various psychological and physiological issues. Excessive use of the smartphone is regarded as problematic in everyday life (Oulasvirta et al., 2012). Smartphone use encompasses certain key dimensions of behavioral addiction: Salience, Mood Modification, Tolerance, Withdrawal, and Conflict (Zach W.Y. Lee, 2015).  Few studies have identified the link between high internet addiction and low satisfaction in life (Nalwa & Anand, 2003). Problematic use of the cell phone is taken as a disorder, and addictive behavior (Billieux, 2012). Eventually, usage of a smartphone can be associated with antisocial behavior, as well as with uncontrolled use and addiction indicators. Some researchers proposed how we can apply conventional addiction syndrome in the context of excessive smartphone usage indicated by loss of control, obsession with the use of a smartphone, and adverse influence on social and work lives (Kwon et al., 2013; Lange et al., 2014, Lin et al., 2015). Though many studies have done significant work on identifying the influence of smartphone usage on social and work life, no significant study is there deducting the comprehensive influence of smartphone addiction on the dimension of conflict behavior(Interpersonal/intrapersonal/academic). Many researchers also view behavioral issues as an outcome of smartphone addiction as a driver to relieve nervousness and indulge in fun for others (Hirschman, 1992; O’Guinn & Faber, 1989; Roberts & Pirog, 2013; Takao et al., 2009). 

H2: Smartphone addiction positively influences Behavioral Issues.

2.4 Technostress and Behavioral Issues.

In today’s working environment, technology has become an integral part of everyone. It is detected that people are so addicted to technological devices that all their direct or indirect speech is influenced by this kind of device and its usage. Many times people sitting around their social or personal group use a smartphone to talk, even if they are sitting next to them. This habit of using a smartphone has a severe effect on their physical and mental health. The habit of smartphone usage sometimes gets converted into an individual’s behavioral issues limiting him/her to outperform, work-life balance, productive work, and so on. Although studies done in the past to determine the effect of the overuse of smartphones have gained significant importance, still many aspects are untouched. This research will also attempt to know whether smartphone-generated technostress may also result in an individual’s behavioral issues or not.

 (Hunter, G. and Perrault, W; 2007) Extensive research done by them reported that the use of smartphones has incremental effects on different aspects of an individual’s performance. Therefore, it is important to investigate the effect of technostress on one’s behavior. Some of the research extracts have well explained the way to manage the technostress, but moreover, many of them have explained the physiological disorders like mental illness, followed by psychological disorders like indecisiveness, depression, anxiety, frustration or anger, lack of control and confidence, and restlessness. In a working environment, using a mobile phone while working can result in multitasking and cause technostress that can lead to poor job performance.

Similarly, technostress can also affect users’ satisfaction. (Lukoff and Gackenbach, 2004) mentioned that some individuals use the Internet in dysfunctional ways that lead to social isolation and deteriorating work performance. If technostress accounts for more stress on a more general level, we expect this form of general stress to influence organizational outcomes in the form of decreased job satisfaction.

H3: Technostress positively mediates the relationship between smartphone addiction and Behavioral issues.

Sample and Procedure

The study employed a descriptive research design wherein; a quantitative research methodology was used to test the proposed research model.  A structured questionnaire consisting of 31 questions was used to collect data through an online survey. Snowball sampling is purposely used to have respondents having a strong habit of using smartphones in excess to enjoy various applications/activities of smartphones, the method was used to collect data from respondents, including students, homemakers, and working professionals residing in India.  A hyperlink to the online questionnaire was sent to 735 respondents via email, and the respondents were also requested to forward the questionnaire to the working professionals known to them as their friends, colleagues, and relatives.  The data collection process was carried out from December 2017 to February 2018. A total of 325 valid responses was received indicating a response rate of 44.2per cent, which was reasonable for studies of this scale. 49.5 of the respondents were females, and 50.5 percent were males. The age of the respondents ranged between 17 years to 66 years with mean as 29.8 years and a standard deviation as 10.5 years. Out of the total sample, 36per cent were students, 31.7per cent was working professionals, and 32.3per cent were housewives. The sample is an indicator group to test the research model as smartphones are very popular among students and homemakers. Moreover, working professionals excessively use smartphones for an online shopping while at work. Furthermore, including respondents from all over the country allow for a generalization of findings to represent the overall Indian context.

Measurement

The scales for all the constructs of the research model were adapted from previous studies carried out in the context of smartphone addiction and conflict behavior.  Specifically, 12 items were used to measure smartphone addiction (SA) which were adapted from (Eengin Karadağet, al.); 6 items were adapted from (Yu-Kang Lee et al.,) used to measure technostress (TS). Behavioral issues (CB) was a multidimensional construct including three dimensions namely intrapersonal problems (IntraP), interpersonal problems (InterP) and academic/professional problems (APP). 9 items were adapted from (Schiein, Guerne, Stover, 1971) to measure interpersonal problems (IntraP), intra personal problem (InterP) and academic/professional problems (APP).  The adapted measurement items for this construct, wherein three items were used to measure IntraP, four items for InterP and three items were used to measure the APP. Each item was measured using a 5 point Likert scale ranging from 1 (strongly disagree) to 5 (strongly agree). Apart from these items, three demographic questions (age, gender, and profession) are also included in the questionnaire. Age was measured in years, whereas gender and profession were measured using a nominal scale.

The questionnaire was developed and administered in the English language and was checked for content validity by experts from a University. Before administering the questionnaire to actual respondents, pilot testing is done with 30 randomly chosen subjects in November 2017. Based on the pilot test results, fewer items are deleted, and a few items are modified in the questionnaire.

Analysis of Data and Findings.

Descriptive Analysis

The descriptive statistics for each construct in the proposed research model is sufficient evidence to support univariate normality of all the items as all values of skewness are below their cutoff point 3, and all kurtosis values are less than 8 (West et al., 1995; Kline, 2011). Moreover, except for a few items (SA3, SA4, SA6, SA7, SA9, SA11, SA12), the critical ratios for both skewness and kurtosis for all items were found to be within the recommended limits of -2 and +2 (Kline, 2011) which indicate support for multivariate normality in the data. However, these items are dropped from further analysis because of their low factor loadings.

Structural Equation Modeling (SEM)

The structural equation modeling (SEM) technique is employed in the study to test the relationships between the constructs within the proposed model. The two-stage SEM approach (Anderson and Gerbing, 1988; Schumacker and Lomax, 2010) was used, beginning with the measurement model for testing the reliability and validity of the instrument and then estimating the structural model.

MEASUREMENT MODEL

Model Fitness

The measurement model was examined to test the model fitness and to establish the reliability and validity of the model constructs. For testing the model fitness, firstly the confirmatory factor analysis (CFA) was conducted with the first order model on smartphone addiction and technostress. The main fit indices, including CMIN/DF, GFI, AGFI, CFI, NFI, RMR, and RMSEA were tested to evaluate the model fitness. As can be noticed from Table 1, all the indices could not reach their recommended threshold values in the initial first order CFA model. Therefore, certain reassessments are done to increase model fitness (Bagozzi and Yi, 1988; Anderson and Gerbing, 1988). To ensure the indicator reliability, the items (SA3, SA4, SA12, TS3, InterP1) having standardized regression weights (factor loadings) less than 0.4 were dropped (Henseler et al., 2009). Apart from this, result extracted from modification indices, it was observed that SA7, and TS1 had higher unacceptable values. Hence, these items were also removed from the model. Besides, by inspecting standardized residual covariance, SA9 and SA11 were found to have higher values than their recommended threshold level of ±2. 58 (Anderson, Tatham, & Black, 1995). Therefore, these two items are also removed.

After these modifications, the first order CFA model is tested again, and the model fitness was improved significantly, as expected. Even though the value of chi-square (Χ2 = 391.3, DF = 122, P = 0.000) was still significant, the remaining fit indices of the modified first order measurement model were found to be within their recommended values (table 1).

Once the first order CFA model was found to be fit, the second order CFA model was tested by hypothesizing behavioral issues (CB) as a higher order construct based on the three lower order constructs: IntraP, InterP, and APP. All the factor loadings were found to be greater than 0.7 which indicated that CB loaded well on its three constructs. As seen in table 3, the fit indices (except chi-square) of the second order model were found to be within their threshold values, indicating the adequate goodness of fit to the data.

Table 1: Measurement Model

Items from SA (i.e. SA3, SA4, SA7 and SA12), two items from TS (i.e.TS1, and TS3) and one item from Interpersonal Problems (i.e. InterP1) are dropped because of low loading factor.

Reliability and Validity

As shown in table 2, all the constructs exhibited adequate levels of reliability with Cronbach’s alpha coefficients (Nunnally, 1978) and composite reliabilities (Hair et al., 2010) greater than the cutoff point of 0.7. Also, the AVE values of all the constructs were greater than their threshold value of 0.5 (Hair et al., 2010) and all AVE values were less than the corresponding CR values indicating sufficient convergent validity (table 1). Also, as reported in table 3, all the correlation estimates between the constructs were found to be less than the maximum level of 0.85 (Kline, 2005) and all the constructs had greater than their inter-correlation estimates with other corresponding constructs. These results provided sufficient evidence to support the discriminant validity of the model constructs.

Table 2: Reliability and Convergent Validity

Table 3: Discriminant Validity

Note: Factor Correlation Matrix with squared roots of AVE on the diagonal

Structural Model

After achieving satisfactory results of the measurement model, the constructs were used to examine the structural model for testing the hypothesized relationships. The fit indices of the structural model were found to be as follows: Χ2 / df = 3.227, GFI = 0.989, AGFI = 0.839, CFI = 0.959, NFI = 0.942, RMR = 0.068, and RMSEA = 0.073. The fit indices indicated that the structural model has the adequate goodness of fit to the data.

The results of the path coefficients (see fig. 2) Indicated that all the hypotheses are supported. Particularly, technostress was found to be significantly affected by smartphone addiction (β = 0.524, C.R = 9.808, p<0.001) and behavioral issues was found to be significantly affected by technostress (β = 0.470, C.R = 9.040, p<0.001). Behavioral issues was also significantly influenced by smartphone addiction (β = 0.415, C.R = 7.583, p<0.01), thereby indicating that technostress partially mediates the relationship between smartphone addiction and conflict behavior. Therefore, all the hypotheses H1, H2, and H3 is supported. Moreover, the R2 values indicated that smartphone addiction explained 27.5per cent variation in technostress and both smartphone addiction and technostress explained 59.7per cent variation in conflict behavior. Also, the R2 values for IntraP, InterP, and APP were found to be 87per cent, 80per cent, and 73per cent, which reflected a good contribution of all three constructs in conflict behavior.

The model is also examined for analyzing the direct effects, indirect effects and total effects between the constructs. Table 4 indicates that the total effect of SA on CB is 0.661 out of which the direct effect is 0.415 and the indirect effect is 0.246. The indirect effect of mediating is attributed of TS in the relationship between SA and CB. Since the indirect effect is lesser than the direct effect of SA on CB, we can interpret that TS weakly mediates the relationship between SA and CB. Also, the direct effect of SA on TS is more than the direct effect of SA on CB.

Further, the indirect effect of SA on IntraP (0.616) is noticed to be more than that on InterP (0.591) and APP (0.564). Similarly, the indirect effect of TS on IntraP (0.438) is more than that on InterP (0.420) and APP (0.401). The results of study indicates smartphone addiction has significant influenceed on  intrapersoanl problems of an individual then inter/academic problems.

Table 4: Direct, Indirect and Total Effects

The research purpose was to examine the relationships between smartphone addiction, technostress, and conflict behavior. The data were extracted from 325 respondents from the age group of 17 to 66 years, out of which 49.5per cent & 50.5 percent females and males respectively, representing Delhi-NCR Region. The total percentage of respondents comprise of 36 students, 31.7 working professionals, and 32.3 homemakers.  The prominent observations made in the research were: firstly, the result of research concludes that smartphone addiction is a strong predictor of technostress (Salomon, 1986; Jain & Lyons: 2008). Excessive use of smartphone leads to technostress among the users and further leads to conflict behavior.

Further, it is also observed that the smartphone has a direct influence on user behavioral issues as well (Kwon et, al. 2013; Lanaj et, at. 2014; Lin et al, 2015.). The results of the study also reveal the technostress partially mediate the relationship between smartphone addiction and individual’s conflict behavior. Secondly, on the agreeable note, it was observed that there is a significant influence on behavioral issues on an individual’s work-life (Zach W, Y; Lee et al, 2015). Further, the result also revealed that the effect of behavioral issues is more on intra-personal problems than interpersonal and academic professional performances. Taken altogether, the findings of the research indicate that smartphone addiction effects the technostress among individual’s and further leads to conflict behavior, eventually smartphone addiction has a direct effect on individual’s conflict behavior, whereas there is the lesser mediating effect of technostress on conflict behavior. One interesting finding of the study reveals that the behavioral issues has a strong influence on individual’s intra/inter and academic performance, 87per cent,80per cent,73per cent respectively, and intra-personal issues are one of the major areas where there is the high influence of behavioral issues may lead to multiple health hazardous issues.

Research Contribution and Implications.

This research significantly contributes to the various theoretical aspects of the study on smartphone usage and its repercussions on users mental and health-related issues. In the past several types of research were done either to instigate the effect of smartphone addiction on creating technostress or to determine whether smartphone addiction leads to behavioral issues or not. This research outstands with the past in a way it's not only tried to determine whether there is any significant relationship exists between smartphone addiction, technostress and behavioral issues forming a triangular relationship, but also tried to determine the direct and indirect effect of Smartphone on conflict behavior. Secondly, we also tried to investigate if this triangular relationship exists, then whether technostress mediates the effect between smartphone addiction and behavioral issues or not, if yes then at what intensity it mediates between smartphone addiction and conflict behavior. Though few studies conducted in the past found the effect of smartphone usage on individual performances, (Hunter, G. & Perrault, W; 2007) reportedly said that there is an incremental effect of smartphone addiction on behavioral issues tampering with different aspect of the performances, but fails to mention the variety of performances. On the other hand, this research not only showed a significant relationship between smartphone addiction and conflict behavior, but also mentioned the outcome effect of behavioral issues on performance indicators like inter/intra-personal, and academic professional performances. Out of these three variables, intrapersonal issues are strongly influenced by conflict behavior.

Practical implications:

Devise a promotional strategy where they can educate the users about the right usage of cell phones on right proportions.

Their strategic team can work on the different aspects of cell phone features enabling users to limit the usage of phone.

Social implications:         

Organization can also consider few suggestions like:

HR Managers can design various OD Interventions for employees where some indoor or outdoor recreational activities can help employees to manage their intra-personal issues.

Philanthropist/ Counselors can conduct some health checkup camps or workshops for Youngsters and oldies to balance their intra-personal problems.

Society can organize some sports event where they can orient young camps towards traditional indoor and outdoor games which India use to have in olden days, engaging people physically and keeping them away from cell phone usage.

Acknowledgement: The authors wish to acknowledge Symbiosis Centre for Management Studies, NOIDA.

Conflict of Interest: No such conflict exists among the authors.

Funding: Not Required

Ethical approval: Not applicable

1. Arnetz, B. B., & Wiholm, C. (1997) Technological stress: Psychophysiological symptoms in modern offices. Journal of psychosomatic research, 43 (1), 35-42.
2. Ayyagari, R., Grover, V., & Purvis, R. (2011) Technostress: technological antecedents and implications. MIS Quarterly, 35 (4), 831-858.
3. Bianchi, A., & Phillips, J. G. (2005) Psychological predictors of problem mobile phone useCyberPsych ology & Behavior, 8 (1), 39-51.
4. Billieux, J. (2012) Problematic use of the mobile phone: a literature review and a pathway model. Current Psychiatry Reviews, 8 (4), 299-307.
5. Brod, C. (1984) Technostress: The human cost of the computer revolution. Addison Wesley Publishing Company.
6. Brown, R. I. F. (1997) A theoretical model of the behavioral addictions–applied to offending Addicted to crime, 13-65.
7. Choi, Y. Y., & SEO, Y. S. (2015) The relationship between insecure adult attachment and smartphone addiction: the mediation effect of impulsiveness moderated by social
8. Korean J Psychol Couns Psychother, 27(3), 749e72.
9. Chóliz, M. (2012) Mobile-phone addiction in adolescence: the test of mobile phone dependence (TMD). Progress in Health Sciences, 2 (1), 33-45.
10. Demirci, K., Akgönül, M., & Akpinar, A. (2015) Relationship of smartphone use severity with sleep quality, depression, and anxiety in university students. Journal of behavioral addictions, 4 (2), 85-92.
11. Figueiredo, J. R. (1994) An evaluation of People's Attitudes toward Technostress and Techniques on How to Overcome It”.
12. Flores, P. J. (2004) Addiction as an attachment disorder. Jason Aronson.
13. Griffiths, M. (1999) Internet addiction: fact or fiction?. The Psychologist.
14. Hirschman, E. C. (1992) The consciousness of addiction: Toward a general theory of conflict Journal of Consumer Research, 19 (2), 155-179.
15. Hunter, G. K., & Perreault Jr, W. D. (2007) Making sales technology effective. Journal of Marketing, 71 (1), 16-34.
16. Janković, B., Nikolić, M., Vukonjanski, J., & Terek, E. (2016) The influence of Facebook and smartphone usage on the leisure activities and college adjustment of students in Serbia. Computers in Human Behavior, 55, 354-363.
17. Jenaro, C., Flores, N., Gómez-Vela, M., González-Gil, F., & Caballo, C. (2007) Problematic internet and cell phone use: Psychological, behavioral, and health correlates. Addiction research & theory, 15 (3), 309-320.
18. Kim, H. (2013) Exercise rehabilitation for smartphone addiction. Journal of exercise
19. rehabilitation, 9 (6), 500.
20. Kuo, L. H., Chen, L. M., Yang, H. J., Yang, H. H., Yu, J. C., & Hu, W. C. (2009, May)
21. Assessing mobile technostress. In WSEAS International Conference. Proceedings.
22. Mathematics and Computers in Science and Engineering (No. 8). World Scientific and
23. Engineering Academy and Society.
24. Kwon, M., Kim, D. J., Chou, H., & Yang, S. (2013) The smartphone addiction scale:
25. development and validation of a short version for adolescents. Plus one, 8 (12), e83558.
26. Kwon, M., Lee, J. Y., Won, W. Y., Park, J. W., Min, J. A., Hahn, C.,... & Kim, D. J. (2013)Development and validation of a smartphone addiction scale (SAS). Plus one, 8 (2), e56936.
27. Lin, Y. H., Chang, L. R., Lee, Y. H., Tseng, H. W., Kuo, T. B., & Chen, S. H. (2014)
28. Development and validation of the Smartphone Addiction Inventory (SPAI). Plus one, 9
29. (6), e98312.
30. Lee, Y. S. (2006) Biological model and pharmacotherapy in internet addiction. Journal of the Korean medical association, 49 (3), 209-214.
31. Miller-Ott, A. E., Kelly, L., & Duran, R. L. (2012) The effects of cell phone usage rules on satisfaction in romantic relationships. Communication Quarterly, 60 (1), 17-34.
32. Misra, S., & Stokols, D. (2012) A typology of people-environment relationships in the
33. Digital Age. Technology in Society, 34 (4), 311-325.
34. Mok, J. Y., Choi, S. W., Kim, D. J., Choi, J. S., Lee, J., Ahn, H., ... & Song, W. Y. (2014) Latent class analysis on the internet and smartphone addiction in college students.Neuropsychiatric disease and treatment, 10, 817.
35. Nalwa, K., & Anand, A. P. (2003) Internet addiction in students: A cause ofconcern. Cyberpsychology & Behavior, 6 (6), 653-656.
36. Oulasvirta, A., Rattenbury, T., Ma, L., & Raita, E. (2012) Habits make smartphone use more pervasive. Personal and Ubiquitous Computing, 16 (1), 105-114.
37. Park, J., & Han, S. H. (2013) Defining user value: A case study of a smartphone.International Journal of Industrial Ergonomics, 43 (4), 274-282.
38. Salomon, I. (1986) Telecommunications and travel relationships: A review. Transportation Research Part A: General, 20 (3), 223–238.
39. Schimmenti, A., Passanisi, A., Gervasi, A. M., Manzella, S., & Famà, F. I. (2014) Insecure attachment attitudes in the onset of problematic Internet use among late adolescents. Child Psychiatry & Human Development, 45 (5), 588-595.
40. Sim, T., Gentile, D. A., Bricolo, F., Serpelloni, G., & Gulamoydeen, F. (2012) A conceptual review of research on the pathological use of computers, video games, and the Internet. International Journal of Mental Health and Addiction, 10 (5), 748-769.
41. Thomée, S., Härenstam, A., & Hagberg, M. (2011) Mobile phone use and stress, sleep disturbances, and symptoms of depression among young adults-a prospective cohort study. BMC public health, 11 (1), 66.
42. Wright, T. A., & Cropanzano, R. (1998) Emotional exhaustion as a predictor of job performance and voluntary turnover. Journal of applied psychology, 83 (3), 486.Zheng, P., & Ni, L. (2010). Smartphone and next-generation mobile computing. Elsevier.