Elsevier

Computers in Human Behavior

Volume 29, Issue 6, November 2013, Pages 2632-2639
Computers in Human Behavior

Social networking on smartphones: When mobile phones become addictive

https://doi.org/10.1016/j.chb.2013.07.003Get rights and content

Highlights

  • We investigate the role of mobile social networking applications on mobile addiction.

  • The use of SNS mobile applications is a significant predictor of mobile addiction.

  • The use of SNS mobile applications is affected by both SNS network size and SNS intensity of the user.

Abstract

As the penetration of mobile phones in societies increases, there is a large growth in the use of mobile phones especially among the youth. This trend is followed by the fast growth in use of online social networking services (SNS). Extensive use of technology can lead to addiction. This study finds that the use of SNS mobile applications is a significant predictor of mobile addiction. The result also shows that the use of SNS mobile applications is affected by both SNS network size and SNS intensity of the user. This study has implications for academia as well as governmental and non-for-profit organizations regarding the effect of mobile phones on individual’s and public health.

Introduction

Information and communication technologies (ICT) have significantly changed the way we live and have become an inseparable part of our lives. Many people, especially the youth, use these technologies on a daily basis and for various purposes. People use computers to study, to search for information on the internet, to play games, and to communicate with others.

These days, most people have mobile phones and use them on-the-go. Recent statistics from December 2011 show that there are 331.6 million mobile phone subscribers in the United States which indicates a penetration rate of 104% for mobile phones across the country (CTIA, 2011). In 2011, the number of mobile phones worldwide exceeds 5.6 billion, showing an 11% increase compared to its previous year and an average penetration rate of 79.86% worldwide (Gartner, 2011).

Recent advances in hardware and software with the introduction of smartphones has augmented the use of ICT in daily life. In 2011, smartphone vendors experienced a sharp increase in their sales, reaching 472 million units which showed 58% increase in comparison to the previous year (Gartner, 2012). The sales for smartphone also accounted for 31% of all mobile phone sales in 2011 (Gartner, 2012).

Nowadays, people use their mobile phones for a wide variety of tasks ranging from calling and texting to playing games, navigation, and social networking. Online social networking services (SNS) have gained rapid popularity in recent years. Social networking services are now more than mere websites. They provide their users with several ways to connect others including web, email, and mobile applications. Many SNS vendors have introduced mobile applications that can be installed on smartphones for fast and easy access to SNS. Facebook, the most popular SNS, currently has 955 million active users (Mashable, 2012) out of whom more than half connect through mobile devices (SocialBakers, 2012).

Although mobile phones are very popular and bring lots of benefits to their users, various social issues have arisen during their adoption, including: use of mobile phones in banned and dangerous circumstances (Bianchi & Phillips, 2005), complaints about the use of mobile phones in public places, compulsive use, and even addiction (Toda et al., 2008). ICT has the potential to create addiction in individuals. This is supported by the theory of optimal flow (Csikszentmihalyi, 1990) which posits that for some individuals, the experience with ICT is so enjoyable that they will try to maintain the state even at high costs. Excessive mobile phone use can even be seen as a type of technostress (Brod, 1984). By preventing people from working or studying, addiction can cause harm to both individuals and the society (Park, 2005).

As Internet is becoming and indispensable part of people’s lives, it is also becoming source of severe problems for both individuals and organizations. Addiction to internet, as a technology, is an instance of addiction caused by technology (Griffiths, 1999, Leung, 2004). Internet addiction not only is harming people’s personal lives, it is also making organizations more concerned about their employees’ productivity, network congestion, and corporate data privacy (Chou, Sinha, & Zhao, 2010). This combined with the sharp increase in the penetration rate of mobile devices, which allows anytime-anywhere internet connectivity, along with widespread use of social networking applications on mobile phones, would exacerbate the social and personal problems associated with mobile phones and internet technology.

Although there has been extensive research on technology addiction, mobile phone addiction has received little attention from scholars (Belles, Beranuy, Carbonell, & Guardiola, 2009). This study contributes to filling this gap by investigating the relationship between use of mobile phone for social networking and mobile phone addiction. Drawing on the theory of optimal flow, this study focuses on how the use of social networking mobile applications can affect mobile phone addiction.

This study has three objectives: (1) to propose a research model explaining how the use of social networking mobile applications can be associated with mobile phone addiction, (2) to empirically test the proposed model using data collected from smartphone users, and (3) to give the academia and practitioners deep insight about this effect and its implications. The following parts of this paper are organized as follows: first, we present the theoretical background of the study. Then, we propose our research model and hypotheses. Finally, we discuss the methodology, results and implications of our study.

Section snippets

Technology addiction

Addiction can be explained by oddly high dependence on a particular thing (Park, 2005). Addiction is characterized by repetitive acts with a total negative sum of consequences (Waal & Mørland, 1999). The need for short-term satisfaction in an addict overshadows the long term implications of his/her actions (Waal & Mørland, 1999). While being over-attentive to instant satisfaction, addicts usually have incorrect or distorted image of the future although they have a degree of recognition about

Research model and hypotheses

This study investigates the effect of social networking services and especially using social networking mobile applications on mobile addiction. Drawing on the theory of optimal flow, we propose that excessive use of social networking applications on mobile phone can lead to mobile addiction. Figure 1 shows our research model. Network size is the number of people a person is connected to through his/her social network. In this study we focus on SNS network size rather than overall social

Measurement

We adapted our measurement scales from prior studies and made minor modifications to fit the context of our study. Our survey instrument is shown in Appendix A. For social network intensity, a variation of Ellison et al. (2007) instrument was used. To measure network size, we simply asked respondents to choose the number of their SNS friends from 7 choices (Less than 10, 10–49, 50–99, 100–199, 200–299, 300–399, More than 400). The scale for mobile phone addiction was adapted from Negahban (2012)

Discussion

Building on the body of knowledge regarding mobile addiction, this study investigates the effect of use of mobile social networking applications on mobile addiction. In this section we discuss the key findings of our study followed by the limitations and implications for research and practice.

Conclusion

In this study, we investigate how SNS intensity, network size, and mobile social networking applications can be associated with mobile addiction. The result of our study shows that mobile social networking applications are significant predictor of mobile addiction. The use of these applications can be influenced by the network size and SNS intensity of the user. SNS intensity is also affected by network size which may be a sign of extensive use of SNS for connecting to weak ties. Overall, the

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    These authors contributed equally to the work.

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