Understanding compulsive smartphone use: An empirical test of a flow-based model
Introduction
Along with the exponential growth of smartphone usage, growing evidence suggests that many users begin to show the symptoms of smartphone addiction and use the devices compulsively (Lapointe, Boudreau-Pinsonneault, & Vaghefi, 2013). The compulsive use of smartphones has been found to result in negative consequences that seriously affect lives and work (Tarafdar, Gupta, & Turel, 2015; Turel et al., 2008 For instance, during socializing with friends, compulsively checking smartphones may negatively influence users’ social lives (Park & Lee, 2011). A recent report from Deloitte1 indicates that compulsive smartphone use becomes prevalent in China with adverse outcomes such as playing smartphones at the cost of study/work performance. To avoid these unpleasant effects, it becomes critical for researchers to understand the formation of compulsive smartphone use.
According to prior research, compulsive information technology (IT) use denotes the extent to which people use ITs repetitively and fail to control the use (Caplan, 2010). It highlights the behavioral aspect of IT addiction (Thadani & Cheung, 2011; Young, 1998), which has been defined as a maladaptive dependency on IT usage (Turel, Serenko, & Giles, 2011). Kim and Davis (2009) posited that there is no doubt that IT usage is problematic if the behavior becomes compulsive. As an emerging research issue, compulsive IT usage has not been extensively studied in the information systems (IS) literature (Cheung, Lee, & Lee, 2013), and certainly the same for smartphones. According to neuroscience research, the neural mechanism of compulsive substance use is intrinsic reward-related, which enables people to receive positive feelings from midbrain dopamine neurons (Blum et al., 2000; Hyman, Malenka, & Nestler, 2006). Seeking intrinsic rewards may generate cycles of dysregulation and finally lead to compulsive behaviors (e.g., Koob & Le Moal, 2001). In other words, when a behavior is intrinsically rewarding, it may increase the likelihood of behaving compulsively. Following this perspective, this study plans to investigate whether intrinsic-reward factors can influence compulsive IT use. More specifically, we examine the influence of flow on compulsive smartphone use.
Prior research posits that flow is a key intrinsic reward and refers to a positive psychological experience when people use ITs (Weibel, Wissmath, Habegger, Steiner, & Groner, 2008). Flow has also been shown to be an important experience for smartphone users. For instance, playing smartphone games can help users attain the positive flow state, which leads to continued usage (Joo, 2016). Smartphone users are found to experience flow during viewing mobile advertising (Kim & Han, 2014). Meanwhile, the “danger” of flow is demonstrated by recent anecdotal evidence of IT addiction. For instance, Salehan and Negahban (2013) stated that users who try to maintain the positive experience of flow while using social networking applications on smartphones tend to develop undesirable addiction behaviors. Sun, Zhao, Jia, and Zheng (2015) showed that flow may facilitate game addiction in mobile platforms. Based on these concerns, it is possible that the “desirable factor” of flow may exert an important effect on compulsive smartphone use. This is also in line with prior research (Park & Lee, 2011), which indicates that positive experiential factors may result in compulsive smartphone use.
In sum, this study considers flow as an important ingredient in the development of compulsive smartphone use. Our first objective is thus to test the effect of flow on compulsive smartphone use (the behavioral aspect of smartphone addiction), rather than on smartphone addiction. Prior research has shown that IT addiction consists a number of symptoms (e.g., conflict, withdrawal, and behavioral salience) (Turel et al., 2011), which may have different determinants (Soror, Hammer, Steelman, Davis, & Limayem, 2015). In this study, we assess the importance of flow in compulsive smartphone use. Linking flow with such a behavioral outcome may partially help to explain why recent research finds inconsistent results regarding whether flow can influence IT addiction (e.g., Thatcher, Wretschko, & Fridjhon, 2008; Wan & Chiou, 2006). The second research objective is to identify the determinants of flow. This can enrich our understanding of why smartphone uses are more likely to experience flow during using the devices. It further helps to provide a more complete nomological network of how flow is developed, and how it leads to negative behavioral outcomes like compulsive smartphone use. More specifically, we identify the determinants of flow based on the desirability–feasibility perspective (i.e., instant gratification, mood regulation, and convenience). This perspective reflects users’ general beliefs toward ITs, which can explain why users develop personal willingness to embrace IT devices (Jia, Wang, Ge, Shi, & Yao, 2012). We further extend this perspective by including personal traits that are stemmed from reinforcement sensitivity theory. Considering personal traits allows us to discern whether users may exhibit predispositions in developing flow experience and finally the compulsive use of smartphones. It can help us develop effective prevention guidelines for different compulsive users.
This study is expected to make several contributions. First, given its practical relevance and research significance, this study contributes to the emerging literature of compulsive IT use and pushes forward the development in this area with solid theoretical background. Second, previous research mostly addresses the positive outcomes of flow (e.g., Hsu & Lu, 2004). In contrast, its negative consequences are largely uninvestigated. The very few IS studies that investigate the role of flow in IT addiction contexts only provide limited and incongruent evidence. Extending this line of studies, we investigate the determinants and effect of flow on compulsive smartphone use. Finally, we extend the desirability–feasibility perspective by incorporating reinforcement sensitivity theory. This can add to the IS literature on the dark sides of ITs, and explicate how different users develop their flow state and further compulsive behaviors. The rest of this paper is structured as follows. We review the theoretical background in the next section. Then, we develop our research model, followed by presenting the research method, data analysis, and results. Finally, we summarize this work by discussing the findings, implications, limitations, and opportunities for future research.
Section snippets
Theoretical background
To guide the current study, we review the relevant literature, including flow theory, desirability and feasibility perceptions, and reinforcement sensitivity theory.
Research model and hypotheses development
Building upon the theoretical background, this study proposes a research model to understand compulsive smartphone use. First, we refer to flow theory to hypothesize the impact of flow on compulsive smartphone use. This is in line with the perspective of intrinsic reward within the literature of flow, neuroscience, and compulsive behaviors (Blum et al., 2000, Hyman et al., 2006; Koob & Le Moal, 2001; Weibel et al., 2008). Second, we adopt the desirability–feasibility perspective and
Data collection
We conducted a cross-sectional online survey to empirically validate our research model. We developed an online questionnaire and adopted a convenient sampling approach to collect data. Invitation messages and flyers with the URL of the questionnaire were distributed at two large universities in China. That is, invitation messages were posted in online discussion forums of these universities, in which most of the students had registered. We also sent flyers in university campuses to enlarge the
Data analysis and results
We adopted partial least squares (PLS) to test our research model. PLS can estimate the measurement and structural models together. In addition, it allows us to evaluate both reflective and formative variables (Gefen, Rigdon, & Straub, 2011). We thus expected PLS was suitable for this study.
First, since our data were perceptual and collected via a self-report method, we employed Harman's single-factor test to detect possible common method bias (Podsakoff, MacKenzie, Lee, & Podsakoff, 2003). Our
Discussion and conclusion
Motivated by the need to enhance the understanding of compulsive smartphone use, this study investigates the influence of flow and its key determinants. More specifically, we develop our research model by examining the relationships among three desirability and feasibility perceptions (i.e., instant gratification, mood regulation, and convenience), two personal traits (i.e., BAS and BIS), and the optimal psychological state of flow.
This study confirms that the positive flow experience can
Acknowledgments
The work described in this paper was supported by grants from the Research Grant Council of the Hong Kong Special Administrative Region, China (Project Nos. CityU 145912 and CityU 192513), the National Natural Science Foundation of China (Project Nos. 71671174 and 71472172), and the Fundamental Research Funds for the Central Universities of China (Project No. WK2040160013).
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