Full length articleMobile ubiquity: Understanding the relationship between cognitive absorption, smartphone addiction and social network services
Introduction
Smartphones are ubiquitous in modern society; evidence suggests that there were 3.9 billion smartphones globally in 2016, which is estimated to rise to 6.8 billion by 2022 (Ericsson, 2017). Smartphone technology, however, is a prime example of what Mick and Fournier (1998) refer to as a ‘paradox of technology’, which can be both emancipating and enslaving simultaneously. Smartphones afford us the liberty to communicate, socialize and search for information in ways almost unthinkable two decades ago; smartphone technology can also result in user dependency and deleterious user outcomes and behaviors.
Traditionally the Internet has been the chief subject of focus for studies of technology addiction and problematic behavioral outcomes (De-Sola Gutiérrez, Rodríguez de Fonseca, & Rubio, 2016). In recent years, however, cell phone technology – and particularly the advent of the smartphone – has begun to supplant the Internet as a potential source of addictive behavior (Lane & Manner, 2011; Lin et al., 2015). Further, smartphone addiction may arguably be more important to study than problematic Internet use as smartphones offer a mobile computing platform (with Web browsers and GPS navigational services) with greater portability than other computing devices such as laptops and tablets and addiction hence may be more acute (Demirci, Orhan, Demirdas, Akpinar, & Sert, 2014; Jeong, Kim, Yum, & Hwang, 2016; Kwon, Kim, Cho, & Yang, 2013).
A debate that is currently emerging in the literature is the distinction between addiction to a device versus addiction to applications and contents, and the relationship between the two (De-Sola Gutiérrez et al. 2016), reminiscent of earlier deliberations concerning the Internet (Griffiths, 1999). While a number of studies have examined smartphone addiction, very few have considered addiction to the device versus addiction to certain services, particularly social network services (SNS), which provide online platforms for building relationships based on shared personal dimensions. While a small corpus of scholarship has examined addiction to various types of content (e.g. news, entertainment, social networking) (Bian & Leung, 2015; Rosen, Whaling, Carrier, Cheever, & Rokkum, 2013; van Deursen, Bolle, Hegner, & Kommers, 2015), with the exception of Jeong et al. (2016), no prior study has compared different types of content in any detail, or, further, differentiated between addiction to the device versus addiction to particular applications. This subtle difference is important as it helps us to better comprehend smartphone addiction, particularly as certain cell phone activities may be more significantly associated with addiction than others (Roberts, Yaya, & Manolis, 2014).
In a departure from previous empirical studies, the present study examines the two different arguments beginning to appear in the literature, namely addiction to the device versus addiction to SNS, in a single study. Further, we examine users' levels of cognitive absorption – their state of involvement and engagement with software and technology – with smartphone and SNS addiction, to help understand the role of user perceptions in computer-mediated environments. Finally, we consider the potential influence of demographic factors on smartphone and SNS addiction.
The research design was based on a single cross-sectional sample and a self-report survey. Scales were adopted from previous studies, but were adapted and extended for the context of this study. The survey was implemented online and distributed to business students at a university in the Mid-Atlantic region of the United States. Hypothesis testing was conducted via t-tests, analysis of variance (ANOVA), regression, and a Sobel test.
The study is structured in the following way. Following this introduction, we consider the subject of technology addiction and research examining problematic smartphone usage. Next we examine the subject of user perceptions through the concept of cognitive absorption. We then turn to the development of a series of hypotheses. The remainder of the study empirically examines the hypothesis based on data obtained via a survey, including a discussion, conclusions and implications of the study's findings.
The aims of this study are threefold: to examine the differences between smartphone addiction and SNS addiction; to understand the influence of user perceptions on addiction (measured through cognitive absorption to examine users' state of involvement and engagement with software and technology); and to examine differences for demographic factors for smartphone and SNS addiction and user perceptions. This section explores the background literature on these topics, focusing on technology addiction, problematic smartphone usage and cognitive absorption.
Merriam-Webster's Medical Dictionary (1995: 273) defines addiction as “… an acquired mode of behavior that has become nearly or completely involuntary,” while the Gale Encyclopedia of Medicine (1999) considers addiction as “… a dependence, on a behavior or substance that a person is powerless to stop.” Traditionally, addiction was regarded as relating only to substances (such as alcohol and drugs), but latterly was broadened to include problematic behaviors (including excessive sexual intercourse and pathological gambling). Further, some have argued that any uncontrollable or overused behavior or activity should be regarded as an addiction (Peele, 1985).
The American Psychiatric Association's Diagnostic and Statistical Manual of Mental Disorders (DSM), currently in its fifth edition (DSM-V, 2013), captures commonly agreed upon mental conditions. Clinicians have deliberated at some length on the possible existence of technology addiction, although the DSM does not currently recognize it as a condition, maintaining instead that it manifests as a consequence of other preceding mental conditions, such as reduced impulse control (Yellowlees & Marks, 2007). This said, however, addictions to various facets of technology have attracted some research attention across a broad range of scholarly disciplines for some time, and there have been calls for its formal recognition (Block, 2008).
In the context of the information systems discipline, Carillo, Scornavacca, & Za, 2017 points out that psychological dependency (addiction) to information and communication technologies should not be confused with goal-oriented dependency. While the two concepts may be related and may influence individuals' reasoned IT usage decisions, goal-oriented dependency captures the extent to which an individual's capacity to reach his or her objectives depends on the use of specific technology. It also tends to focus on the more positive consequences of the use of technology. On the other hand, addiction tends to focus on the more negative effects of technology use as it relates to a psychological state of maladaptive dependency on the use of a technology to such a degree that typical obsessive-compulsive behavioral symptoms arise. This paper focuses on this facet of the phenomenon.
A growing body of research has pointed to the presence of addiction to several forms of information technology (Barnes & Pressey, 2014; Carillo et al. 2017; Griffiths, 2001; Lin, 2004; Turel, Serenko, & Giles, 2011; Turel & Serenko, 2010). Turel et al. (2011) report that neurobehavioral support has been offered for the existence of behavioral addictions, including technology addictions, and points to the similarities between substance and behavioral addictions (Helmuth, 2001). One study employing functional magnetic resonance imaging in online gaming found that urge/craving in substance addiction and urge/craving in online gaming addiction have analogous neurobiological mechanisms (Ko et al. 2009). Hence Turel et al. (2011, p. 1045) conclude that “It is, therefore, reasonable to apply concepts, models and theories from the substance addiction area to the fairly new field of behavioral addictions”.
Studies examining the problematic usage of technology have a considerable lineage; for example, Hadley Cantril and Gordon W. Allport questioned the potentially addictive nature of radio programs in their text The Psychology of Radio published in 1935. Later scholarship addressed dependencies on certain technologies such as excessive television viewing (Horvath, 2004; Mcllwraith, 1998), excessive video game playing (Keepers, 1990), ‘computer addiction’ (Shotton, 1991), and the addictive potential of the Internet (Brenner, 1997; Griffiths, 1996, 1997; Young, 1998), with the latter topic having attracted significant empirical attention (Bozoglan, Demirer, & Sahin, 2014; Bridges & Florsheim, 2008; Charlton & Danforth, 2007; Demirer & Bozoglan, 2016; Kuss, van Rooij, Shorter, Griffiths, & van de Mheen, 2013; Lehenbauer-Baum et al. 2015; Morahan-Martin & Schumacher, 2000; Pontes & Griffiths, 2016; Turel et al. 2011). A subset of Internet addiction research has also examined specific online activities, including addiction to online auctions (Turel et al. 2011) and virtual worlds (Barnes & Pressey, 2014). One natural extension of this line of scholarly enquiry that has received scholarly attention is problematic smartphone usage.
The first study to empirically examine mobile phone addiction is attributed to a master's thesis (Jang, 2002), conducted in South Korea. Various facets of smartphone addiction have been examined and published in recent years (see Table 1 below), with an emphasis on the drivers of problematic smartphone usage. Smartphone addiction may arguably be more important to study than Internet or computer addiction as smartphones offer a mobile computing platform and thus offer greater portability than other computing devices such as laptops and tablets, and addiction may be more acute (Demirci et al. 2014; Jeong et al. 2016; Kwon et al., 2013), resulting in the habitual checking of a device (Lee, 2015; Oulasvirta, Rattenbury, Ma, & Raita, 2012). Some commentators have speculated that smartphones may represent the preeminent technological device encouraging addiction for our time (Shambare, Rugimbana, & Zhowa, 2012).
Collectively, these studies represent diverse academic fields including information systems, computer studies, healthcare, education, and psychology, among others. Only a handful of studies, however, have empirically examined the motives, drivers or user perceptions towards smartphone usage and addiction (Bian & Leung, 2014; Bianchi & Phillips, 2005; Ehrenberg et al. 2008; Jeong & Lee, 2015; Pearson & Hussain, 2015; Takao et al. 2009; van Deursen et al. 2015; Zhang et al. 2014). Of this subset of papers, user perceptions towards smartphone usage and addiction have been looked at from a standpoint of personality drivers (e.g. low self-esteem, neuroticism, extraversion) (Bianchi & Phillips, 2005; Ehrenberg et al. 2008; Pearson & Hussain, 2015; Takao et al. 2009; Zhang et al. 2014), influencing factors (e.g. number of friends, academic achievement, and reading quantity) (Jeong & Lee, 2015), process and social orientation (e.g. smartphone usage types, emotional intelligence, social stress, and self-regulation) (van Deursen et al. 2015), and hybrid studies (e.g. research examining personality characteristics and patterns of smartphone use) (Bian & Leung, 2014).
The effect of user perceptions and the link to smartphone addiction is a pertinent area of enquiry as it relates to how users engage with technology and can become deeply immersed with it – sometimes to a problematic extent. Understanding user perceptions or beliefs are important as they influence user behavior, and help explain how users become absorbed with technology. Further, understanding what motivates users to harbor certain beliefs helps us to understand why they hold those beliefs; while prior research on mobile phone addiction has focused heavily on usage and attitudes, less attention has been placed on belief formation. This is the subject to which we now turn, particularly through introducing the concept of cognitive absorption.
While a number of theories help to illuminate user adoption and acceptance of information technologies – including diffusion of innovations theory, the theory of planned behavior, theory of reasoned action, and the technology acceptance model (TAM) (Ajzen, 1985, 1991; Brancheau & Wetherbe, 1990; Davis, 1989; Fishbein & Ajzen, 1975; Rogers, 1995) – they have limited power in explaining how beliefs around information technologies are shaped (Agarwal & Karahanna, 2000). Agarwal and Karahanna (2000) introduced the concept of cognitive absorption (CA) in order to help overcome this conceptual shortfall. CA shares a conceptual root with some of the first major IT user-acceptance theories including TAM by emphasizing instrumentality as a core driver of user beliefs, and where usage behavior is motivated by “… cognitive complexity beliefs” (Agarwal & Karahanna, 2000, p. 666).
CA also has the advantage of being grounded in a large corpus of scholarship in the cognitive and social psychology literature, where CA draws its theoretical basis from three related strands of literature: the personality trait dimension of absorption (Tellegen & Atkinson, 1974; Tellegen, 1981, 1982), the state of flow (Csikszentmihalyi, 1990; Trevino & Webster, 1992), and the notion of cognitive engagement (Webster & Hackley, 1997; Webster & Ho, 1997).
Defined as “… a state of deep involvement with software” (Agarwal & Karahanna, 2000, p. 673), cognitive absorption can act as a powerful motivating factor towards beliefs related to IT, where highly engaging and engrossing experiences result in users' ‘deep attention’ and complete immersion and engagement with an activity (Csikszentmihalyi, 1990; Deci & Ryan, 1985; Tellegen & Atkinson, 1974; Vallerand, 1997).
Agarwal and Karahanna (2000) proposed CA as a powerful motivating factor towards beliefs related to IT, where highly engaging and engrossing experiences result in ‘deep attention’. CA is driven by an intrinsic motivation (i.e. the enjoyment, satisfaction and pleasure as a result of an experience) as opposed to extrinsic motivation (i.e. the expectation of a reward associated with a certain behavior). As “… an end in themselves” (Csikszentmihalyi, 1990), intrinsic motivators have greater explanatory power in usage intentions than extrinsic motivators (Davis, Bagozzi, & Warshaw, 1992). Cognitive absorption is a multidimensional construct across five dimensions:
- i.
Temporal dissociation (“the inability to register the passage of time while engaged in interaction”);
- ii.
Focused immersion (“the experience of total engagement where other attentional demands are, in essence, ignored”);
- iii.
Heightened enjoyment (“the pleasurable aspects of the interaction”);
- iv.
Control (“the user's perception of being in charge of the interaction”); and
- v.
Curiosity (“the extent the experience arouses an individual's sensory and cognitive curiosity”).
We would expect that individuals with high levels of problematic smartphone and SNS service usage, or addiction, will experience higher levels of CA, as this provides some explanation of the deep state of involvement, engagement and attention that may be experienced by some individuals when interacting with computer-mediated environments, which may foster problematic behaviors among some users. Thus, addicted users will most likely have some form of perceptual distortion.
There is some evidence to support this assertion. The relationship between addiction and perceptual distortion may result in higher levels of CA, particularly as addiction may produce a framing effect that results in users perceiving websites more positively than non-addicted users (Barnes & Pressey, 2017; Turel et al. 2011). Addiction results in the modification of cognitive processes and the intensification of a particular experience. Hence, users who exhibit higher levels of addiction hold positive perceptions of a system (even if such perceptions are illogical), thus resulting in higher levels of absorption in a system. For example, Turel et al. (2011) found evidence that users with an addiction to online auctions reported higher levels of perceived usefulness, enjoyment and ease of use of an auction site, while Barnes and Pressey (2017) report that addiction to virtual worlds has a positive impact on cognitive absorption.
In sum, examining the relationship between CA and addiction affords us the capacity to understand the routes via which behaviors concerning technology are manifested and what drives individuals to harbor particular beliefs concerning IT, and “serves as a key antecedent to salient beliefs about an information technology” (Agarwal & Karahanna, 2000, p. 666). This would seem both valuable and timely given the ubiquity of smartphone technology and reports of problematic usage, and would help us to understand why some users experience a deeper state of involvement with a particular technology than others. In the following section we outline our hypotheses related to addiction to smartphone technology.
Section snippets
Hypothesis development
This section is organized into six areas. Initially, we examine smartphone addiction versus addiction to SNS, and this followed by the impact of cognitive absorption on addiction, and the demographic factors related to smartphone addiction. Next, we consider the impact of cognitive absorption on smartphone addiction, and finally the impact of cognitive absorption by gender, age and education.
Research design
The research design adopted involved a single cross-sectional convenience sample using a self-report survey. The study employed scales from previous research to measure the constructs in the study, although these were adapted and extended for the context of the study – social network applications and smartphones. The measure of cognitive absorption was adapted from Agarwal and Karahanna (2000) and comprises five factors: temporal dissociation (“the inability to register the passage of time
Smartphone addiction versus addiction to SNS
Our first series of tests sought to identify any difference between user addiction to the smartphone and addiction to SNS by means of a paired samples t-test between the summary variables for smartphone addiction and SNS addiction (see Table 2). The results indicate that there is a significant difference between these two forms of addiction, with a mean difference of 3.44 and t-value of 7.303 (p < .001, Msmartphone_addiction = 25.43, MSNS_addiction = 21.99). Hence H1 – addiction to the
Findings and discussion
The present paper contributes empirical evidence relating to addiction to smartphones versus addiction to social networking apps. While there are clearly related streams of research regarding addiction to a smartphone device and addiction to social networking sites these are not fully integrated, although the issue is alluded to in recent studies (De-Sola Gutiérrez et al. 2016; Jeong et al. 2016; Pearson & Hussain, 2015). No study to-date, however, has distinguished between addiction to
Conclusions
As Rudi Volti (1995) has observed “[our] inability to understand technology and perceive its effects on our society and on ourselves is one of the greatest, if most subtle, problems of an age that has been so heavily influenced by technological change.” The paradox of smartphone technology is that it has the capacity of simultaneously liberating users and also subjugating them, which may result in problematic user behaviors and even addiction. As such, it would seem imperative to understand the
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