Mobile ubiquity: Understanding the relationship between cognitive absorption, smartphone addiction and social network services (2019)

1.1. Definitions and literature review

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.

1.2. Technology addiction

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.

1.3. Problematic smartphone usage

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.

1.4. 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.

2.1. Smartphone addiction versus addiction to SNS

The majority of studies examining problematic technology usage focus on the apparatus or device itself (radio, TV, computer, cell phone), rather than the content the technology provides (a particular program, software, website, or application). As Roberts and Pirog (2012: 308) note: “research must dig beneath the technology being used to the activities that draw the user to the particular technology.” There is a current debate in the literature as to whether addiction is to the phone or to the services provided upon it; as Pearson and Hussain (2015, p. 19) have observed: “With so many addictive applications available on the smartphone, it is difficult to decipher the cause and effect relationship of problematic use. The smartphones multi-faceted functionality may be addictive or it may be that users are addicted to a certain media.” Further, it may be a case of co-occurrence, where a user is addicted to both their smartphone and SNS sites.

As De-Sola Gutiérrez et al. (2016: 2) note: “There is evidence that the smartphone, with its breadth of applications and uses, tends to induce greater abuse than regular cell phones” (see also Taneja, 2014). However, is it a case of an addiction to the device (e.g. smartphone) or addiction to contents and applications? This line of enquiry chimes with earlier discussions around addiction to the Internet where it is recognized that there is “a fundamental difference between addictions to the Internet and addictions on the Internet” (Griffiths, 2012, p. 519). As Griffiths (2000) argues, online activities will differ in their capacity to be habit forming (see also Young, 1999).

To-date, however, no study has distinguished between addiction to the smartphone or addiction to the activities they afford users or has integrated these two perspectives, despite being alluded to in earlier research. De-Sola Gutiérrez et al. (2016: 1) mention: “Research in this field has generally evolved from a global view of the cell phone as a device to its analysis via applications and contents.” Central to this debate – and germane to the present study – is whether the problem is the smartphone or its applications and contents (De-Sola Gutiérrez et al. 2016).

Roberts et al. (2014) assert that owing to the expanding variety of activities that can be performed on cell phones, it is crucial that we comprehend which activities are more likely to be habit forming than others. Before undertaking such research it would seem a pertinent starting point to establish if there is a difference in addiction to the smartphone versus addiction to a set of core applications (such as SNS). SNS use has been shown to be a strong predictor of smartphone addiction, and stronger than other favored smartphone uses such as gaming (Jeong et al. 2016), and is the basis for its inclusion in the present study. Is it a case of addiction to one important aspect of smartphone usage, SNS, or is it a case of addiction more broadly or globally? We maintain that addiction to the device more generally will be more acute than addiction to SNS more specifically; therefore no single application may be more addictive generally than the device in totality. Consequently we propose the following:

2.2. The impact of cognitive absorption on smartphone and SNS addiction

We now consider the impact of cognitive absorption on addiction to SNS and smartphones separately. The most popular choice of app used on smartphones is widely reported as SNS, driven by the social relations they afford (Barkhuus & Polichar, 2011; Pearson & Hussain, 2015; Salehan & Negahban, 2013), and SNS use is reported as an antecedent of smartphone addiction (Salehan & Negahban, 2013). On this latter point, one explanation for the relationship between SNS availability and usage and smartphone adoption and addiction would be the level of cognitive absorption experienced by users and the highly engaging and engrossing experiences that result in users’ ‘deep attention’ and complete immersion (Csikszentmihalyi, 1990; Deci & Ryan, 1985; Tellegen & Atkinson, 1974; Vallerand, 1997), particularly as SNS expand and grow in sophistication. Hence, we posit that cognitive absorption will be greater for SNS than smartphones, as one of the outcomes of high levels of cognitive absorption is an inability to self-regulate potentially harmful or damaging behaviors, particularly those driven through the popularity of SNS.

2.3. Demographic factors and smartphone addiction

Younger people and females may be more at risk of smartphone addiction, while the evidence regarding the impact of education is more mixed. Initially, women tend to spend more daily time on their phones than males (Roberts et al., 2014), and there is compelling evidence to suggest that females experience a greater degree of cell phone dependence and problematic usage than males (Beranuy et al. 2009; Geser, 2006; Hakoama & Hakoyama, 2011; Jackson et al. 2008; Jenaro, Flores, Gomez-Vela, Gonzalez-Gil, & Caballo, 2007; Leung, 2008; Lopez-Fernandez, Honrubia-Serrano, & Freixa-Blanxart, 2012; Sanchez Martinez & Otero, 2009). Women (particularly those with low self-esteem) are deemed the most vulnerable group regarding smartphone addiction (Pedrero et al. 2012), and may rely more on smartphones than males in order to reduce social anxiety (Lee et al. 2014). Evidence also suggests that females send more text messages than their male counterparts and also tend to compose lengthier texts than males (Pawłowska & Potembska, 2012). Notwithstanding, some studies have found little or no differences in cell phone dependence by gender (Bianchi & Phillips, 2005; Junco, Merson, & Salter, 2010; Pearson & Hussain, 2015).

There is evidence to suggest that males and females use their phones in distinct and different ways. Geser (2006, p. 3) asserts that “the motivations and goals of cell phone usage mirror rather conventional gender roles.” While females tend to favor the emotional and personal exchange that phones allow, and hence value the social functionality of a device more than males – particularly social networking sites (such as Facebook) (Bianchi & Phillips, 2005; De-Sola Gutiérrez et al. 2016; Geser, 2006; Lenhart, Purcell, Smith, & Zickuhr, 2010) – males tend to value professional networking sites (such as LinkedIn) (Lenhart et al. 2010). Further, in their study of college students in the US, Roberts et al. (2014) found that females spent significantly more time on Facebook than their male colleagues, and that certain social networking sites were significant drivers of cell phone addiction. Hence we may argue that social motives are a prime driver in females’ use of smartphones.

Younger people, particularly adolescents, are prone to compulsive phone usage, while the total time spent on mobile phones decreases with age (De-Sola Gutiérrez et al. 2016), attributed to a reduced capacity for self-control (Bianchi & Phillips, 2005). Research has also pointed to the age one first obtains a cell phone and the increased likelihood of problematic usage in the future (Sahin, Ozdemir, Unsal, & Temiz, 2013).

Evidence regarding the relationship between educational attainment and problematic cell phone usage is equivocal. While some studies point to a link between problematic cell phone usage and level of education (particularly among those undertaking extended periods of study) (Tavakolizadeh, Atarodi, Ahmadpour, & Pourgheisar, 2014), others refute this (Billieux, 2012), and even report a link between low educational levels and problematic cell phone usage (Leung, 2007). Hence explanations that have been made concerning a link between problematic cell phone usage and educational attainment have not been particularly compelling.

We hypothesize the following:

2.4. The impact of cognitive absorption on smartphone addiction

Since SNS can be considered as a subset of the functionality and services provided on smartphones, and a dominant aspect of time spent on the device, the impact of cognitive absorption on phone addiction per se should (logically) be mediated by SNS addiction. In other words, addiction to SNS (such as Facebook, Instagram, Pinterest, etc.) will act as a draw or enticement for overall addiction to the device and a conduit for processing cognitive absorption.

There is support for this relationship in the related literature. As noted earlier, by far the most popular choice of smartphone app is SNS (Barkhuus & Polichar, 2011; Pearson & Hussain, 2015; Salehan & Negahban, 2013), and evidence points to a positive relationship between mobile phone addiction and SNS. Therefore, the rapid rise in smartphone adoption and usage corresponds with the significant proliferation of SNS, where SNS use drives smartphone addiction (Salehan & Negahban, 2013). Hence, the greater the pull of SNS, the greater the overall level of addiction to the device. Given that we would anticipate the direct impact of cognitive absorption on addiction will be greater for SNS than smartphones, as argued earlier we would expect addiction to SNS to mediate the relationship between cognitive absorption and smartphone addiction. Thus, we posit:

2.5. The impact of cognitive absorption by gender, age and education

Some evidence suggests that different genders perceive and identify with technology in different ways. Females are thought to emphasize people-oriented and socially-driven usage motives, while males are regarded as more task-oriented (Claisse & Rowe, 1987). Pertinent to the present study, differences have been reported between male and female users for certain psychological traits and compulsive smartphone usage. Lee et al. (2014) found that three psychological traits (need for touch, locus of control, and social interaction anxiety) differed between male and female smartphone users.

As noted earlier, compared to males, females tend to favor the emotional and personal exchange that phones allow, and hence value the social functionality of a device, particularly social networking sites (Bianchi & Phillips, 2005; De-Sola Gutiérrez et al. 2016; Geser, 2006; Lenhart et al. 2010). Further, females tend to use smartphones for communal reasons, while males favor agentic purposes (Lenhart et al. 2010). Hence, could females experience a deeper state of engrossment (or cognitive absorption) with SNS than males?

While Agarwal and Karahanna’s (2000) study of cognitive absorption experienced via the Internet found no differences according to gender, their study was conducted in a pre-SNS world. If, as assumed earlier, female users will experience higher levels of SNS addiction than males due to their predilection for the personal and emotional exchange that smartphones allow and their social functionality, then we can posit that female users will experience greater levels of cognitive absorption than male users.

In terms of age and education, there is no evidence to suggest that either factor plays a role in the levels of cognitive absorption experienced by users. We would therefore expect no differences in cognitive absorption by age or educational attainment.

Given the above, we hypothesize:

2.6. Cognitive absorption and user addiction

Our final series of hypotheses relate to user smartphone and SNS addiction and the levels of cognitive absorption experienced. As noted, we would anticipate that users experiencing high levels of problematic smartphone and SNS service usage, or addiction, will exhibit higher levels of CA. This would explain why some users experience a deep state of involvement, engagement and attention when interacting with computer-mediated environments while other users do not. Hence a heightened degree of CA is driving addiction and fostering problematic behaviors among some users, and acting as a form of perceptual distortion. Thus, we posit:

3.1. 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 while engaged in interaction”), focused immersion (“the experience of total engagement where other attentional demands are, in essence, ignored”), heightened enjoyment (“the pleasurable aspects of the interaction”), control (“the user’s perception of being in charge of the interaction”), and curiosity (“the extent the experience arouses an individual’s sensory and cognitive curiosity”). The wording of the cognitive absorption questions focused on the focal activity of “using social networking apps on my smartphone”. The measures for addiction to the smartphone and addiction to social networking services were adapted from Charlton and Danforth (2007). The measure for each was identical in content but differed in terms of focus on “my smartphone” or “social networking apps”. Five items were included from Charlton and Danforth (2007), supplemented with a further two items to better fit the context of the study “I feel lost without [social networking apps/my smartphone]” and “I tend to get easily distracted by [social networking apps/my smartphone].” All construct items were measured on seven-point Likert scales from 1 = Strong Disagree to 7 = Strongly Agree, where 4 = Neither Agree Nor Disagree. The scales items used in the survey are provided in the Appendix. Demographic and background information was collected for gender, age, highest level of educational achievement, daily SNS usage, and daily smartphone usage.

3.2. Data collection and analysis

The survey was implemented online via Qualtrics, and was distributed to students studying business at a university in the Mid-Atlantic region of the United States in February 2015. A total of 140 valid responses were collected. The sample was 68.6% female and 31.4% male. A total of 75% of the sample were 34 years of age or under, whilst 42.9% had a Bachelor’s degree and 13.6% a Master’s degree.

We examined the reliability of the measures using Cronbach’s Alpha; the scales for addiction to the smartphone and addiction to social networking apps had Cronbach’s Alphas of 0.835 and 0.890 respectively, well above the 0.7 threshold recommended by Nunnally (2010). The Cronbach’s Alpha for the cognitive absorption measure was 0.909, whilst those for its subcomponents ranged from 0.722 to 0.949, all of which are considered acceptable. Discriminant validity was also examined through variance inflation factors. Examining the Variance Inflation Factor (VIF) for the measured variables in our study, we find that all VIF values are well below 10, ranging from 1.032 to 1.404, indicating that multicollinearity is not a problem (Hair, Black, Babin, & Anderson, 2014). Common method bias was examined using Harman’s single factor test. The first factor explained only 35% of the variance for the sample and thus common method bias did not appear to be present.

In order to assess the levels of addiction for the ANOVA used for testing H7 and H8, we created three addiction groups using a method similar to Morahan-Martin and Schumacher (2000). Our addiction groups were assessed in terms of the number of “active” symptoms from the seven-item addiction scale. For a symptom to be “active”, the Likert scale response should be in excess of the mid-point, 4 (Charlton & Danforth, 2007; Morahan-Martin & Schumacher, 2000). The three addiction groups were: no addiction (no symptoms), low addiction (one or two symptoms) and high addiction (three or more symptoms).

The majority of the statistical analysis was conducted in SPSS 22. The hypothesis testing process utilized a number of statistical procedures: t-tests, analysis of variance (ANOVA), regression and a Sobel test calculated using the method of Baron and Kenny (1986).

4.1. Smartphone addiction versus addiction to SNS

4.2. The impact of cognitive absorption on addiction

To test whether the difference in the beta values is statistically significant we use the test of Paternoster, Brame, Mazerolle, and Piquero (1998) and the following formula:

The result is Z = 1.766, which is significant at the 5% level, confirming that CA has a more significant impact on SNS addiction than on smartphone addiction. Therefore, H2 – the direct impact of cognitive absorption on addiction will be greater for SNS than smartphones – is supported.

4.3. Demographic factors and addiction

We turn now to demographic facets of smartphone addiction. In order to test for differences according to gender, education and age, we use ANOVA to examine differences between groups for the summary addiction variables for both smartphones and SNS. In order to examine addiction by age group it was necessary to recode the older age groups into a single group for 35 + years due to inadequate data. We therefore had three age groups: 18–24 years, 25–34 years and 35 + years. Similarly, educational attainment was recoded into three groups due to inadequate data: High School Graduate or Below, Bachelor’s degree or Equivalent, and Master’s Degree or Equivalent.

The ANOVA tests for SNS service addiction revealed no significant differences according to age (F = 1.368; p = .258), gender (F = 0.327, p = .568) or educational attainment (F = 1.488, p = .229). Hence, H3 – SNS service addiction will vary by: (a) gender; (b) age; and (c) education – is rejected.

In terms of smartphone addiction and demographic factors, the ANOVA tests find significant differences between groups for educational attainment (F = 3.098, p = .048). A post-hoc test using the Bonferroni procedure found that the lowest educational group, High School Graduate or Below, had significantly higher addiction than Bachelor’s Degree or Equivalent (difference = 4.093, M_HighSchool = 27.462, M_Bachelors = 23.333, p = .042). No significant differences are revealed according to gender (F = 0.102, p = .750) or age (F = 1.008, p = .368). Hence, H4 – smartphone addiction will vary by: (a) gender; (b) age; and (c) education – is partially accepted, with evidence for smartphone addiction differences for education.

4.4. The impact of cognitive absorption on smartphone addiction

4.5. The impact of cognitive absorption by gender, age and education

In order to examine cognitive absorption by age group it was necessary to recode the older age groups into a single group for 35 + years due to inadequate data (as mentioned above). We therefore had three age groups: 18–24 years, 25–34 years and 35 + years. Equal variances were assumed for the ANOVA tests. The ANOVA tests revealed a small number of significant differences between age groups, namely for Curiosity and Control (F = 4.444, p = .013; and F = 5.008, p = .008 respectively). Post-hoc tests using the Bonferroni procedure found that for Curiosity, the 18–24 years age group is significantly greater than the 25–34 years age group (mean difference = 0.800, p = .016). For Control, the 35 + years age group was found to have levels significantly larger than the 25–34 years age group (mean difference = 0.731, p = .006). There were no other significant differences in the data. Thus, we find that H6b – the effect of cognitive absorption when using SNS will not differ by age – is partially supported.

The ANOVA tests to ascertain differences in cognitive absorption according to educational attainment found no significant results. Therefore, H6c – the effect of cognitive absorption when using SNS will not differ by educational attainment – is supported.

4.6. Cognitive absorption and user addiction

Our final series of tests examine the relationship between level of addiction and the extent of CA experienced by users of smartphones and SNS. In order to conduct these tests we created groups of users with high, low and no addiction, as outlined in the methodology section.

6.1. Implications

This study makes a number of contributions to theory, policy and practice. While many studies have examined aspects of smartphone addiction (see, for example, Bian & Leung, 2015; Rosen et al. 2013; van Deursen et al. 2015) few have differentiated between devices and applications in order to better understand problematic smartphone usage. Further, while earlier research has examined personality characteristics and compulsive smartphone usage (Lee et al. 2014; Wang et al. 2015), user perceptions have been overlooked. Measured through cognitive absorption, we find that users addicted to smartphones experience a deeper state of engagement and involvement – what might be described as a ‘cognitive corridor’. Therefore, to the best of our knowledge, this study is the first extant research project to draw a distinction between devices and applications in the context of problematic smartphone usage, as well as user perceptions.

A number of policy implications can also be drawn. Initially, we must show caution in describing an activity as ‘addictive’, particularly smartphone usage, which may actually be due to broader issues (e.g. impulse control disorders). This said, however, excessive smartphone usage may result in social withdrawal and damaged personal relationships. Similar to related debates around Internet addiction and its regulation (Barnes & Pressey, 2014), the regulation of smartphone usage is problematic and a subject of recent debate in the media, although beyond the remit of the present study. Whatever label may be ascribed to excessive use of smartphone technology – dependency, compulsive and habitual use, or addiction – it is a subject of concern in most developed countries, particularly given smartphone usage is almost entirely self-regulated. As devices and platforms become increasingly sophisticated, they have a greater capacity to encourage user engagement and involvement, which, in turn, can result in excessive usage. Further, research has demonstrated that compulsive smartphone usage may cause ‘technostress’ (Lee et al. 2014) – the inability to cope with new computing technologies (Brod, 1984), and users experiencing feelings of anxiety owing to communication and information overload (Ragu-Nathan, Tarafdar, Ragu-Nathan, & Tu, 2008).

The results of the present study also have practical implications. Smartphones have become an indispensable aspect of everyday life for many people, and whilst this technology affords the capacity to engage in social networking, entertainment, and educational pursuits, it can also however, result in overdependence and compulsive usage, and ultimately psychological distress for some users (James & Drennan, 2005; Lee et al. 2014). The industry implications for devices that can facilitate ever higher levels of cognitive absorptive are stark, with media reports questioning whether smartphones should actually carry health warnings, and concerns of problematic smartphone usage among young people (Pells, 2017; Siddique, 2015). A further practical application to support individuals with problematic smartphone usage would be an informative mobile app which records the application usage to its user, which should help with self-regulation.

6.2. Limitations and future research

This study has several limitations. Regarding internal validity, the research is based on participants’ self-reports, which may be vulnerable to common-method variance. This said, self-reports may be the most valid mechanism to assess individuals’ psychological characteristics, as subjects are best placed to provide insights into their own beliefs than outside observers. The problems associated with common-method variance, however, might be exaggerated (Spector, 2006). Secondly, regarding external validity, the study is based on a cross-sectional survey, administered to a US student sample, which may interfere with the detection of demographic relationships in the study. Future research might employ longitudinal research designs and broader sample profiles in an attempt to replicate the findings of the present study. In order to do so, further research should focus on specific services and seek a stratified sample that is more representative of the SNS population. Thirdly, given that two dimensions yielded nonsignificant results concerning the relationship between cognitive absorption and the two addictions, we may begin to question whether the current cognitive absorption construct is suitable in its present form for studies of addiction to social network services, and how immersion in social networks might differ from immersion in other potentially addictive behaviors. Scale development and refinement in this context provides another potential avenue for future research to improve internal validity.

There is considerable scope for future research on addiction to smartphones and their applications and the dark side of technology more generally, particularly among those members of society who are vulnerable including adolescents and those from younger age groups. One question that demands further attention is whether extended smartphone usage is actually increasing Internet addiction. Further, certain facets of problematic smartphone usage (for example, so-called smartphone zombies), are yet to receive detailed scrutiny. We must move on from studies of ‘global’ addiction to a device to more nuanced studies that distinguish between the device and its applications and user addictions, as well as an understanding of users’ cognitive perceptions of technology. Finally, given the ubiquity of smartphone devices it is important to comprehend the paradox of technology both in its capacity to liberate and to subjugate.