Examining common information technology addictions and their relationships with non-technology-related addictions

Sigerson, Leif; Li, Angel Y.-L.; Cheung, Mike W.‐L.; Cheng, Cecilia

doi:10.1016/j.chb.2017.05.041

Cited by 64 publications

(52 citation statements)

References 48 publications

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“…In the present study, we analyzed the direct association between smartphone addiction assessed by the SPAI and Internet addiction assessed by the CIAS-R. As the items of the SPAI are derived from those of the CIAS-R, the resemblance of item wording between these two instruments may inflate the strength of their association. For instance, the effect size of the present finding ( r = 0.75) is considerably higher than those of previous studies ( r ’s ranged from 0.21 to 0.51) using instruments of smartphone addiction and Internet addiction that are structurally distinct (e.g., Mok et al, 2014 ; Choi et al, 2015 ; Sigerson et al, 2017 ). Nevertheless, this potentially inflated strength of association has also been obtained in past research using structurally similar instruments for assessing smartphone addiction and Internet addiction.…”

Section: Discussioncontrasting

confidence: 93%

“…The present analysis also demonstrates the convergent validity of SPAI-I by revealing its strong positive association with Internet addiction. Although the strength of this association does not exceed the suggested criteria for conceptual overlap between the two constructs ( Kline, 2005 ), the association obtained in our study is stronger than those derived from previous studies using other measures to examine the association between smartphone addiction and Internet addiction ( Choi et al, 2015 ; Pavia et al, 2016 ; Sigerson et al, 2017 ). This discrepancy with previous findings may occur as the SPAI is developed based on a validated measure of Internet addiction (i.e., the CIAS-R), and thus the items of these instruments may have similar wordings.…”

Section: Discussioncontrasting

confidence: 88%

“…Despite sharing several common symptoms and risk factors, smartphone addiction and Internet addiction are found to be conceptually distinct ( Sigerson et al, 2017 ). Compared to other Internet devices such as personal computers, the portability and availability of smartphones allow users to engage in addictive activities under a plethora of circumstances that are not possible for other information technology devices (e.g., Choi et al, 2015 ; Liu et al, 2016 ).…”

Section: Introductionmentioning

confidence: 99%

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Psychometric Properties and Factor Structures of Chinese Smartphone Addiction Inventory: Test of Two Models

et al. 2018

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There has been a growing concern of excessive smartphone use that interferes with people’s daily functioning, most notably among youngsters. The Smartphone Addiction Inventory (SPAI) was constructed to assess this type of information technology addiction. Although the SPAI was developed in a Taiwanese adolescent sample, this measure has not been validated on Chinese youngsters in other regions. Moreover, the initial evidence yielded a four-factor structure, but recent findings obtained an alternative five-factor structure. As no studies have systematically compared these two factor structures, which of the models fits the data better remained unknown. This study aimed to evaluate the empirical validity of both the four- and five-factor structures of the SPAI in a sample of university students from Mainland China (n = 463). Four psychometric properties of the SPAI were examined. First, the structural validity of both factor models was evaluated with confirmatory factor analysis. Satisfactory fit was found for both the five-factor model (RMSEA = 0.06, SRMR = 0.05, CFI = 0.99, TLI = 0.99) and the four-factor model (RMSEA = 0.07, SRMR = 0.06, CFI = 0.98, TLI = 0.98), but the five-factor model demonstrated an overall better model fit. Second, the five-factor model yielded good internal consistencies (all Cronbach α’s > 0.70). Third, concurrent validity of the SPAI was supported by its moderately strong correlations with four widely adopted criterion variables (i.e., loneliness, social anxiety, depression, and impulsivity). Lastly, the convergent validity of the SPAI was demonstrated by its strong, positive correlation with a popular, validated measure of Internet addiction. This study is the first to demonstrate the validity of the newly proposed five-factor model of the SPAI in a sample of Mainland Chinese youngsters.

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Section: Discussioncontrasting

confidence: 93%

Section: Discussioncontrasting

confidence: 88%

Section: Introductionmentioning

confidence: 99%

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Psychometric Properties and Factor Structures of Chinese Smartphone Addiction Inventory: Test of Two Models

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“…Individuals cannot be far from technology, owing to the need for faster access to various types of information, interactions, and faster communication (Au-Yong-Oliveiraa, Gonçalvesb, Martinsb, & Branco, 2018). However, there are consequences associated with this rapid development in technology, such as excessive usage (Davis, 2001;Sigerson, Angel, Cheung, & Cheng, 2017), high levels of involvement (Charlton & Danforth, 2007), and addiction (Turel, Serenko, & Giles, 2011). The technology in the field of communication led to the development of a smartphone (David, 2019).…”

Section: Introductionmentioning

confidence: 99%

Application of Rasch Rating Scale Model to Analysis of Fear of Missing Out a Smartphone

Alizamar

2019

JELS

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The presence of smartphones is one of the technological advancements that have contributed greatly to changes in social behavior. This led to the inception of the Fear of Missing Out (FoMO) smartphone device. The purpose of this study therefore was to determine the right instrument to measure the FoMO smartphone by analyzing response points obtained from respondents. The study sample consisted of three groups of test subjects, from large city, small town and villages with n values of 226, 248, and 55 respectively. The data in this study were obtained using the 5-point Likert scale politomy data from a Fear of Missing out scale instrument, distributed online. The research data were analyzed using the Rasch model by testing rating scale analysis through Threshold. The results showed that the rating scale answered choices turned into a 3-point Likert scale with those not right, less right, and very right.

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“…On the other hand, there are many studies, particularly from a neuroscientific perspective, which find parallels among substance-use disorders and IGD (and also other types of Internet-use disorders) and thus justify the classification as an addiction (Weinstein et al, 2017 ). On a behavioral level using questionnaires, some studies, however, show that different types of behavioral addictions (i.e., gambling disorder and different types of Internet addiction) have larger overlap among them compared with the overlap among behavioral addictions and substance-use disorders (Sigerson et al, 2017 ), speaking for a distinct category of behavioral addictions. One has to notice, that there are also significant differences across different types of substance-use disorders (Shmulewitz et al, 2015 ), and they are nevertheless classified together within one category in the DSM-5.…”

Section: Introductionmentioning

confidence: 99%

Merging Theoretical Models and Therapy Approaches in the Context of Internet Gaming Disorder: A Personal Perspective

Young

Brand

2017

Front. Psychol.

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Although, it is not yet officially recognized as a clinical entity which is diagnosable, Internet Gaming Disorder (IGD) has been included in section III for further study in the DSM-5 by the American Psychiatric Association (APA, 2013). This is important because there is increasing evidence that people of all ages, in particular teens and young adults, are facing very real and sometimes very severe consequences in daily life resulting from an addictive use of online games. This article summarizes general aspects of IGD including diagnostic criteria and arguments for the classification as an addictive disorder including evidence from neurobiological studies. Based on previous theoretical considerations and empirical findings, this paper examines the use of one recently proposed model, the Interaction of Person-Affect-Cognition-Execution (I-PACE) model, for inspiring future research and for developing new treatment protocols for IGD. The I-PACE model is a theoretical framework that explains symptoms of Internet addiction by looking at interactions between predisposing factors, moderators, and mediators in combination with reduced executive functioning and diminished decision making. Finally, the paper discusses how current treatment protocols focusing on Cognitive-Behavioral Therapy for Internet addiction (CBT-IA) fit with the processes hypothesized in the I-PACE model.

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Examining common information technology addictions and their relationships with non-technology-related addictions

Cited by 64 publications

References 48 publications

Psychometric Properties and Factor Structures of Chinese Smartphone Addiction Inventory: Test of Two Models

Psychometric Properties and Factor Structures of Chinese Smartphone Addiction Inventory: Test of Two Models

Application of Rasch Rating Scale Model to Analysis of Fear of Missing Out a Smartphone

Merging Theoretical Models and Therapy Approaches in the Context of Internet Gaming Disorder: A Personal Perspective

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