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. 2015 Oct 14;10(10):e0140296.
doi: 10.1371/journal.pone.0140296. eCollection 2015.

Pathological Buying Online as a Specific Form of Internet Addiction: A Model-Based Experimental Investigation

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Pathological Buying Online as a Specific Form of Internet Addiction: A Model-Based Experimental Investigation

Patrick Trotzke et al. PLoS One. .

Abstract

The study aimed to investigate different factors of vulnerability for pathological buying in the online context and to determine whether online pathological buying has parallels to a specific Internet addiction. According to a model of specific Internet addiction by Brand and colleagues, potential vulnerability factors may consist of a predisposing excitability from shopping and as mediating variable, specific Internet use expectancies. Additionally, in line with models on addiction behavior, cue-induced craving should also constitute an important factor for online pathological buying. The theoretical model was tested in this study by investigating 240 female participants with a cue-reactivity paradigm, which was composed of online shopping pictures, to assess excitability from shopping. Craving (before and after the cue-reactivity paradigm) and online shopping expectancies were measured. The tendency for pathological buying and online pathological buying were screened with the Compulsive Buying Scale (CBS) and the Short Internet Addiction Test modified for shopping (s-IATshopping). The results demonstrated that the relationship between individual's excitability from shopping and online pathological buying tendency was partially mediated by specific Internet use expectancies for online shopping (model's R² = .742, p < .001). Furthermore, craving and online pathological buying tendencies were correlated (r = .556, p < .001), and an increase in craving after the cue presentation was observed solely in individuals scoring high for online pathological buying (t(28) = 2.98, p < .01, d = 0.44). Both screening instruments were correlated (r = .517, p < .001), and diagnostic concordances as well as divergences were indicated by applying the proposed cut-off criteria. In line with the model for specific Internet addiction, the study identified potential vulnerability factors for online pathological buying and suggests potential parallels. The presence of craving in individuals with a propensity for online pathological buying emphasizes that this behavior merits potential consideration within the non-substance/behavioral addictions.

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Conflict of interest statement

Competing Interests: The authors have declared that no competing interests exist.

Figures

Fig 1
Fig 1. Illustration of the proposed mediation model.
The tendencies towards online pathological buying are assumed to be predicted by the predisposing factor shopping excitability (operationalized by variables of cue-reactivity) and online shopping expectancies (operationalized by motives to shop and buy on the Internet). The expectancies should mediate the relationship between shopping excitability and tendencies towards online pathological buying. The direct effect is indicated by the continued arrow; the indirect effects are drawn dashed.
Fig 2
Fig 2. Results for the structural equation model.
The factor loadings of the latent dimensions and the β-weights with the significance, as well as the total direct and total indirect effects, are depicted. e = error *** p < .001, ** p < .01, * p < .05
Fig 3
Fig 3. Subjective craving reactions.
Results with respect to the craving reactions pre- and post-cue presentation for the participants scoring high and low on the s-IAT-shopping. Error bars represent standard deviation (SD). *** p < .001, n.s. = not significant.
Fig 4
Fig 4. Frequencies of participants with problematic buying behavior.
Illustration of the frequencies of those participants who were screened as pathological buyers by the Compulsive Buying Scale (CBS; total score > 1.09) and as problematic/pathological buyers by the Short Internet Addiction Test modified for shopping (s-IATshopping; problematic score > 30; pathological score > 37). Whole sample size N = 240.

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