A study investigating the association between compulsive buying with measures of anxiety and obsessive–compulsive behavior among internet shoppers

Zhang

et al. 2015

Psychiatry Research

This longitudinal study examined the association between psychosocial antecedents, including illicit drug use, and adult compulsive buying (CB) across a 29-year time period from mean age 14 to mean age 43. Participants originally came from a community-based random sample of residents in two upstate New York counties. Multivariate linear regression analysis was used to study the relationship between the participant’s earlier psychosocial antecedents and adult CB in the fifth decade of life. The results of the multivariate linear regression analyses showed that gender (female), earlier adult impulse buying (IB), depressive mood, illicit drug use, and concurrent ADHD symptoms were all significantly associated with adult CB at mean age 43. It is important that clinicians treating CB in adults should consider the role of drug use, symptoms of ADHD, IB, depression, and family factors in CB.

Section: Discussionmentioning

confidence: 78%

Compulsive buying: Earlier illicit drug use, impulse buying, depression, and adult ADHD symptoms

Zhang

et al. 2015

Psychiatry Research

Journal of Psychiatric Research

“…The importance of compulsivity has much less been identified in PIU (Bernardi and Pallanti, 2009, Pallanti, 2010), although specific types of problematic online behaviours have been identified to have compulsive components (King and Barak, 1999, Greenfield, 1999), (Wetterneck et al., 2012), (Weinstein et al., 2015a, Weinstein et al., 2015b). Our results showed that compulsivity variables are useful to make out-of-sample predictions of PIU, suggesting that compulsivity as a dimensional variable plays an important role in those behaviors and merits further investigation.…”

Section: Discussionmentioning

confidence: 99%

Problematic internet use (PIU): Associations with the impulsive-compulsive spectrum. An application of machine learning in psychiatry

Ioannidis

Chamberlain

Treder

et al. 2016

Problematic internet use is common, functionally impairing, and in need of further study. Its relationship with obsessive-compulsive and impulsive disorders is unclear. Our objective was to evaluate whether problematic internet use can be predicted from recognised forms of impulsive and compulsive traits and symptomatology. We recruited volunteers aged 18 and older using media advertisements at two sites (Chicago USA, and Stellenbosch, South Africa) to complete an extensive online survey. State-of-the-art out-of-sample evaluation of machine learning predictive models was used, which included Logistic Regression, Random Forests and Naïve Bayes. Problematic internet use was identified using the Internet Addiction Test (IAT). 2006 complete cases were analysed, of whom 181 (9.0%) had moderate/severe problematic internet use. Using Logistic Regression and Naïve Bayes we produced a classification prediction with a receiver operating characteristic area under the curve (ROC-AUC) of 0.83 (SD 0.03) whereas using a Random Forests algorithm the prediction ROC-AUC was 0.84 (SD 0.03) [all three models superior to baseline models p < 0.0001]. The models showed robust transfer between the study sites in all validation sets [p < 0.0001]. Prediction of problematic internet use was possible using specific measures of impulsivity and compulsivity in a population of volunteers. Moreover, this study offers proof-of-concept in support of using machine learning in psychiatry to demonstrate replicability of results across geographically and culturally distinct settings.

J of Consumer Behaviour

“…Compulsive buying has been studied intensively in the social‐psychological literature. One popular approach to understanding this behavioral disorder has been to focus on the impact of psychological factors, such as self‐esteem (e.g., Roberts et al, 2014), self‐congruence (e.g., Japutra, Ekinci, & Simkin, 2019), level of anxiety, and stress (e.g., Weinstein, Mezig, Mizrachi, & Lejoyeux, 2015) and tendency to seek hedonic pleasure (e.g., Horváth & Adıgüzel, 2018). These previous studies suggest that consumers may purchase compulsively with the goal of alleviating anxiety, relieving stress, enhancing self‐esteem, expressing identity, and/or getting repeated excitement from shopping.…”

Section: Conceptual Framework and Literature Reviewmentioning

confidence: 99%

Mindsets, shopping motivations and compulsive buying: Insights from China

Japutra

Song

2020

This study investigates how mindsets (i.e., the beliefs that individuals have about the nature of human characteristics) are associated with compulsive buying behavior (i.e., the tendency to buy impulsively and obsessively). In particular, we test the relationships between consumer mindsets, three shopping motivations (deal proneness, social comparison, and hedonic motives) and compulsive buying. Based on a survey of 421 respondents conducted in China, our findings reveal the mechanism through which mindsets affect compulsive buying. The results show that when consumers believe in the consistency of personal traits (fixed mindset), they tend to be more vulnerable to deal offers and social comparison. These motivations increase the desire to seek hedonic pleasure, which is associated with a greater propensity for compulsive buying. However, when consumers believe that personal traits can be changed and developed (growth mindset), they seek hedonic pleasure in shopping, which result in a greater tendency to engage in compulsive buying. The present study adds to the existing body of knowledge by unveiling how different mindsets are associated with compulsive buying. In terms of practical implications, this study provides policy makers and marketers with a better understanding of the different motivations that lead to compulsive buying.