A behavioral economic analysis of media multitasking: Delay discounting as an underlying process of texting in the classroom

Problematic mobile phone use as impulsive choice: Development and empirical verification of a reinforcer-pathology model

Problematic mobile phone use is characterized by its "impulsive" nature; users engage in it despite their negative attitude toward it. From a behavioral-economic perspective, this attitude-behavior discrepancy is generated by competing contingencies that involve smaller-sooner social reinforcers associated with mobile phone use and larger-later prosocial reinforcers potentially compromised by phone use. Based on this conceptualization, the reinforcer-pathology model of problematic mobile phone use is proposed, which posits that such phone use stems from excessive delay discounting of the social and prosocial reinforcers and/or excessive demand for the social reinforcers. A secondary data analysis of previously published studies was conducted, with the novel addition of principal component analysis and hierarchical cluster analysis of these data. The results generated evidence that supports the reinforcer-pathology model proposed in this article. Based on the theoretical analyses and accumulated empirical evidence, theory-driven prevention and intervention strategies for problematic mobile phone use are proposed. Overall, the reinforcer-pathology model of problematic mobile phone use provides a comprehensive framework for understanding and addressing this growing issue.

Text-Message Dependency, Executive Function, and Impulsivity in College Students: A Cluster Analysis

This study examined whether text-message users would differ in levels of executive function, a trait of impulsivity, and impulsive decision making. A sample of 167 college students (57 percent of whom were female and the mean age was 19.9 years with the standard deviation of 4.1) participated. Using a hierarchical cluster analysis with levels of text-message dependency, the participants were grouped into excessive and dependent users (dependent users), excessive but nondependent users (excessive users), or nonexcessive and nondependent users (normal users). The groups were then compared on the extent to which they differed in levels of executive function, impulsivity, and impulsive decision making. The results showed that, relative to excessive users, dependent users were lower on levels of executive function and higher on levels of the trait of impulsivity. The moderating effects of gender on these differences were also examined, but gender did not significantly moderate the differences. This study demonstrated that excessive text-message users are not necessarily dependent text-message users and executive function and impulsivity may play an important role in differentiating the two types of users.

A cluster analysis of text message users based on their demand for text messaging: A behavioral economic approach

The goal of this study was to determine whether cluster analysis could be used to identify distinct subgroups of text message users based on behavioral economic indices of demand for text messaging. Cluster analysis is an analytic technique that attempts to categorize cases based on similarities across selected variables. Participants completed a questionnaire about mobile phone usage and a hypothetical texting demand task in which they indicated their likelihood of paying an extra charge to continue to send text messages. A hierarchical cluster analysis was conducted on behavioral economic indices, such as demand intensity, demand elasticity, breakpoint, and the maximum expenditure. With the cluster analysis, we identified 3 subgroups of text message users. The groups were characterized by (a) high intensity and low elasticity, (b) high intensity and medium elasticity, and (c) low intensity and high elasticity. In a demonstration of convergent validity, there were statistically significant and conceptually meaningful differences across the subgroups in various measures of mobile phone use and text messaging. Cluster analysis is a useful tool for identifying and profiling distinct, practically meaningful groups based on behavioral indices and could provide a framework for targeting interventions more efficiently.