A Behavioral Economic Analysis of Demand for Texting while Driving

Optimizing variables for contingency management among infant caregivers using a simulated purchase task

Behavioral economics offers a framework for understanding choice making around public health concerns such as drug use and distracted driving. Such a framework could be beneficial to understanding caregiver choices related to arranging an infant sleep environment. Nonadherence to infant sleep safety guidelines provided by the American Academy of Pediatrics increases the risk of sleep-related infant deaths. The present study adopted a willingness-to-accept purchase task procedure, used in previous research to evaluate variables that predict abstaining from consuming alcohol, to evaluate intention to adhere to recommendations for arranging a safe infant sleep environment. This analysis would inform contingency management incentive scales used to measure caregiver adherence following training caregivers to arrange an infant sleep environment and identify variables that might predict treatment engagement. The results identified incentive sizes, condition duration, and participant variables that predict caregiver adherence. The results can be applied to future investigations that train caregivers to arrange an infant sleep environment.

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.

A hierarchical cluster analysis of young drivers based on their perceived risk and frequency of texting while driving

INTRODUCTION

The present study attempted to provide a proof-of-concept of usefulness of cluster analysis for identifying distinct and practically meaningful subgroups of drivers who differed in their perceived risk and frequency of texting while driving (TWD).

METHOD

Using a hierarchical cluster analysis, which involves sequential steps in which individual cases are merged together one at a time based on their similarities, the study first attempted to identify distinct subgroups of drivers who differed in their perceived risk and frequency of TWD. To further evaluate the meaningfulness of the subgroups identified, the subgroups were compared in terms of levels of trait impulsivity and impulsive decision making for each gender.

RESULTS

The study identified the following three distinct subgroups: (a) drivers who perceive TWD as risky but frequently engage in TWD; (b) drivers who perceive TWD as risky and infrequently engage in TWD; and (c) drivers who perceive TWD as not so risky and frequently engage in TWD. The subgroup of male, but not female, drivers who perceive TWD as risky but frequently engage in TWD showed significantly higher levels of trait impulsivity, but not impulsive decision making, than the other two subgroups.

DISCUSSION

This is the first demonstration that drivers who frequently engage in TWD can be categorized into two distinct subgroups that differ in terms of the perceived risk of TWD.

PRACTICAL APPLICATIONS

For drivers who perceived TWD as risky yet frequently engage in TWD, the present study suggests that different intervention strategies may be needed for each gender.

A behavioral economic demand analysis of mothers' decision to exclusively breastfeed in the workplace

The present study determined whether behavioral economic demand analysis could characterize mothers' decision to exclusively breastfeed in the workplace. Females, aged between 18 and 50 who have given birth in the past three years, completed a novel demand task with hypothetical scenarios, in which they returned to work with a 2-month-old baby. Participants rated their likelihood of breastfeeding their baby at a workplace lactation room versus formula-feeding their baby at their desk. The distance to the lactation room ranged from 10 s to 60 min. This assessment was conducted with and without hypothetical financial incentives for 6-month exclusive breastfeeding. Primary dependent measures were demand intensity and change in demand elasticity, which could conceptually represent initiation and continuation of breastfeeding, respectively. Demand for breastfeeding was more intense and less elastic (i.e., more likely to initiate and continue breastfeeding) among mothers with an experience of 6-month exclusive breastfeeding and under the condition with the financial incentives. The novel demand task can potentially provide a useful behavioral marker for quantifying mothers' decision to initiate and continue exclusive breastfeeding in the workplace, informing workplace policy regarding lactation rooms, identifying risk for early cessation, and developing and individualizing an intervention to assist mothers to exclusively breastfeed in the workplace.

Applying Mixed-Effects Modeling to Behavioral Economic Demand: An Introduction

Behavioral economic demand methodology is increasingly being used in various fields such as substance use and consumer behavior analysis. Traditional analytical techniques to fitting demand data have proven useful yet some of these approaches require preprocessing of data, ignore dependence in the data, and present statistical limitations. We term these approaches "fit to group" and "two stage" with the former interested in group or population level estimates and the latter interested in individual subject estimates. As an extension to these regression techniques, mixed-effect (or multilevel) modeling can serve as an improvement over these traditional methods. Notable benefits include providing simultaneous group (i.e., population) level estimates (with more accurate standard errors) and individual level predictions while accommodating the inclusion of "nonsystematic" response sets and covariates. These models can also accommodate complex experimental designs including repeated measures. The goal of this article is to introduce and provide a high-level overview of mixed-effects modeling techniques applied to behavioral economic demand data. We compare and contrast results from traditional techniques to that of the mixed-effects models across two datasets differing in species and experimental design. We discuss the relative benefits and drawbacks of these approaches and provide access to statistical code and data to support the analytical replicability of the comparisons.

Supplementary Information

The online version contains supplementary material available at 10.1007/s40614-021-00299-7.

Analysis of Driving Behavior Based on Dynamic Changes of Personality States

This study investigated the relationship between personality states and driving behavior from a dynamic perspective. A personality baseline was introduced to reflect the driver's trait level and can be used as a basic reference for the dynamic change of personality states. Three kinds of simulated scenarios triggered by pedestrian crossing the street were established using a virtual reality driving simulator. Fifty licensed drivers completed the driving experiments and filled in the Neuroticism Extraversion Openness Five-Factor Inventory (NEO-FFI) questionnaire to measure the drivers' personality baselines. Key indicators were quantified to characterize the five types of personality states by K-means clustering algorithm. The results indicated that the high-risk situation had a greater impact on the drivers, especially for drivers with openness and extroversion. Furthermore, for the drivers of extroverted personality, the fluctuation of personality states in the high-risk scenario was more pronounced. This paper put forward a novel idea for the analysis of driving behavior, and the research results provide a personalized personality database for the selection of different driving modes.

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.