The research’s primary objective was to ascertain if a behavioral economic framework of need analysis is related to texting while driving. To this end, we developed a new hypothetical task designed to measure the degree and elasticity of the need for social interaction from texting while driving. This task involved a situation in which participants get a text message while driving. They rated the probability of responding to a text message immediately versus waiting to answer before arriving at a destination once the amounts of a good for texting while driving ranged from $1 to $300. To evaluate this undertaking’s construct validity, the situation presented two flaws to some destination (15 min and 60 min). The requirement for social interaction from texting was more extreme (more significant in the lowest level of the fine) and less elastic (less sensitive to the gain in the quantities of the fine) for drivers that self-reported a greater frequency of texting while driving than if you are self-reported a lesser frequency of texting while driving. Demand was also more extreme and less elastic under the 60-min delay condition than under the 15-min condition. The proof-of-concept study results suggest that behavioral economic requirement analyses are potentially helpful for understanding and predicting texting while driving.
The study complements the increasing literature on behavioral economic approaches toward texting while driving by completing earlier investigations that have examined the function of delay and probability discounting in texting while driving. The study results suggest that measures of need elasticity and intensity can help a broader understanding of individual differences in the evaluation of social interaction acquired from texting while driving.
The current study also provides a rich source of information regarding the significance of texting while driving to differing amounts of monetary penalties, which leads to a greater comprehension of the economic variables that determine the maladaptive option. In other words, the present data support the conclusion that an increase in the amount of a fine for texting while driving may be an effective method to reduce the behavior. For this function, the value of Pmax, the stage where the requirement curve shifts from inelastic to elastic, delivers an empirical foundation for determining a possibly sufficient amount of the fine for texting while driving. From a policy-making standpoint, Pmax can be regarded as a quantitative description of the stage where the sum of a good becomes sufficiently high and maximizes its effectiveness in reducing texting while driving. Consistent with this idea, require analysis was employed to analyze potential policy implications of different commodities, like cigarettes and high caloric food.
Moreover, validation of the approach comes from Grace, Kivell, and Laugesen. They revealed demand elasticity by a hypothetical cigarette buy task called consumption among smokers subsequent increases in tobacco excise taxes. Taken together, after the current study is replicated using a more varied and more extensive sample, mimicked demand curves may provide an essential and potentially distinctive source of information about how human drivers’ behavior will change following a rise in the amount of a good. This way, the current proof-of-concept study shows great promise in paving the way for”empirical public policy” related to texting while driving.