In particular, we aim at investigating the behavioral and physiological indexes elicited in the decision-making process of consumers, facing a financial choice in the framework of the two above mentioned types of scenario.
The analysis will be conducted in the laboratory on a sample made up of 80 naive individuals. The tasks will be implemented by means of customized software. Participants will complete the tasks by using a standard personal computer. In particular, participants will be asked to (1) complete a financial education questionnaire and (2) to complete the financial investment task. The task includes 22 trials including probabilistic and what-if scenarios. Each trial consists of two financial products where one of them stochastically dominates the other. Participants will be asked to make 22 financial choices during which behavioral and physiological indices will be recorded. In particular, the behavioral measures will include the (1) accuracy of the financial decision and the (2) reaction time (RT) associated to each decision (i.e. the time spent to make each decision). The physiological measures will include galvanic skin responses (GSRs) and heart rate (HR), both considered as reliable indexes of stress level and cognitive load during the decision-making process.
According to the neuroscientific investigation literature, the analyzed sample can provide significant and robust statical results for testing the addressed research questions by means of one-way, two- way and N-way ANOVA, and MANOVA statistical methodologies.
These studies will permit systematically testing, for the first time, the framework effect of the probabilistic and what-if investment scenarios on the accuracy of consumers’ financial decisions. We expect to find a greater number of accurate financial decisions in the more informative framework. Furthermore, RT will provide information about the duration of the decision-making process, with slower reaction times indicating a more complicated decision process. Similarly, we expect to find slower RT in the framework offering the clearest picture.