E-commerce e o frete grátis: Uma análise com base na teoria do prospecto

Abstract

The present study investigates the impact of shipping costs on purchasing decisions in e-commerce from the perspective of Behavioral Economics. The central issue analyzed is how the perception of shipping fees influences consumer choices, especially considering the principles of loss aversion and the "power of free" effect. The research is justified by the scarcity of studies directly addressing the relationship between shipping costs and purchasing behavior, providing insights for optimizing business strategies. The theoretical framework is based on Behavioral Economics, exploring concepts such as bounded rationality (SIMON, 1955) and Prospect Theory (KAHNEMAN; TVERSKY, 1979), which highlight how cognitive biases affect financial decisions. Studies by ARIELY (2009) and RENNIE et al. (2020) indicate that the word "free" has a strong emotional influence, often outweighing rational cost-benefit evaluations. The research adopts a quantitative methodology, with a questionnaire applied to 146 consumers, collecting data on demographic profiles, online shopping frequency, the influence of discount coupons, and willingness to pay for shipping in different scenarios. The data were statistically analyzed using R software, applying the chi-square test and Cramér’s V coefficient to assess associations between variables. The results indicate that most consumers prefer to wait longer for delivery to avoid paying shipping fees, even when the total product cost remains unchanged. In one of the analyzed scenarios, 65.1% of participants chose a product with free shipping priced at R$20.00 over an option costing R$10.00 plus R$10.00 for shipping, demonstrating strong loss aversion. As a limitation, the study highlights the need for a more diverse sample to deepen the analysis of the influence of demographic variables. For future research, it is suggested to explore the interaction between delivery time and consumer value perception, as well as to test alternative pricing strategies to minimize the effects of loss aversion bias.