Data for "Privacy Signals: Exploring the Relationship Between Cookies and Online Purchase Intention" published by RAC - Revista de Administração Contemporânea
Description
The data is from the article "Privacy Signals: Exploring the Relationship Between Cookies and Online Purchase Intention" and shows consumer perceptions of website transparency about cookie requests in the e-commerce environment. Overall, we used a quantitative methodology, through a descriptive study and four experimental studies. The results studies show that cookie acceptance positively influences the intention to purchase, only when the consumer accepts cookie collection and when they have a need for the product, resulting in greater perception of benefits associated with information disclosure. Risks did not show significance in this process. However, providing more information to consumers about data collection is advantageous because the intention to purchase is higher, even for those who do not accept cookies. The data for the experiments were collected using Qualtrics software and online, using Facebook, through sponsored ads to ensure the randomness of responses. The products used in the experiment scenarios were chosen because of the pandemic context, where the consumption of products used in the home increased, to the detriment of superfluous or luxury products. The samples of the experiments respected the minimum criteria of 30 people in each experiment condition, as suggested by Hair et al. (2009). Statistical analyses were done through IBM SPSS Statistics statistical software using the Procces macro, which is an extension created for SPSS for multivariate data analysis and mediation analysis, as well as integrated conditional process models (Hayes, 2018). Finally, we also used General Linear Model (GLM) in the analyses, as it is an extension of the linear regression model and indicated in cases of probability distributions other than the normal distribution, which makes it more flexible to handle the data (Hair et al. 2009).