Buying domestic products has become increasingly important in many countries. As a form of social influence, social norms affect people’s domestic purchasing intentions and behavior. The current study aims to examine the mechanisms by which social norms influence domestic purchasing intentions through the lens of consumer ethnocentrism and domestic product judgments. The data were collected through an online survey in China, and a total of 346 valid responses were obtained. The results indicate that social norms influence domestic purchasing intention through four paths, namely, direct path, motivational path, cognitive path, and motivational–cognitive path. Consumer ethnocentrism and domestic product judgments, serving as the motivational and cognitive factors, respectively, play mediating and serial mediating roles in the relationship between social norms and domestic purchasing intention. In addition, consumer ethnocentrism has two dimensions, namely, pro-domestic and anti-foreign consumer ethnocentrism, and only the former plays a significant role in the model. The current study has theoretical contributions to research on domestic purchasing intention and practical implications for interventions in domestic purchasing behavior. Future studies are encouraged to conduct experiments, distinguish between different types of social norms, measure purchasing behavior, and verify the relationships in other countries.

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Ancient glasses identification and classification has been widely developed to investigate the ancient culture and historical issues. However, existing researches are concentrated on the classifications for ancient glass with unacceptable identification accuracy, which ignores the prediction method can also successful identify the types of ancient glass. Therefore, there is a demand for correctly predicting types of ancient glass with acceptable identification accuracy and reasonable system costs. Logistic regression models are statistical methods used to predict a binary (yes/no, 0/1) outcome based on one or more predictor variables. The aim of this paper is to make species predictions for ancient glass based on chemical composition distributions, and to determine whether an unknown glass sample is a high potassium or leadbarium glass. We collected data from 61 sets of samples, each corresponding to fourteen chemical compositions, and used a logistic regression algorithm to make species predictions for these sample glasses, solving for the parameters using Newton's method and gradient descent, and comparing them.