In recent years, convolutional neural networks (CNN) have become leading algorithms for many computer vision tasks. Many studies have used deep learning and artificial intelligence (AI). This paper summarizes the development of deep learning approaches and the impact of AI on marketing and customer behavior and discusses the challenges facing marketing research. The major marketing application of AI is to support customer decision-making. However, most previous marketing research has focused on how to contribute to marketing decision-making in practice. We believe that there is room for research on how deep learning approaches can be used to support marketing decision-making. One area that has seen major developments in deep learning approaches is that of image data analysis, including video analysis. In marketing decision-making, market segmentation and positioning strategies are closely related to image data analysis. We believe that more research based on deep learning approaches should be conducted to support decision-making in these areas. Therefore, to support decision-making in marketing, we present studies on market segmentation and position- ing strategy formulation as examples of studies that combine deep learning approaches with traditional marketing research.

In this study, we examined reciprocal relations between students’ achievement emotions (enjoyment, anxiety, and boredom) and learning strategies (deep-processing strategy and surface-processing strategy). Japanese elementary school students (N = 111; 5th and 6th grade students) twice completed self-report measures of achievement emotions and learning strategy use in mathematics and Japanese classes. Results of the cross-lagged panel model indicated that “enjoyment” positively predicted the use of deep-processing strategies and negatively predicted the use of surface-processing strategies. In addition, “anxiety” positively predicted the use of surface-processing strategies, and “boredom” negatively predicted the use of deep-processing strategies and positively predicted the use of surface-processing strategies in Japanese class. Results also indicated that the use of deep-processing strategies negatively predicted “boredom.” In addition, the use of deep-processing strategies positively predicted “joy” and negatively predicted “anxiety” in Japanese class. These results suggest that emotions can be antecedents of students’ learning strategies and that the development of emotion differs depending on strategy use.

Public confidence in the police has become an important issue in many countries because high levels of trust and confidence can allow the public to believe the police organisation is accountable and legitimate. Much empirical research on public attitudes towards policing is available in Western countries, and it has been revealed that community policing can affect the levels of trust and confidence in the police both positively and negatively. Using data from an online survey, this article seeks to address the impact of community policing on public trust and confidence in the Japanese police. The results of a series of hierarchical multiple regression models demonstrated that being female, participating in crime prevention and self-defence classes are associated with high levels of trust in the police, while being liberal and experiencing police-initiated contact are associated with low levels of trust in the police. The implications of the findings for theory, research, and policing policy and practice are discussed.