Online shopping has eliminated the need to visit physical commercial centres. As a result, trips to these centres have shifted from primarily shopping-motives to leisure, companionship, and dining. The shifting in consumer behaviour is implicated in the growing spatial agglomeration of restaurants/cafes within commercial centres in European cities. Conversely, in southern cities, various casual restaurants/cafes also serve as leisure and companionship hubs. However, their spatial patterns are less explained. This article aims to elucidate the spatial pattern of these diverse restaurants/cafes in a typical southern city, Surabaya City. In this study, we employ the term ‘food services’ to encompass the various types of restaurants/cafes found in southern cities. We gather Points of Interest (POIs) data about food services via web scraping on Google Maps, then map out their spatial distribution across 116 spatial units of Surabaya City. Utilising k-means cluster analysis, we classify these 116 spatial units into six distinct clusters based on the composition of food service variants. Our findings show that City Centres and Sub-City Centres are locations for different types of restaurants/cafes. The City Centre is typically a location for fine dining restaurants and cafes, whereas Sub-City Centres are locations for fast casual dining and fast food restaurants. Cafes and fast food restaurants are centralised throughout downtown areas. Casual food service restaurants, such as casual style dining, coffee shops, and food stalls, are dispersed along business, residential zones, and periphery areas without intense domination of any specific variant.

Artificial Intelligence (AI) has become a pivotal force in transforming the retail industry, particularly in the online shopping environment. This study investigates the impact of various AI applications—such as personalized recommendations, chatbots, predictive analytics, and social media engagement—on consumer buying behaviors. Employing a quantitative research design, data was collected from 760 respondents through a structured online survey. The snowball sampling technique facilitated the recruitment of participants, focusing on diverse demographics and their interactions with AI technologies in online retail. The findings reveal that AI-driven personalization significantly enhances consumer purchase intentions and satisfaction. Multiple regression analysis shows that AI personalization (β = 0.35, p < 0.001) has the most substantial impact on purchase intention, followed by chatbot effectiveness (β = 0.25, p < 0.001), predictive analytics (β = 0.20, p < 0.001), and social media engagement (β = 0.15, p < 0.01). Similarly, AI personalization (β = 0.30, p < 0.001), predictive analytics (β = 0.25, p < 0.001), and chatbot effectiveness (β = 0.20, p < 0.001) significantly influence consumer satisfaction. The hierarchical regression analysis underscores the importance of ethical considerations, showing that ethical and transparent use of AI increases consumer trust and engagement. Model 1 explains 45% of the variance in consumer behavior (R² = 0.45, F = 154.75, p < 0.001), while Model 2, incorporating ethical concerns, explains an additional 10% (R² = 0.55, F = 98.25, p < 0.001). This study highlights the necessity for retailers to leverage AI technologies ethically and effectively to gain a competitive edge, improve customer satisfaction, and drive long-term success. Future research should explore the long-term impacts of AI on consumer behavior and the integration of emerging technologies such as augmented reality and the Internet of Things (IoT) in retail.

This study explores the integration of data mining, customer relationship management (CRM), and strategic management to enhance the understanding of customer behavior and drive revenue growth. The main goal is the use of application of data mining techniques in customer analytics, focusing on the Extended RFM (Recency, Frequency, Monetary Value and count day) model within the context of online retailing. The Extended RFM model enhances traditional RFM analysis by incorporating customer demographics and psychographics to segment customers more effectively based on their purchasing patterns. The study further investigates the integration of the BCG (Boston Consulting Group) matrix with the Extended RFM model to provide a strategic view of customer purchase behavior in product portfolio management. By analyzing online retail customer data, this research identifies distinct customer segments and their preferences, which can inform targeted marketing strategies and personalized customer experiences. The integration of the BCG matrix allows for a nuanced understanding of which segments are inclined to purchase from different categories such as “stars” or “cash cows,” enabling businesses to align marketing efforts with customer tendencies. The findings suggest that leveraging the Extended RFM model in conjunction with the BCG matrix can lead to increased customer satisfaction, loyalty, and informed decision-making for product development and resource allocation, thereby driving growth in the competitive online retail sector. The findings are expected to contribute to the field of Infrastructure Finance by providing actionable insights for firms to refine their strategic policies in CRM.