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The digital era has fundamentally transformed the market landscape, requiring e-commerce platforms to understand the determinants of consumer behavior. This study aims to analyze the role of E-commerce Trust as a mediating variable in the relationship between Perceived Ease of Use, Perceived Usefulness, and Perceived Enjoyment on Purchase Intention. Using a quantitative approach, data was collected from 326 active users of the Lazada platform in Central Java. Data analysis was conducted using the Structural Equation Modeling-Partial Least Squares (SEM-PLS) method. The results show that the three main factors ease, usefulness, and enjoyment have a positive and significant influence on Brand Trust and consumer Purchase Intention directly. Furthermore, it was found that E-commerce Trust acts as a crucial mediator that strengthens Purchase Intention through positive user experience. Theoretically, this study strengthens the Technology Acceptance Model (TAM) framework and the context of Cross-Border E-commerce (CBEC) in the digital retail industry. Practically, these findings emphasize the importance of optimizing platform features to enhance transaction convenience and security and build long-term loyalty. The research's implications suggest that platform managers should focus on both aesthetics and functionality to create a credible and enjoyable digital shopping ecosystem for consumers in developing regions.

This research extends the previous work of Tsaqila, Winiarti, and Widaningrum (2024), who applied the Complex Proportional Assessment (COPRAS) method within a decision support system for supermarket branch location selection. Unlike the prior study, which focused on Ponorogo through a web-based framework, this study expands the implementation of COPRAS to the Medan Area, Medan Kota, Medan Polonia, dan Medan Maimun districts, adapting it to local urban, social, and economic characteristics. The main objective is to identify the most strategic site for a new supermarket by analyzing multiple criteria, including land cost, population density, accessibility, safety, cleanliness, and disaster risk. Data were collected from both field surveys and official government publications. The findings reveal that the COPRAS method provides reliable and objective assessments among the evaluated alternatives, with Medan Area emerging as the most suitable location for supermarket development. Overall, this study broadens the practical scope of the COPRAS method in a different regional context and reinforces its reliability and adaptability as a multi-criteria decision-making tool in the modern retail industry.

Organizational Communication Climate Mangement in Improving Charles & Keith Employee Work Motivation (Case Study at Charles & Keith Store Summarecon Mall Serpong). Organizational communication climate is a key element in creating a productive work environment, especially in the dynamic retail industry. This study aims to describe the organizational communication climate formed at the Charles and Keith Store Summarecon Mall Serpong and to determine the level of employee work motivation at the Charles and Keith Store Summarecon Mall Serpong. This study was conducted at the Charles & Keith Store located in Summarecon Mall Serpong, Tangerang for 3 months, starting from April to June 2025 using a qualitative approach with a case study method, involving in-depth interview observations, and documentation of data collection techniques. The results of the study indicate that a warm, supportive, and transparent communication climate plays a vital role in creating a healthy and inspiring work atmosphere. Employees feel valued because they are given space to express their opinions, are involved in decision-making, and receive honest and constructive feedback. In addition, trust and honesty are important foundations in building harmonious and work-oriented working relationships. These findings emphasize that good communication climate management has a direct impact on employee morale, loyalty, and productivity in the modern retail sector.

Internet of Things (IoT)-based digital transformation has become a major catalyst in improving the efficiency of operational systems in various sectors, including the modern retail industry. One of the common logistics problems found in supermarket environments is the accumulation of unorganized shopping trolleys, which can hinder service flow and increase staff workload. This study presents a design of an IoT-based autonomous smart trolley system and automatic navigation to address these problems in a structured manner. The system design utilizes the integration of ESP32 and Arduino UNO microcontrollers, ultrasonic sensors for distance detection, line sensors for automatic path navigation, and Raspberry Pi modules for visual image processing in location tracking. The system is designed to be able to independently reposition the trolley to a predetermined parking station. Conceptual analysis shows that this system has significant potential in reducing operational costs, increasing labor efficiency, and strengthening customer service automation. Initial evaluation of technical and economic feasibility aspects strengthens the opportunity for widespread system implementation in the future. This design is the first step in developing a smart retail solution based on adaptive technology that is in line with the principles of Society 5.0. Furthermore, the development of this smart trolley system also considers user safety and comfort through additional features such as anti-collision sensors, an early warning system in the event of technical problems, and a manual control option as an alternative in emergency situations. The integration of Internet of Things-based technology also enables real-time monitoring and management systems through a web-based dashboard or mobile application, which can be accessed by supermarket management for operational analysis. Thus, this system not only addresses internal logistics needs but also contributes to improving the overall customer experience.

Market basket analysis is an important technique in data mining used to understand consumer purchasing patterns. This research uses the Apriori algorithm to identify relationships between products in the shopping basket, aiming to improve sales and marketing strategies in the retail industry. The focus of this study is on retail transaction data from West Java Province, which has a large and diverse population, reflecting complex consumer purchasing patterns. The research identifies several key issues: limited understanding of consumer behavior, unoptimized business strategy opportunities, and challenges in managing large transaction data. As a solution, the application of the Apriori algorithm can help find frequent consumer purchasing patterns and design more effective marketing strategies. The results show that market basket analysis using the Apriori algorithm is effective in understanding consumer purchasing patterns in the retail industry. This algorithm allows companies to discover itemsets that frequently appear together in transactions, which can be used to design more effective marketing and sales strategies.

The development of digital technology has had a significant impact on the use of demand deposits in the trading business. This discussion aims to analyze the effect of the application of digital technology on the use of demand deposits in the retail industry. Several retail companies are still facing challenges related to technological infrastructure and the availability of reliable internet access. In order to optimize the use of demand deposits in the trading business with the adoption of digital technology, retail companies need to involve employees in training and developing technology skills. Attention to data protection and customer privacy is also required and the development of strategies to promote consumer acceptance of digital payment methods.