Purpose: This study aims to identify the primary determinants of consumer behavior influencing customer satisfaction in the context of online mobile application (App) purchases of perishable products. Utilizing the well-established SERVQUAL (Service Quality) model, which has been extensively studied in various service-oriented settings, the research seeks to determine the factors with the greatest impact on customer satisfaction during online transactions of perishable products. Design: The investigation focuses on analyzing the five core dimensions of the SERVQUAL model: tangibles, reliability, responsiveness, assurance, and empathy. The study employs a survey methodology administered through Google Forms, targeting the population residing in the Klang Valley of Malaysia. A total of 400 samples were successfully collected using a snowball sampling technique. Methodology: The study employs the SERVQUAL model as the theoretical framework to examine the dimensions of tangibles, reliability, responsiveness, assurance, and empathy. The survey, conducted through Google Forms, targeted the population in the Klang Valley of Malaysia, with a sample size of 400 collected through snowball sampling. Findings: The study’s outcomes reveal the robust predictive capability of the overarching SERVQUAL model in the realm of online perishable product procurement. Notably, the assurance dimension emerges as the most influential factor, emphasizing its pivotal role in shaping and defining customer satisfaction for online retailers of perishable goods in the Malaysian market. Novelty: This research contributes to the understanding of consumer behavior in online perishable product purchases, by identifying determinants of consumer behavior; the study promotes sustainable production and responsible consumption within the perishable products category, offering insights beneficial for online retailers in the Malaysian market. This study aligns with United Nations sustainable development goals especially industry innovation, food security and responsible consumption.

This study conducts a systematic review to explore the applications of Artificial Intelligence (AI) in mobile learning to support indigenous communities in Malaysia. It also examines the AI techniques used more broadly in education. The main objectives of this research are to investigate the role of Artificial Intelligence (AI) in support the mobile learning and education and provide a taxonomy that shows the stages of process that used in this research and presents the main AI applications that used in mobile learning and education. To identify relevant studies, four reputable databases—ScienceDirect, Web of Science, IEEE Xplore, and Scopus—were systematically searched using predetermined inclusion/exclusion criteria. This screening process resulted in 50 studies which were further classified into groups: AI Technologies (19 studies), Machine Learning (11), Deep Learning (8), Chatbots/ChatGPT/WeChat (4), and Other (8). The results were analyzed taxonomically to provide a structured framework for understanding the diverse applications of AI in mobile learning and education. This review summarizes current research and organizes it into a taxonomy that reveals trends and techniques in using AI to support mobile learning, particularly for indigenous groups in Malaysia.

The growth of mobile Internet has facilitated access to information by minimizing geographical barriers. For this reason, this paper forecasts the number of users, incomes, and traffic for operators with the most significant penetration in the mobile internet market in Colombia to analyze their market growth. For the forecast, the convolutional neural network (CNN) technique is used, combined with the recurrent neural network (RNN), long short-term memory network (LSTM), and gated recurrent unit (GRU) techniques. The CNN training data corresponds to the last twelve years. The results currently show a high concentration in the market since a company has a large part of the market; however, the forecasts show a decrease in its users and revenues and the growth of part of the competition. It is also concluded that the technique with the most precision in the forecasts is CNN-GRU.

This research explores the role of digital economy in driving agricultural development in the BIMSTEC region, which includes Thailand, Myanmar, Sri Lanka, Nepal, India, Bangladesh and Bhutan (with Bhutan excluded due to data limitations) with a particular focus on mobile technologies, computing capacity and internet connectivity which were the most readily available data points for BIMSTEC. Using a combination of document analysis, and panel data analysis with the data covering 10 years (2012–2021), the study examines the interplay of key digital technologies with agricultural growth while controlling for factors including water usage, fertilizer consumption, and land temperature and agricultural land area. The analysis incorporates additional variables such as infrastructure development, credit to agriculture, investment in agricultural research, and education level. The findings reveal a strong positive correlation between mobile technology, Internet and computing capacity in BIMSTEC. This study underscores that digital tools are pivotal in enhancing agricultural productivity, yet their impact is significantly combined with investment in infrastructure and education. This study suggests that digital solutions, when strategically integrated with broader socio-economic factors can effectively challenges in developing countries, particularly in rural and underserved regions. This research contributes to the growing body of literature on digital economy in agriculture, highlighting how digital technologies can foster agricultural productivity in developing countries.