Digital mjournal3/14/2023 ![]() Both models identified the quality of services offered by m-commerce, satisfaction, and trust as determining factors for increasing consumer loyalty in virtual commerce. The research results confirm the hypotheses presented in this study. The analysis was performed with SPSS Statistics and Amos software, using a hybrid approach: Structural Equation Modeling (SEM) and Artificial Neural Network (ANN). The data were collected through a survey addressed to customers who, during 2021–2022, made at least one purchase through m-commerce. ![]() The research purpose is to contribute to the understanding of the COVID-19 pandemic impact on the intensification of commercial transactions on the mobile channel (m-commerce) and to identify the most significant factors that act on consumer behavior based on the development of a conceptual model to establish the influence of m-commerce service quality on customer satisfaction and loyalty. Experimental results show that our approach has obvious advantages in accuracy compared with the existing mainstream service discovery approaches. We embed service-related information into the knowledge graph, alleviating the impact of data sparsity and mining deep relationships between services, which improves the accuracy of service discovery. To solve these problems in current service discovery approaches, we propose a service discovery approach based on a knowledge map (SDKG). For existing methods, there is a problem of data sparsity, because one mashup is related to a few APIs, and another problem of over-reliance on semantic information. It is unrealistic for mashup developers to manually select appropriate APIs from a large number of services. However, with the rapid development of Internet technology, the number of Web APIs is increasing rapidly. For mashup application development, the quality of the selected APIs is particularly important. Mashup is a new type of application that integrates multiple Web APIs.
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