In the 21st century, brand communication has been significantly transformed through the interaction of users and artificial intelligence (AI), who co-create and recreate texts in digital environments. This evolution challenges traditional disciplines and roles, opening new perspectives for textual production on multiple platforms. The study examines the current state and application of the textual component in brand communication, exploring its disciplinary foundations, rhetorical traces, and research methodologies. To this end, a content analysis of 97 relevant publications from 2000 to 2024 was conducted, selected for their impact on the field of brand communication and following the guidelines established in the PRISMA statement. The results identified three sources of textual creation: Organization, users and algorithms. In addition, persuasion and sentiment take precedence at the rhetorical level, while data mining stands out in message analysis. In conclusion, the advertising text, which previously prevailed in brand communication with corporate authorship, formal prefiguration and a closed entity, now expands in a media and networked context. This text originates from a multiplicity of human and automated sources, overlapping rhetorical phases and fluid textualities. The shift implies a transition from unidirectional communication, characterized by repeated impacts, to multidirectional communication with spiraling trajectories and iterative adjustments. This challenges the boundaries of genres and formats, merging the persuasiveness of rhetoric and the imagination of storytelling. This situation demands commercial policies that integrate new professionals and roles, in partnership with the educational sector, and that address copyright with AI and users.

The article highlights Malaysia’s multicultural history, the advancement of Internet technology, and the worldwide appeal of Chinese food, all of which serve as a good basis for the project. This study focuses on Malaysian Chinese takeout systems. The research’s primary goals include developing new business options for the Chinese food sector, as well as enhancing customer happiness and efficiency of takeout systems. As a result, the project intended to create a Web-based system for managing several tasks associated with meal ordering by users. For the system development, an Object-Oriented System Development (OOSD) methodology was used, mostly with the Java programming language. Model-View-Control (MVC) framework was employed throughout development to improve system administration. Redis and HTTP session technologies were included for user login to increase system security. For database operations, MyBatis and MyBatis Plus were also employed to enhance ease and security. The system adheres to design principles and leverages technologies like ElementUI and jQuery to further fulfill this criterion to provide a user-friendly interface. The results of this study demonstrate significant improvements in the overall efficiency of the takeout process, leading to enhanced user experiences and greater customer satisfaction. In addition to streamlining operations, the system opens new avenues for the Malaysian Chinese food industry to capitalize on the growing demand for online food ordering. This research provides a solid foundation for future innovations in takeout systems and serves as a reference point for enhancing the Chinese gastronomy sector in a rapidly digitizing world.

Credit risk assessment is one of the most important aspects of financial decision-making processes. This study presents a systematic review of the literature on the application of Artificial Intelligence (AI) and Machine Learning (ML) techniques in credit risk assessment, offering insights into methodologies, outcomes, and prevalent analysis techniques. Covering studies from diverse regions and countries, the review focuses on AI/ML-based credit risk assessment from consumer and corporate perspectives. Employing the PRISMA framework, Antecedents, Decisions, and Outcomes (ADO) framework and stringent inclusion criteria, the review analyses geographic focus, methodologies, results, and analytical techniques. It examines a wide array of datasets and approaches, from traditional statistical methods to advanced AI/ML and deep learning techniques, emphasizing their impact on improving lending practices and ensuring fairness for borrowers. The discussion section critically evaluates the contributions and limitations of existing research papers, providing novel insights and comprehensive coverage. This review highlights the international scope of research in this field, with contributions from various countries providing diverse perspectives. This systematic review enhances understanding of the evolving landscape of credit risk assessment and offers valuable insights into the application, challenges, and opportunities of AI and ML in this critical financial domain. By comparing findings with existing survey papers, this review identifies novel insights and contributions, making it a valuable resource for researchers, practitioners, and policymakers in the financial industry.