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This study aims to propose an architecture and presents the implementation of a unified chatbot that faces the challenges of heterogeneous communication channels. This approach enables the interaction with the chatbot to be carried out over multiple communication media on a single platform.

The chatbot was embedded in a unified communications framework. Furthermore, it has been developed and tested using the information and communications technology (ICT)Core platform. Three test scenarios have been considered in the context of a digital marketing company, which include the use of multiple channels such as text, audio and e-mail. Usability and empirical tests were performed to collect both qualitative and quantitative data.

The results indicate that the proposed model improves the completion rate and enables the chatbot to interact with the customer by capturing information over multiple channels. The findings also reveal that digital marketing organizations can use a unified chatbot in their marketing campaigns, which contributes to improving the quality of customer interaction, message personalization and continuous learning throughout the process.

While the use of a chatbot is a relatively common practice among companies, its integration into unified communications networks is an emerging topic. Proposals for integration into a unified communication channel have mainly focused on access to the same account and conversations from multiple devices or access platforms. This approach, while useful, does not allow for the integration of information from multiple sources. Alternatively, an integrated architecture is suggested in which a chatbot obtains knowledge from multiple sources and uses it to increase the quality of communication with the customer.

Chatbots are increasingly embodied in business and IS contexts to enhance customer and user experience. Despite wide interest in chatbots among business and IS academics, surprisingly, there are no current comprehensive reviews to reveal the knowledge structure of chatbot research in such areas.

This study employed a mixed-method approach that combines systematic review and bibliometric analysis to provide a comprehensive synthesis of chatbot research. The sample was obtained in December 2023 after searching across six databases: EBSCOhost, PsycINFO, Web of Science, Scopus, ACM Digital Library and IEEE Computer Society Digital Library.

This study reveals the major trend in publication trends, countries, article performance and cluster distribution of chatbot research. We also identify the key themes of chatbot research, which mainly focus on how users interact with chatbots and their consequences, such as users’ cognition and behavior. Moreover, several important research agendas have been discussed to address some limitations in the current chatbot research in business and IS fields.

The present review is one of the first attempts to systematically reveal the ongoing knowledge map of chatbots in business and IS fields, which makes important contributions and provides useful resources for future chatbot research and practice.

This study aims to present a great deal of interest in researching plant-based phytopharmaceuticals and nutraceuticals as a possible alternative to synthetic medication, both to avoid their side effects and for financial reasons.

Mankind has used medicinal plants since the beginning of civilization. Nature has been explored as a source of therapeutic chemicals for thousands of years, and many modern drugs have been discovered from natural sources. The primary medical care system of resource-poor areas in India has continued to rely on traditional medicine as the most accessible and reasonably priced form of treatment.

Tinospora cordifolia is a plant that is frequently used in Ayurvedic and traditional medicine throughout India. Although almost all of its parts are used in conventional medical systems, the leaves, stems and roots are the most significant ones used medicinally. All forms of existence can benefit from the versatility of T. cordifolia. It includes a wide variety of compounds that impact the body.

The goal of this review is to provide a concise summary of the knowledge about the pharmacological, phytochemistry, botanical, ethnopharmacology, toxicity study, marketed products and patents of the T. cordifolia plant.

The aim of the study is to understand the transformative impact of ChatGPT on artificial intelligence (AI) research, its applications, implications, challenges and potential to shape future AI trends. The study also seeks to assess the relevance and quality of research output through citation and bibliographic coupling analysis.

This study employed a comprehensive bibliometric analysis using Biblioshiny and VOSviewer to investigate the research trends, influential entities and leading contributors in the domain of AI, focusing on the ChatGPT model.

The analysis revealed a high prevalence of AI-related terms, indicating a significant interest in and engagement with ChatGPT in AI studies and applications. “Nature” and “Thorp H.H.” emerged as the most cited source and author, respectively, while the USA surfaced as the leading contributor in the field.

While the findings provide a comprehensive overview of the ChatGPT research landscape, it is important to note that the conclusions drawn are only as current as the data used.

The study highlights potential collaboration opportunities and signals areas of research that might benefit from increased focus or diversification. It serves as a valuable resource for researchers, practitioners and policymakers for strategic planning and decision-making in AI research, specifically in relation to ChatGPT.

This study is one of the first to provide a comprehensive bibliometric analysis of the ChatGPT research domain, its multidimensional impact and potential. It offers valuable insights for a range of stakeholders in understanding the current landscape and future directions of ChatGPT in AI.

This paper aims to explore the intersection of artificial intelligence (AI) and marketing within the context of knowledge management (KM). It investigates how AI technologies facilitate data-driven decision-making, enhance business communication, improve customer personalization, optimize marketing campaigns and boost overall marketing effectiveness.

This study uses a quantitative and systematic approach, integrating citation analysis, text mining and co-citation analysis to examine foundational research areas and the evolution of AI in marketing. This comprehensive analysis addresses the current gap in empirical investigations of AI’s influence on marketing and its future developments.

This study identifies three main perspectives that have shaped the foundation of AI in marketing: proxy, tool and ensemble views. It develops a managerially relevant conceptual framework that outlines future research directions and expands the boundaries of AI and marketing literature within the KM landscape.

This research proposes a conceptual model that integrates AI and marketing within the KM context, offering new research trajectories. This study provides a holistic view of how AI can enhance knowledge sharing, strategic planning and decision-making in marketing.