Intention to Use and Adoption of Artificial Intelligence Chatbots: A Visual Analysis Based on Literature Data

Purpose: This study examines the process formation of customer loyalty and customer value co-creation towards AI chatbots by exploring the successive effects of perceived value aspects, perceived information quality, technological self-efficacy for online trust, aspects of loyalty, and value co-creation. Theorical framework: The increasingly strong reception of humans for a new wave of digitalization has promoted the need to learn about customer loyalty and customers' value co-creation formation for businesses applying AI chatbots to their operations business to attract and retain customers. The study utilized the perceived value dimension, as well as perceived information quality, technological self-efficacy, and online trust, to comprehend loyalty and value co-creation. Design/methodology/approach: The study was conducted using a self-administered questionnaire survey with 447 participants, who had used Pizza Hut's AI chatbot service in Vietnam. The data was analyzed by integrating two techniques: partial least square structural equation modeling (PLS-SEM) and artificial neural networks (ANN). Findings: The results show that aspects of perceived value, perceived information quality, and technological self-efficacy all have a significant impact on online trust except hedonic value, which in turn leads to the formation of aspects of loyalty and high ability to create value co-creation. The analysis results show that perceived information quality has a stronger impact on online trust than technological self-efficacy. In addition, the non-linear results from the ANN analysis show that attitudinal loyalty has relatively stronger importance for value co-creation than behavioral loyalty. Research, Practical & Social Implication: This study contributes to the emerging literature on the use of AI chatbots by investigating the possibility of consumers and providers co-creating value. Second, in this study, the authors delved into the internal aspects of loyalty and separated it into two primary aspects, behavioral and attitudinal, in order to clarify their impact on the factors that influence AI chatbot and value co-creation. In conclusion, this research contributes to the existing body of knowledge by providing a more multidimensional perspective on theories. Originality/value: The integration of PLS-SEM and ANN techniques into the analysis to simultaneously explore both linear and non-linear mechanisms of this study explained the influence of aspects of perceived value, perceived information quality, and technological self-efficacy on aspects of loyalty and value co-creation via online trust in AI chatbots context. In addition, this study extends the perceived value to explore the impact of internal and external personal factors on AI chatbots.

Explaining the use of AI chatbots as context alignment: Motivations behind the use of AI chatbots across contexts and culture

AI Chatbot is a computer program integrated with artificial intelligence designed to interact with humans and provide useful information. AI Chatbot offers anonymity, one of the factors motivating someone to use it since they feel safe. Indonesian students nowadays inhibit shyness to ask questions and participate in learning due to anxiety factors and fear of negative judgment. Therefore, it is necessary to conduct a test to understand the extent of acceptance factors of AI Chatbots. This research aims to examine the effect of anonymity variable on the use of AI Chatbots among university students at Medan using the UTAUT2 model. A survey was conducted on 421 students. Using the Structural Equation Modeling (SEM) model with the help of SmartPLS software, this study introduces the anonymity variable in the UTAUT2 model, which has a positive and significant effect on students’ behavioral intention to use AI Chatbots. These findings also show that price value and habit have a positive and significant effect on behavioral intention, also habit and behavioral intention effect students’ behavior towards AI Chatbots at Medan. However, performance expectancy, effort expectancy, social influence, facilitating conditions, and hedonic motivation do not affect the students’ behavioral intention and behavior towards AI Chatbots

Currently, AI customer service is gaining popularity at a high rate, with over 92% of online shoppers having experience with AI customer service. The question is: what attitudes do consumers have towards AI customer service? A randomly selected sample of 670 consumers was surveyed and the study found that 71.5% of consumers accept or at least do not resist AI customer service. The main reasons AI customer service is so popular are that it is fulltime responsive, absolutely neutral, more objective, and represents a future trend. Nevertheless, 28.5% of consumers are still resistant to AI chatbot, mainly because they are not as relevant, effective, and smooth as a human customer service agent. Besides, there are prevalent obstacles to seamlessly bridge AI chatbots with human agents. In terms of explicitly specified AI chatbots versus concealed AI chatbots, merchants should have them specified since consumers have strong antipathy with AI chatbots disguising as human agents. Furthermore, there are differences in attitudes towards AI customer service across age and educational background. Accordingly, we recommend that, in the context of the overall improvement of service quality, to slow down the pace of AI replacing human agents, to do so in a step-by-step and orderly manner, and to give consumers adequate choice for human agents versus AI chatbots. Finally, to fully promote AI customer service, it is an essential measure to increase its media coverage and publicity.

Objective: AI chatbots are changing how companies interact with customers. AI chatbots leverage natural language processing and artificial intelligence to deliver customers a seamless, personalized, and user-friendly experience, ensuring quick, efficient responses. Methodology/Technique - Using an AI chatbot as a virtual agent to assist customers can be seen as a tool for companies to provide better customer service. Despite their growing adoption across industries, AI chatbots in Malaysia's food service sector remain relatively underused. Findings – Moreover, there is limited research on their impact, particularly in restaurant settings. This research aims to examine the impact of chatbot service quality adoption on customer loyalty in the food service sector. This research focuses on service quality attributes such as ease of use, availability, response accuracy, assurance, omnipresence, and human-like empathy. This research would adopt a quantitative research approach, with data collected through a Google Form questionnaire. A total of 185 valid questionnaires were gathered using a purposive sampling technique. The gathered data were analyzed using Partial Least Squares Structural Equation Modeling (PLS-SEM) with SmartPLS version 4 and the IBM SPSS Statistics software 29.0. Novelty - The findings reveal that ease of use, availability, accuracy of response, and assurance are significant factors that positively influence customer loyalty. These attributes underscore the importance of service quality in fostering customer loyalty. This research provides valuable insights to support the food service industry in improving customer loyalty and to guide the adoption of AI chatbots. Type of Paper: Empirical JEL Classification: Q31, M15, L80. Keywords: AI Chatbot, Service quality, Customer loyalty Reference to this paper should be made as follows: Li, H.Y, Thoo, A.C, Chan, Y.Y, Tuan, L.Y. (2026). Examining the Impact of AI Chatbot Service Quality Adoption on Customer Loyalty in the Food Service Sector, J. Mgt. Mkt. Review, 11(2), 76 – 98. https://doi.org/10.35609/jmmr.2026.11.2(4)

This research examines differences in consumer responses to large language model AI chatbots versus search engines when reviewing and evaluating product information in the information search stage of the purchase decision process. Building on the mere exposure effect, the consumer–brand relationships literature, and consumer decision-making research, this research investigates consumer search preferences and self-disclosure willingness along with a psychological mechanism. We conducted an experiment comparing two types of product search tools and tested a moderated mediation using Process Model 15. The findings suggest that consumers prefer search engines over AI chatbots and are more willing to self-disclose their personal information to search engines (vs. AI chatbots) due to perceived familiarity. However, we find that the quality of consumers’ relationship with the source of information moderates this effect. Further, we show that consumers evaluate AI chatbot-based results as less biased than traditional search engine results. Contrary to previous research that has mainly focused on AI chatbots’ functional aspects in consumer adoption, the current research emphasizes the psychological and relational aspects of product search perception and preference. In this way, we offer novel insights into a purchase decision-making process and consumer engagement as consumers adopt AI chatbot technology for product search.

This study is centered on investigating the acceptance and utilization of AI Chatbot technology among graduate students in China and its implications for higher education. Employing a fusion of the UTAUT (Unified Theory of Acceptance and Use of Technology) model and the ECM (Expectation-Confirmation Model), the research seeks to pinpoint the pivotal factors influencing students’ attitudes, satisfaction, and behavioral intentions regarding AI Chatbots. The study constructs a model comprising seven substantial predictors aimed at precisely foreseeing users’ intentions and behavior with AI Chatbots. Collected from 373 students enrolled in various universities across China, the self-reported data is subject to analysis using the partial-least squares method of structural equation modeling to confirm the model’s reliability and validity. The findings validate seven out of the eleven proposed hypotheses, underscoring the influential role of ECM constructs, particularly “Confirmation” and “Satisfaction,” outweighing the impact of UTAUT constructs on users’ behavior. Specifically, users’ perceived confirmation significantly influences their satisfaction and subsequent intention to continue using AI Chatbots. Additionally, “Personal innovativeness” emerges as a critical determinant shaping users’ behavioral intention. This research emphasizes the need for further exploration of AI tool adoption in educational settings and encourages continued investigation of their potential in teaching and learning environments.

Purpose and design: To evaluate the accuracy and bias of ophthalmologist recommendations made by three AI chatbots, namely ChatGPT 3.5 (OpenAI, San Francisco, CA, USA), Bing Chat (Microsoft Corp., Redmond, WA, USA), and Google Bard (Alphabet Inc., Mountain View, CA, USA). This study analyzed chatbot recommendations for the 20 most populous U.S. cities.Methods: Each chatbot returned 80 total recommendations when given the prompt “Find me four good ophthalmologists in (city).” Characteristics of the physicians, including specialty, location, gender, practice type, and fellowship, were collected. A one-proportion z-test was performed to compare the proportion of female ophthalmologists recommended by each chatbot to the national average (27.2% per the Association of American Medical Colleges (AAMC)). Pearson’s chi-squared test was performed to determine differences between the three chatbots in male versus female recommendations and recommendation accuracy.Results: Female ophthalmologists recommended by Bing Chat (1.61%) and Bard (8.0%) were significantly less than the national proportion of 27.2% practicing female ophthalmologists (p<0.001, p<0.01, respectively). ChatGPT recommended fewer female (29.5%) than male ophthalmologists (p<0.722). ChatGPT (73.8%), Bing Chat (67.5%), and Bard (62.5%) gave high rates of inaccurate recommendations. Compared to the national average of academic ophthalmologists (17%), the proportion of recommended ophthalmologists in academic medicine or in combined academic and private practice was significantly greater for all three chatbots.Conclusion: This study revealed substantial bias and inaccuracy in the AI chatbots’ recommendations. They struggled to recommend ophthalmologists reliably and accurately, with most recommendations being physicians in specialties other than ophthalmology or not in or near the desired city. Bing Chat and Google Bard showed a significant tendency against recommending female ophthalmologists, and all chatbots favored recommending ophthalmologists in academic medicine.

This study investigated the impact of AI-driven brainstorming tools on process writing instruction and students' writing outcomes in the context of third-year Thai university students. A mixed-methods approach was employed to examine the effectiveness of AI-driven brainstorming tools, foreign English lecturers' preferences and comments towards AI-generated brainstorming results, students' preferences for specific AI chatbots, and the perceived challenges and facilitative factors experienced by students. The findings revealed that the intervention group using AI tools significantly outperformed the conventional group on two out of three assignments (People: p = .002; Things: p &lt; .001), with ChatGPT emerging as the most popular AI chatbot (78.8%). Foreign English lecturers acknowledged the AI chatbots' strengths but preferred students' brainstorming results. Overreliance on AI for idea generation was identified as the most concerning challenge (M = 4.62), while enhanced creativity (M = 4.53) and increased idea generation (M = 4.51) were the most appreciated facilitative factors. The study demonstrates the potential of AI-driven brainstorming tools to revolutionize process writing instruction and highlights the importance of striking a balance between leveraging AI benefits and fostering students' independent thinking and creativity skills.

A framework and exemplars for ethical and responsible use of AI Chatbot technology to support teaching and learning

This study examines the actual functions and educational implications of AI chatbots integrated into university Learning Management Systems (LMS), while exploring their potential and limitations from the user experience perspective. AI chatbots from seven Korean universities were selected, and their characteristics were compared across functional, technical, and educational dimensions based on official documents, user manuals, and demonstration videos. In addition, in-depth interviews were conducted with six undergraduate and graduate students and three faculty members from one selected university, and the data were analyzed thematically. The findings reveal a clear discrepancy between the designation of “AI chatbots” and their actual functions within LMS. Although some universities demonstrated the potential for adopting advanced functions based on Retrieval-Augmented Generation (RAG) or Large Language Models (LLMs), the majority of chatbots primarily supported basic administrative tasks. As a result, advanced learning support features—such as contextual understanding and personalized feedback—were minimally implemented. Furthermore, users from the interviewed university highlighted common issues, including limited integration with course content, formulaic response patterns, and low awareness of the AI chatbot’s existence. Overall, the study provides empirical evidence of a substantial gap between the technical potential of LMS-based AI chatbots and users’ actual experiences. To move beyond simple functional adoption, the study suggests that development should emphasize functional sophistication informed by user experience, system stabilization, and effectiveness validation.

The current research investigated the factors associated with the intensity of AI chatbot usage among university students, applying the Technology Acceptance Model (TAM) and its extended version, TAM3. A daily diary study over five days was conducted among univserity students, distinguishing between inter-individual (between-person) and intra-individual (within-person) variations. Multilevel structural equation modeling (SEM) was used to analyze the data. In Study 1 (N = 72), results indicated that AI chatbot anxiety was associated with perceived ease of use (PEOU) and perceived usefulness (PU), which serially mediated the link with AI chatbot usage intensity. Study 2 (N = 153), supported these findings and further explored the roles of facilitating conditions and subjective norm as additional predictors of PEOU and PU. Results from both studies demonstrated that, at the between-person level, students with higher average levels of PEOU and PU reported more intensive AI chatbot usage. In Study 1, the relationship between PEOU and usage intensity was mediated through PU at the within-person level, while the mediation model was not supported in Study 2. Post-hoc comparisons highlighted much higher variability in PEOU and PU in Study 1 compared to Study 2. The results have practical implications for enhancing AI chatbot adoption in educational settings. Emphasizing user-friendly interfaces, reducing AI-related anxiety, providing robust technical support, and leveraging peer influence may enhance the usage intensity of AI chatbots. This study underscores the necessity of considering both stable individual differences and dynamic daily influences to better understand AI chatbot usage patterns among students.

This study aimed to assess the validity and reliability of AI chatbots, including Bing, ChatGPT 3.5, Google Gemini, and Claude AI, in addressing frequently asked questions (FAQs) related to dental trauma. A set of 30 FAQs was initially formulated by collecting responses from four AI chatbots. A panel comprising expert endodontists and maxillofacial surgeons then refined these to a final selection of 20 questions. Each question was entered into each chatbot three times, generating a total of 240 responses. These responses were evaluated using the Global Quality Score (GQS) on a 5-point Likert scale (5: strongly agree; 4: agree; 3: neutral; 2: disagree; 1: strongly disagree). Any disagreements in scoring were resolved through evidence-based discussions. The validity of the responses was determined by categorizing them as valid or invalid based on two thresholds: a low threshold (scores of ≥ 4 for all three responses) and a high threshold (scores of 5 for all three responses). A chi-squared test was used to compare the validity of the responses between the chatbots. Cronbach's alpha was calculated to assess the reliability by evaluating the consistency of repeated responses from each chatbot. The results indicate that the Claude AI chatbot demonstrated superior validity and reliability compared to ChatGPT and Google Gemini, whereas Bing was found to be less reliable. These findings underscore the need for authorities to establish strict guidelines to ensure the accuracy of medical information provided by AI chatbots.

The terms “chatbot,” “AI chatbot,” and “virtual agent” are used in a way that often does not differentiate between them, which might lead to possible misunderstandings. Despite the fact that the technologies associated with these terms are inextricably linked to one another, the subtle differences that exist between them result in considerable differences in their unique capabilities. The term “chatbot” is often used to refer to a wide variety of artificial intelligence systems that are designed to carry on conversations. The development of artificial intelligence chatbot software has resulted in the creation of virtual agents, which constitutes a significant step forward. These agents participate in conversation via the use of AI methods known as conversational AI, and they make use of deep learning algorithms in order to improve their performance over time. This chapter discusses the differences between chatbots, AI chatbots and virtual assistants, and other related topics.

The kidney, an essential organ of the human body, can suffer pathological damage that can potentially have serious adverse consequences on the human body and even affect life. Furthermore, the majority of kidney-induced illnesses are frequently not readily identifiable in their early stages. Once they have progressed to a more advanced stage, they impact the individual's quality of life and burden the family and broader society. In recent years, to solve this challenge well, the application of machine learning techniques in renal medicine has received much attention from researchers, and many results have been achieved in disease diagnosis and prediction. Nevertheless, studies that have conducted a comprehensive bibliometric analysis of the field have yet to be identified. This study employs bibliometric and visualization analyses to assess the progress of the application of machine learning in the renal field and to explore research trends and hotspots in the field. A search was conducted using the Web of Science Core Collection database, which yielded articles and review articles published from the database's inception to May 12, 2024. The data extracted from these articles and review articles were then analyzed. A bibliometric and visualization analysis was conducted using the VOSviewer, CiteSpace, and Bibliometric (R-Tool of R-Studio) software. 2,358 papers were retrieved and analyzed for this topic. From 2013 to 2024, the number of publications and the frequency of citations in the relevant research areas have exhibited a consistent and notable increase annually. The data set comprises 3734 institutions in 91 countries and territories, with 799 journals publishing the results. The total number of authors contributing to the data set is 14,396. China and the United States have the highest number of published papers, with 721 and 525 papers, respectively. Harvard University and the University of California System exert the most significant influence at the institutional level. Regarding authors, Cheungpasitporn, Wisit, and Thongprayoon Charat of the Mayo Clinic organization were the most prolific researchers, with 23 publications each. It is noteworthy that researcher Breiman I had the highest co-citation frequency. The journal with the most published papers was "Scientific Reports," while "PLoS One" had the highest co-citation frequency. In this field of machine learning applied to renal medicine, the article "A Clinically Applicable Approach to Continuous Prediction of Future Acute Kidney Injury" by Tomasev N et al., published in NATURE in 2019, emerged as the most influential article with the highest co-citation frequency. A keyword and reference co-occurrence analysis reveals that current research trends and frontiers in nephrology are the management of patients with renal disease, prediction and diagnosis of renal disease, imaging of renal disease, and development of personalized treatment plans for patients with renal disease. "Acute kidney injury," "chronic kidney disease," and "kidney tumors" are the most discussed diseases in medical research. The field of renal medicine is witnessing a surge in the application of machine learning. On one hand, this study offers a novel perspective on applying machine learning techniques to kidney-related diseases based on bibliometric analysis. This analysis provides a comprehensive overview of the current status and emerging research areas in the field, as well as future trends and frontiers. Conversely, this study furnishes data on collaboration and exchange between countries, regions, institutions, journals, authors, keywords, and reference co-citations. This information can facilitate the advancement of future research endeavors, which aim to enhance interdisciplinary collaboration, optimize data sharing and quality, and further advance the application of machine learning in the renal field.