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Purpose This study aims to investigate the customers’ behavioral intention and actual usage (AUE) of artificial intelligence (AI)-powered chatbots for hospitality and tourism in India by extending the technology adoption model (TAM) with context-specific variables. Design/methodology/approach To understand the customers’ behavioral intention and AUE of AI-powered chatbots for tourism, the mixed-method design was used whereby qualitative and quantitative techniques were combined. A total of 36 senior managers and executives from the travel agencies were interviewed and the analysis of interview data was done using NVivo 8.0 software. A total of 1,480 customers were surveyed and the partial least squares structural equation modeling technique was used for data analysis. Findings As per the results, the predictors of chatbot adoption intention (AIN) are perceived ease of use, perceived usefulness, perceived trust (PTR), perceived intelligence (PNT) and anthropomorphism (ANM). Technological anxiety (TXN) does not influence the chatbot AIN. Stickiness to traditional human travel agents negatively moderates the relation of AIN and AUE of chatbots in tourism and provides deeper insights into manager’s commitment to providing travel planning services using AI-based chatbots. Practical implications This research presents unique practical insights to the practitioners, managers and executives in the tourism industry, system designers and developers of AI-based chatbot technologies to understand the antecedents of chatbot adoption by travelers. TXN is a vital concern for the customers; so, designers and developers should ensure that chatbots are easily accessible, have a user-friendly interface, be more human-like and communicate in various native languages with the customers. Originality/value This study contributes theoretically by extending the TAM to provide better explanatory power with human–robot interaction context-specific constructs – PTR, PNT, ANM and TXN – to examine the customers’ chatbot AIN. This is the first step in the direction to empirically test and validate a theoretical model for chatbots’ adoption and usage, which is a disruptive technology in the hospitality and tourism sector in an emerging economy such as India.

As technology continues to advance, the integration of generative artificial intelligence tools in various sectors, including education, has gained momentum. ChatGPT, an extensively recognized language model created by OpenAI, has gained significant importance, particularly in education. This study investigates the awareness, acceptance, and adoption of ChatGPT, a state-of-the-art language model developed by OpenAI, in higher education institutions across China. This study applies the partial least squares structural equation modeling (PLS-SEM) method for examining data collected from 320 Chinese university students. The study’s conceptual framework integrates key determinants from the Technology Acceptance Model (TAM) and extends it by incorporating perceived intelligence as a critical factor in the adoption process. The study findings reveal that ChatGPT awareness significantly influences the intention to adopt ChatGPT. Perceived ease of use, usefulness, and intelligence significantly mediate the association between ChatGPT awareness and adoption intention of ChatGPT. Additionally, perceived trust significantly moderates the relationship between ChatGPT awareness and perceived ease of use, usefulness, and intelligence. Moving forward, in order to maintain students’ critical thinking skills and inventiveness in their assessment writing, assessments must promote the safe use of ChatGPT. Therefore, educators will be crucial in ensuring that artificial intelligence tools are used in assessments ethically and suitably by providing clear guidelines and instructions.

This study explored the effect of perceived usefulness, perceived ease of use, social influence, facilitating condition, lifestyle compatibility, and perceived trust on both the intention to use an e-wallet and the adoption of an e-wallet among adults by using the unified theory of acceptance and use of technology (UTAUT). This quantitative study adopted a cross-sectional research design to gather data from 501 respondents using a Google Form. The collected data were analyzed using partial least square structural equation modelling (PLS-SEM). As a result, perceived usefulness, perceived ease of use, social influence, lifestyle compatibility, and perceived trust displayed a significant positive effect on both intentions to use an e-wallet and adoption of an e-wallet. This study evidenced the mediating effect of the intention to use an e-wallet on the correlations between the predictors and adoption of an e-wallet. Both the age and gender of the respondents moderated the effect of lifestyle compatibility on intention to use an e-wallet. The study outcomes serve to inform managers and policy makers to devise effective strategies that capture consumers’ intention to use and experience of using an e-wallet in the midst of a turbulent market. Ultimately, such carefully sculpted policies may promote the digital platform and web-based application, apart from encouraging higher rates of e-wallet adoption in developing countries.

The use of artificial intelligence (AI) in the healthcare field is gaining popularity. However, it also raises some concerns related to privacy and ethical aspects that require the development of a responsible AI framework. The principle of responsible AI states that artificial intelligence-based systems should be considered a part of composite societal and technological systems. This study attempts to establish whether AI risks in digital healthcare are positively associated with responsible AI. The moderating effect of perceived trust and perceived privacy risks is also examined. The theoretical model was based on perceived risk theory. Perceived risk theory is important in the context of this study, as risks related to uneasiness and uncertainty can be expected in the development of responsible AI due to the volatile nature of intelligent applications. Our research provides some interesting findings which are presented in the discussion section.

The context of education has changed due to revolutionary developments in the information communication technology (ICT) industry in the post-COVID era. Innovative learning methods were introduced in the education sector to promote quality education. The students find it more convenient to use ICT tools to integrate their knowledge-seeking. China has recently paid more attention to developing and adopting electronic infrastructure. The study assesses the effect of technology self-efficacy (TSE) on ICT acceptance and implementation in China’s education sector. It also analyzed the role of perceived trust, perceived security, and electronic word of mouth (eWOM) in integrating digital information sharing and interaction tools. Data is collected from 382 business students at Chinese universities. The results revealed that perceived trust mediates the relationship between TSE and the actual use of ICT tools, intention to use ICT tools for information, and intention to use ICT tools for interaction. Further, perceived security and eWOM significantly moderate the relationship between TSE and perceived trust. The findings indicate that it is essential to offer assistance and instruction to students in the educational sector so they can use ICT technology more frequently. It is also crucial for organizations to establish a supportive culture and provide the necessary technological resources to facilitate the use of ICT.

PurposeThis study aims to investigates customer satisfaction from the use of bank chatbots and the effect of perceived trust in chatbots and banks' reputation on customer satisfaction.Design/methodology/approachA survey is conducted in Turkey involving 240 customers who experienced banking transactions using a chatbot. Partial least squares structural equation modeling (PLS-SEM) is used to investigate the relationships between the variables. The data were analyzed using SPSS 21 and SmartPLS programs.FindingsPerceived performance, perceived trust and corporate reputation significantly affect customer satisfaction with chatbot use. Customer expectations and confirmation of customer expectations have no direct impact on customer satisfaction, but customer expectations positively affect perceived performance. Customer expectations exert an indirect influence on customer satisfaction through perceived performance. Perceived performance has a positive impact on the confirmation of customer expectations, but customer expectations do not significantly impact the confirmation of customer expectations.Research limitations/implicationsThis study relies on a limited number of participants. Moreover, its sample is not representative of the target population due to the convenience sampling technique. Even if the results may not be generalized to the entire population of Turkey, they reflect the reality of emerging markets with relatively high technology sensitivity and a young population.Practical implicationsThe results provide new insights regarding banking service delivery channels, which may be of interest to professionals, academics, banks' top management, product development teams, design teams and customer satisfaction units.Social implicationsThis study is believed to help the community make their lives easier by providing them with knowledge and awareness about chatbots.Originality/valueThis study extends expectations confirmation theory's predictions to chatbot use in banking.

The emergence of high-speed internet (5G) services and the demonetization of the Indian currency by the Government of India in the year 2016 served as catalysts for the growth of banking services, such as internet/mobile banking. The main objective of the study was to investigate the role of perceived financial cost, perceived risk, and trust in the adoption of mobile banking services by users. The study extends the Unified Theory of Acceptance and Use of Technology (UTAUT) to explain the adoption of mobile banking services by young Indian customers. The data from 253 users of age between 18–30 years were collected through a survey questionnaire and were analyzed using structural equation modeling with Amos 22.0. The results revealed that performance expectancy, effort expectancy, social influence, and perceived financial cost exhibited a significant positive influence on behavioral intentions. However, the facilitating conditions were found to exert no effect on actual use. In addition, the results demonstrate that both perceived risk and perceived trust moderate the relationship between behavioral intention and the actual use of mobile banking. The results of the study provide some novel insights into how perceived risk and perceived trust shape the interplay between behavioral intention and the actual use of mobile banking services. The inferences drawn from the study may enhance the understanding of the transformation of behavioral intention into the adoption and actual use of mobile banking services.

This research delves into the multifaceted landscape of various factors that influence the adoption of Generation-Artificial Intelligence (Gen-AI) in Higher Education. By employing a comprehensive framework that includes perceived risk, perceived ease of use, usefulness, Technological Pedagogical Content Knowledge (TPACK), and trust, the study aims to reveal the crucial roles that these factors play in shaping the intention to embrace Gen-AI. Through the utilization of a hybrid approach that combines Structural Equation Modeling (SEM) and Artificial Neural Networks (ANN), the research examines the complex relationships among these predictors and their overall impact on the adoption of Gen-AI. By examining a diverse cohort of 242 participants, including undergraduate and postgraduate students, as well as faculty members from Indian Higher Education Institutions, the research uncovers significant determinants such as perceived ease of use, usefulness, TPACK, and trust. Interestingly, perceived usefulness does not emerge as a significant factor in influencing the intention to adopt AI in Higher Education. Through demographic analysis using the SPSS statistical tool package, the study reveals non-compensatory and nonlinear relationships between age, gender, and the intention to use AI. By leveraging the significant predictors identified through SEM, the developed ANN model accurately predicts the intention to use AI with a 71% accuracy rate. This study not only provides valuable insights into the theoretical foundations but also offers practical implications for the seamless integration of Gen-AI in higher education.

As an emerging healthcare technology, artificial intelligence (AI) health assistants have garnered significant attention. However, the acceptance and intention of ordinary users to adopt AI health assistants require further exploration. This study aims to identify factors influencing users’ intentions to use AI health assistants and enhance understanding of the acceptance mechanisms for this technology. Based on the unified theory of acceptance and use of technology (UTAUT), we expanded the variables of perceived trust (PT) and perceived risk (PR). We recruited 373 Chinese ordinary users online and analyzed the data using covariance-based structural equation modeling (CB-SEM). The results indicate that the original UTAUT structure is robust, performance expectancy (PE), effort expectancy (EE), and social influence (SI) significantly positively affect behavioral intention (BI), while facilitating conditions (FC) do not show a significant impact. Additionally, perceived trust is closely related to performance expectancy, effort expectancy, and behavioral intention, negatively impacting perceived risk. Conversely, perceived risk adversely affects behavioral intention. Our findings provide valuable practical insights for developers and operators of AI health assistants.