Purpose: Despite the fact that chatbots have been largely adopted for the last few years, a comprehensive literature review research focusing on the intention of individuals to adopt chatbots is rather scarce. In this respect, the present paper attempts a literature review investigation of empirical studies focused on the specific issue in nine scientific databases during 2017-2021. Specifically, it aims to classify extant empirical studies which focus on the context of individuals' adoption intention toward chatbots. Design/methodology/approach: The research is based on PRISMA methodology, which revealed a total of 39 empirical studies examining users' intention to adopt and utilize chatbots. Findings: After a thorough investigation, distinct categorization criteria emerged, such as research field, applied theoretical models, research types, methods and statistical measures, factors affecting intention to adopt and further use chatbots, the countries/continents where these surveys took place as well as relevant research citations and year of publication. In addition, the paper highlights research gaps in the examined issue and proposes future research directions in such a promising information technology solution. Originality/value: As far as the authors are concerned, there has not been any other comprehensive literature review research to focus on examining previous empirical studies of users' intentions to adopt and use chatbots on the aforementioned period. According to the authors' knowledge, the present paper is the first attempt in the field which demonstrates broad literature review data of relevant empirical studies.

Artificial intelligence agents (chatbots), which are programs to communicate with users primarily in customer service contexts, are an alternative interaction channel supporting businesses in the digital environment and vital components in customer service. The present empirical paper, which is aimed at identifying and discussing the factors motivating nonusers to adopt the specific technology in mobile contexts, proposes a comprehensive conceptual model, which combines the UTAUT 2 behavioral theory with variables of mobile service quality contexts, such as information quality, privacy concerns, interface, and equipment, as well as trust and mobility factors. Data analysis, based on the partial least squares structural equation modeling (PLS-SEM) statistical method, revealed that performance expectancy, facilitating factors, hedonic motivation, mobility, trust, and service quality positively affect nonusers’ behavioral intention to adopt chatbots. In addition, equipment, interface, and trust have a significant impact on users’ trust in the context of mobile chatbots. Personal data privacy issues also have a negative effect on trust, in contrast to effort expectancy, which positively affects performance expectancy. As mobile service quality factors have not been investigated before in the context of chatbots, the findings of the present research are expected to provide useful insights both to academia and the business industry.

As Artificial Intelligence (AI) becomes increasingly integrated into financial services, its alignment with sustainability goals has given rise to a new domain: Green FinTech. This study investigates the Behavioural Intention (BI) of Greek banking customers to adopt AI chatbots in the context of sustainable digital finance. Building upon the Unified Theory of Acceptance and Use of Technology (UTAUT), the proposed model incorporates additional constructs, i.e., Trust, Digital AI Literacy (DAIL), Environmental Concern (ENC), and Consumer Social Responsibility (CnSR), to examine the behavioural intention (BI) to use AI chatbots in the context of sustainable digital finance. Unlike prior UTAUT-based research, which has mainly examined AI, FinTech, or chatbot adoption separately or in different contexts, the present study develops and empirically tests an extended green-oriented UTAUT model that integrates technological, environmental, and ethical dimensions within a single framework. In this way, the study addresses a geographical, contextual, and model-specific gap in the literature, as research on AI chatbot adoption in Green FinTech remains limited, particularly in the Greek banking context. The target population for this study consists of educated, working-age adults who have already used an AI chatbot for a banking transaction in the context of e-banking services. A structured questionnaire was administered to a sample of 209 users of AI chatbots in the banking context. Using Structural Equation Modelling (SEM) and factor analysis via Principal Component Analysis (PCA) in conjunction with orthogonal rotation (VARIMAX), the results show that Green Performance Expectancy (GPE), Green Effort Expectancy (GEE), Digital AI Literacy (DAIL), and Trust significantly influence Behavioural Intention (BI). Consumer Social Responsibility (CnSR) also has an indirect impact via Green Social Influence (GSI). The study extends UTAUT in the Green FinTech context by integrating sustainability- and AI chatbot usage-related constructs, showing that Green Performance Expectancy and trust are the strongest drivers of bank customers’ behavioural intention to use AI chatbots. The study therefore contributes theoretically by extending UTAUT into a green-oriented framework that captures sustainability-related and ethical drivers of AI chatbot adoption in banking, rather than examining technology-use determinants alone. More specifically, it explains AI chatbot adoption in Green FinTech through a unified framework that combines core UTAUT variables with Trust, Digital AI Literacy, Environmental Concern, and Consumer Social Responsibility in the underexplored context of Greek banking.