Developments in artificial intelligence (AI) have led to the emergence of new technologies offering unique business opportunities. This article examines the factors influencing AI-based chatbot implementation by small and medium enterprises (SMEs). We grounded the article's conceptual model in the technology–organization–environment (TOE) framework. Employing a quantitative research methodology, we collected data from 292 SME respondents via an online survey. We then utilized covariance-based structural equation modeling to analyze the data. The empirical results reveal that perceived employee capability, perceived availability of financial support, perceived top management support, perceived cost, perceived complexity, and perceived relative advantage are positively associated with SMEs' AI-based chatbot adoption intention. This article, thus, contributes to the scarce literature on the adoption of AI-based chatbots for SMEs in developing small island countries. The findings provide meaningful insights to developers, marketers, and SMEs to enhance firms’ performance and competitiveness by increasing the adoption of AI-based chatbots.

With the outbreak of COVID-19, contact tracing is becoming a used intervention to control the spread of this highly infectious disease. This article explores an individual's intention to adopt COVID-19 digital contact tracing (DCT) apps. A conceptual framework developed for this article combines the procedural fairness theory, dual calculus theory, protection motivation theory, theory of planned behavior, and Hofstede's cultural dimension theory. The study adopts a quantitative approach collecting data from 714 respondents using a random sampling technique. The proposed model is tested using structural equation modeling. Empirical results found that the perceived effectiveness of privacy policy negatively influenced privacy concerns, whereas perceived vulnerability had a positive influence. Expected personal and community-related outcomes of sharing information positively influenced attitudes toward DCT apps, while privacy concerns had a negative effect. The intention to adopt DCT apps were positively influenced by attitude, subjective norms, and privacy self-efficacy. This article is the first to empirically test the adoption of DCT apps of the COVID-19 pandemic and contributes both theoretically and practically toward understanding factors influencing its widespread adoption.

Advancements in Artificial Intelligence (AI) have led to the development of autonomous decision-making processes, allowing customers to delegate decisions and tasks. Such technologies have the potential to alter the retailing landscape. Grounded in the unified theory of acceptance and use of technology and Hofstede's cultural theory, this article investigates customers’ adoption of AI-based autonomous decision-making processes by analyzing 454 customer responses using covariance-based structural equation modeling. The results reveal that effort expectancy, performance expectancy, facilitating conditions, and social influence are positively associated with customers’ adoption of autonomous decision-making processes. Collectivism strengthened the positive association of social influence with customer attitude, whereas uncertainty avoidance dampened the associations of performance expectancy, effort expectancy, and social influence with attitude. The findings provide useful implications for system developers and managers while providing future researchers with directions to further explore autonomous decision-making processes.