Abstract

The fast pace of instantiating the Artificial Intelligence (AI) in digital services has changed the manner in which organisations provide personalised, effective, and data-driven solutions in various fields like e-commerce, healthcare, and education. Even after these improvements, AI implementation by users is still not consistent, mainly because of the issues surrounding the areas of transparency, fairness, accuracy, and control. This lack of transparency, which is commonly called the black-box problem with many AI systems, has decreased the trust level and disposition of their users. To address this, Explainable Artificial Intelligence (XAI) has been proposed as a highly important concept to increase the level of transparency and user comprehension in AI-driven decisions. This research will explore the impact of the main XAI characteristics, such as the transparency of the algorithm, the perceived fairness, the perceived accuracy, and the perceived control on the perceived value of the users and, consequently, on their willingness to use AI-driven services. The paper relies on the Technology Acceptance Model (TAM) and the Theory of Consumption Values that suggested a combined model where perceptions of value serve as a mediating variable between the features of the AI systems and their intention to use them. The quantitative research design was used, and the primary data were gathered through the use of a structured questionnaire and a sample of 200 respondents in the National Capital Region (NCR) of India. The research adds to current literature, combining XAI characteristics with value-based and technology acceptance models, thus providing a more in-depth insight into the AI adoption in the new markets. Managerially, the findings demonstrate the need to create AI systems that are accurate and transparent, as well as fair and user-focused, to increase the perception of value and instigate user acceptance. On balance, it is possible to note that the study highlights the critical importance of the perceived value as a key process that connects the explainable features of AI to user adoption intentions.

Keywords: artificial intelligence (ai); explainable artificial intelligence (xai); perceived value; technology acceptance model (tam); theory of consumption values; ai adoption; consumer behavior;