This study explores how customers recognize and accept artificial intelligence devices (AIDs) in the realm of sustainable banking services, applying the Artificially Intelligent Device Use Acceptance (AIDUA) model. This research not only seeks to corroborate the AIDUA model in the banking sector, but also aims to enrich it by introducing technological literacy as a moderating factor, particularly in the perspective of sustainable banking. Data were collected through 435 valid, self-administered face-to-face surveys from bank customers in China, determined through convenience sampling. The hypotheses, covering both direct and moderating effects, were examined using structural equation modeling. This study verifies the applicability and reliability of the AIDUA model, in assessing customer acceptance of AIDs within sustainable banking services. The findings indicate that customer acceptance of AIDs unfolds in three distinct phases. Initially, the consumers’ perceptions of social influence (SI), hedonic motivation (HM), and perceived anthropomorphism (PA) positively influence their green performance expectancy (GPE) and green effort expectancy (GEE) concerning AIDs. As a result, greater GPE and GEE among bank customers lead to stronger positive emotions, which greatly contribute to increased AIDs usage and a reduction in resistance to their implementation. Additionally, the findings determine that technological literacy plays a substantial moderating role in the association connecting green performance expectancy and customer emotions in relation to adopting AIDs, thereby highlighting its importance in advancing sustainable banking initiatives.

The study aims to provide a series of information that will contribute to the understanding of the concept of healthcare marketing and other associated concepts from the perspective of how research in the field has evolved. We aim to quantify the scientific interest in the field of healthcare marketing and to extract aspects that will contribute to a better understanding of the trends that characterize the specific vision of this topic. The research involves a bibliometric analysis, and the conduct of the study is based on the investigation of the academic literature from the Scopus and PubMed databases based on the PRISMA methodology. The study investigates 1077 papers published between 2000 and 2022. According to our study, the most productive countries in the field of healthcare marketing academic publications are the United States, the United Kingdom, and India. As research themes, the study revealed that customer/patient satisfaction, medical tourism, social marketing, and hospital-related marketing topics are highlighted based on cluster analysis of authors keywords. We also discuss the significant lack of research to clarify the use of the concepts such as patient/client/customer in healthcare marketing and to understand the role of prevention services as essential in sustainable healthcare.

In our research, we propose a model that leverages transfer learning and active learning techniques to accumulate knowledge and effectively solve complex problems in the field of artificial intelligence. This model operates within a parallel learning paradigm, aiming to mimic the continuous learning and improvement observed in human beings. To facilitate knowledge accumulation, we introduce a convolutional deep classification auto encoder that extracts spatially localized features from images. This enhances the model’s ability to extract relevant information. Additionally, we propose a learning classification system based on a code fragment, enabling effective representation and transfer of knowledge across different domains. Our research culminates in a theoretical and practical prototype for active learning-based knowledge extraction in various living organisms, including humans, plants, and animals. This knowledge extraction is achieved through image-based learning transfer, focusing on advancing activity recognition in image processing. Experimental results confirm that our method outperforms both baseline approaches and state-of-the-art convolutional neural network methods, underscoring its effectiveness and potential.