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The primary objective of this study is to investigate the ethical implications of deploying AI-enabled chatbots in the healthcare sector. In addition, the research underscores trust and reliability as critical factors in addressing the ethical challenges associated with these chatbots.

This study takes a qualitative approach, conducting 13 semi-structured interviews with a diverse range of participants, including patients, healthcare professionals, academic researchers, ethicists, and legal experts. This broad spectrum of perspectives ensures a comprehensive understanding of the ethical implications of AI-enabled chatbots in healthcare. The rich exploratory data gathered from these interviews is then analysed using thematic analysis.

The findings of this study are highly significant in the context of AI-enabled healthcare chatbots. They highlight four major themes: developing trust, ensuring reliability, ethical considerations, and potential ethical implications. The interconnectedness of these themes forms a coherent narrative, highlighting the pivotal role of trust and reliability in mitigating ethical issues.

This study contributes to the existing literature on AI-enabled healthcare chatbots. It not only reveals potential ethical concerns associated with these technologies, such as data security, patient privacy, bias, and accountability, but it also places a significant emphasis on trust and reliability as critical elements that can boost user confidence and engagement in using AI-enabled chatbots for healthcare advice.

This paper aims to address the question of what can significantly impact employees' IB and how employees' IB may be effectively stimulated by investigating key factors such as employees' knowledge sharing, innovation passion, absorptive capacity and risk-taking behaviour on workplace innovation. The moderating role of risk-taking behaviour on the link between absorptive capacity and innovation behaviour is also investigated.

Based on the principles of social exchange theory, the study design explores the complex relationship among knowledge sharing, innovation passion, absorptive capacity and risk-taking vis-à-vis employees' innovation behaviour within a unified analysis framework. Methodologically, employees in the information technology industry in China were surveyed via a questionnaire instrument, with a total of 318 valid questionnaires being collected online. Following a reliability and validity test of the questionnaire, the Smart PLS was used to verify the research model.

Statistically significant results reported were as follows: (1) employees' innovation behaviour is positively impacted by knowledge sharing, innovation passion and absorptive capacity; (2) employees' innovation behaviour is negatively impacted by risk-taking behaviour; (3) knowledge sharing is positively impacted by innovation passion; (4) absorptive capacity is positively impacted by innovation passion; and (5) risk-taking behaviour regulates the relationship between absorptive capacity and innovation behaviour.

Owing to limited research resources, 318 front-line employees were surveyed via an online questionnaire vis-à-vis the sampling method only, specifically taking knowledge sharing, innovation passion, absorptive capacity and risk-taking behaviour as antecedent variables with implications on how employees' innovation behaviour may be stimulated.

The mechanism of augmenting employees' innovation behaviour is chiefly explained from the perspective of innovation passion and risk-taking behaviour, which are conducive towards promoting employees' willingness to improve knowledge sharing and innovation behaviour. The social exchange theory is used as a basis to form an integrated model for the research, contributing to a cumulative theoretical perspective for future work on the impact of innovation passion and risk-taking behaviour on innovation.