Accelerating AI Adoption with Responsible AI Signals and Employee Engagement Mechanisms in Health Care

Abstract

Artificial Intelligence (AI) technology is transforming the healthcare sector. However, despite this, the associated ethical implications remain open to debate. This research investigates how signals of AI responsibility impact healthcare practitioners’ attitudes toward AI, satisfaction with AI, AI usage intentions, including the underlying mechanisms. Our research outlines autonomy, beneficence, explainability, justice, and non-maleficence as the five key signals of AI responsibility for healthcare practitioners. The findings reveal that these five signals significantly increase healthcare practitioners’ engagement, which subsequently leads to more favourable attitudes, greater satisfaction, and higher usage intentions with AI technology. Moreover, ‘techno-overload’ as a primary ‘techno-stressor’ moderates the mediating effect of engagement on the relationship between AI justice and behavioural and attitudinal outcomes. When healthcare practitioners perceive AI technology as adding extra workload, such techno-overload will undermine the importance of the justice signal and subsequently affect their attitudes, satisfaction, and usage intentions with AI technology.

Stimuli Influencing Engagement, Satisfaction, and Intention to Use Telemedicine Services: An Integrative Model

“…People tend to support technical changes (e.g., adopting new technology), but acceptance or rejection of the change will rely on their engagement levels [ 17 ]. High engagement is the key to successful technology adoption [ 17 , 65 ] and satisfaction [ 22 ]. Engaged patients are more likely to be involved in preventive behaviors, self-manage symptoms and treatments, affirmatively seek health information, become advisors to new patients, and promote peer support [ 66 ].…”

“…Several researchers have reported that satisfaction positively influences patients’ continuous intentions [ 44 , 48 ], online and face-to-face consultation intention [ 68 ], usage intention [ 59 ], and m-health emergency use [ 25 ]. Satisfaction has also been found to positively affect AI [ 22 ]. However, travel expenses negatively affect AI [ 26 , 69 ], thus becoming the first encouragement of behavioral intentions in telemedicine [ 70 ].…”

“…In the technology set, engagement, the degree of attention and interest held in using technology [ 71 ], has a successful role in its adoption [ 17 , 65 ]. Therefore, technology researchers place considerable importance on operating studies, which discern the ways and facts to improve engagement [ 19 ]; this engagement affects consumers’ long-term behavioral outcomes, like AI usage intentions in healthcare [ 22 ] and social network usage in sales [ 72 ]. The stimulating variables in technology adoption were indicated to directly influence consumers’ behavioral intentions [ 29 , 41 , 54 ].…”

“…Secondly, engagement might also develop functional outcomes, like satisfaction and long-term usage intentions, as found in the digital healthcare literature. For example, a study by Wang et al [ 22 ] argued that healthcare professionals using AI technologies like Chabot and robots express satisfaction and continuous usage intentions (CUI). In contrast, such research in the context of video-based teleconsultations run on mobile apps or web portals remains untested.…”

AI Decision Making with Dignity? Contrasting Workers’ Justice Perceptions of Human and AI Decision Making in a Human Resource Management Context

“…Beyond the deployment of these technologies, there may also be an educative role for organizations in upskilling workers on the benefits and limitations of AI use. Managers, in particular, could benefit from training concerning the affordances of the technology, as well as an awareness of the dangers of perceiving AI as adding "extra workload" which has the potential to act as a "techno-stressor" which can influence AI justice perceptions and behavioral outcomes (Wang et al, 2021;cf. Yassaee and Mettler, 2019).…”