Promoting Healthcare Workers’ Adoption Intention of Artificial-Intelligence-Assisted Diagnosis and Treatment: The Chain Mediation of Social Influence and Human–Computer Trust

Cheng, Mengting; Li, Xianmiao; Xu, Jicheng

doi:10.3390/ijerph192013311

Cited by 19 publications

(18 citation statements)

References 88 publications

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“…This implies that individuals are more inclined to adopt a new healthcare technology when they observe others using it and perceive its user-friendliness and practical utility. Our finding aligns with contemporary research indicating that SI functions as a mediator linking expectancy factors (performance and effort) to healthcare workers' intention to adopt intelligent computing systems for diagnosis and treatment purposes (Cheng et al, 2022). Based on our research model, it is noteworthy that the demonstration effect within SI may alleviate initial concerns regarding the insignificant direct effect of facilitating conditions, as behaviours observed in an individual's environment are readily imitated.…”

Section: Discussionsupporting

confidence: 87%

Determinants of e-pharmacy adoption and the mediating role of social influence among young users

Adebo,

Aladelusi,

Mohammed

2024

JHASS

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PurposeThis study aims to examine the mediating role of social influence on the relationship between key predictors of E-pharmacy adoption among young consumers based on the unified theory of adoption and use of technology (UTAUT).Design/methodology/approachThis study employs a quantitative correlational research design. Based on cluster sampling, data was collected from 306 university students from three public universities in southwestern Nigeria. Data was analysed using partial least square structural equation modeling.FindingsThe primary determinant driving the adoption of e-pharmacy is performance expectancy. Social influence plays a partial mediating role in linking performance expectancy to e-pharmacy adoption. In contrast, it fully mediates the relationship between effort expectancy, facilitating conditions and the adoption of e-pharmacy services.Research limitations/implicationsThis study provides theoretical clarity on recent issues within the UTAUT framework. Findings highlight the complexity of how social factors interact with individual beliefs and external conditions in determining technology acceptance.Practical implicationsResearch includes information relevant to access the impact of e-pharmacy services on healthcare accessibility, affordability and quality in developing countries.Originality/valueThe findings extend the adoption of technology literature in healthcare and offer a new understanding of adoption dynamics. The results emphasize the importance of performance expectancy in driving e-pharmacy adoption, providing a clear direction for stakeholders to enhance service quality and user experience of e-pharmacy. Additionally, the mediating effect of social influence highlights the significance of peer recommendations, celebrity endorsements and social media campaigns in shaping consumer adoption of e-pharmacies among young people.

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Section: Discussionsupporting

confidence: 87%

Determinants of e-pharmacy adoption and the mediating role of social influence among young users

Adebo,

Aladelusi,

Mohammed

2024

JHASS

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“…Building upon the existing body of knowledge, our study meticulously underscores the significance of social influence in the adoption of ChatGPT, reflecting patterns observed in various other contexts as denoted by studies 23 , 105 , 106 . By zeroing in on the unique microcosm of the university setting, we have been able to add layers of depth and specificity to this broader narrative.…”

Section: Discussionsupporting

confidence: 61%

Analyzing ChatGPT adoption drivers with the TOEK framework

Jo,

Bang

2023

Sci Rep

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With the rapid advancements in AI technology and its growing impact on various aspects of daily life, understanding the factors that influence users' adoption intention becomes essential. This study focuses on the determinants affecting the adoption intention of ChatGPT, an AI-driven language model, among university students. The research extends the Technology-Organization-Environment (TOE) framework by integrating the concept of knowledge application. A cross-sectional research design was employed, gathering data through a survey conducted to university students. Structural equation modeling was used to analyze the data, aimed at examining the relationships between key determinants influencing adoption intention. The findings of this research indicate that factors such as network quality, accessibility, and system responsiveness contribute to satisfaction. Furthermore, satisfaction, organizational culture, social influence, and knowledge application significantly affect adoption intention. These findings offer both theoretical and practical implications.

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“…Amplifying publicity and social influence, along with mandatory adoption, can accelerate technology acceptance. Positive social attitudes towards AI, built through effective promotion and word-of-mouth, can significantly influence healthcare workers' adoption intentions [ 59 ].…”

Section: Reviewmentioning

confidence: 99%

Optimizing the Clinical Direction of Artificial Intelligence With Health Policy: A Narrative Review of the Literature

Lakkimsetti,

Devella,

Patel

et al. 2024

Cureus

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Artificial intelligence (AI) has the ability to completely transform the healthcare industry by enhancing diagnosis, treatment, and resource allocation. To ensure patient safety and equitable access to healthcare, it also presents ethical and practical issues that need to be carefully addressed. Its integration into healthcare is a crucial topic. To realize its full potential, however, the ethical issues around data privacy, prejudice, and transparency, as well as the practical difficulties posed by workforce adaptability and statutory frameworks, must be addressed. While there is growing knowledge about the advantages of AI in healthcare, there is a significant lack of knowledge about the moral and practical issues that come with its application, particularly in the setting of emergency and critical care. The majority of current research tends to concentrate on the benefits of AI, but thorough studies that investigate the potential disadvantages and ethical issues are scarce. The purpose of our article is to identify and examine the ethical and practical difficulties that arise when implementing AI in emergency medicine and critical care, to provide solutions to these issues, and to give suggestions to healthcare professionals and policymakers. In order to responsibly and successfully integrate AI in these important healthcare domains, policymakers and healthcare professionals must collaborate to create strong regulatory frameworks, safeguard data privacy, remove prejudice, and give healthcare workers the necessary training.

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Promoting Healthcare Workers’ Adoption Intention of Artificial-Intelligence-Assisted Diagnosis and Treatment: The Chain Mediation of Social Influence and Human–Computer Trust

Cited by 19 publications

References 88 publications

Determinants of e-pharmacy adoption and the mediating role of social influence among young users

Determinants of e-pharmacy adoption and the mediating role of social influence among young users

Analyzing ChatGPT adoption drivers with the TOEK framework

Optimizing the Clinical Direction of Artificial Intelligence With Health Policy: A Narrative Review of the Literature

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