Cureus · 2023
Factors Affecting the Adoption of Artificial Intelligence-Enabled Virtual Assistants for Leukemia Self-Management.
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Alanzi Turki, Almahdi Reham, Alghanim Danya, Almusmili Lamyaa, Saleh Amani, Alanazi Sarah, Alshobaki Kienaz, Attar Renad, Al Qunais Abdulaziz, Alzahrani Haneen, Alshehri Rawan, Sulail Amenah, Alblwi Ali, Alanzi Nawaf, Alanzi Nouf
Аннотация
AIM AND PURPOSE: The purpose of this study is to analyze the various influencing factors affecting the adoption of artificial intelligence (AI)-enabled virtual assistants (VAs) for self-management of leukemia.
METHODS: A cross-sectional survey design is adopted in this study. The questionnaire included eight factors (performance expectancy, effort expectancy, social influence, facilitating conditions, behavioral intention, trust, perceived privacy risk, and personal innovativeness) affecting the acceptance of AI-enabled virtual assistants. A total of 397 leukemia patients participated in the online survey.
RESULTS: Performance expectancy (μ = 3.14), effort expectancy (μ = 3.05), and personal innovativeness (μ = 3.14) were identified to be the major influencing factors of AI adoption. Statistically significant differences (p < .05) were observed between the gender-based and age groups of the participants in relation to the various factors. In addition, perceived privacy risks were negatively correlated with all other factors.
CONCLUSION: Although there are negative factors such as privacy risks and ethical issues in AI adoption, perceived effectiveness and ease of use among individuals are leading to greater adoption of AI-enabled VAs.
METHODS: A cross-sectional survey design is adopted in this study. The questionnaire included eight factors (performance expectancy, effort expectancy, social influence, facilitating conditions, behavioral intention, trust, perceived privacy risk, and personal innovativeness) affecting the acceptance of AI-enabled virtual assistants. A total of 397 leukemia patients participated in the online survey.
RESULTS: Performance expectancy (μ = 3.14), effort expectancy (μ = 3.05), and personal innovativeness (μ = 3.14) were identified to be the major influencing factors of AI adoption. Statistically significant differences (p < .05) were observed between the gender-based and age groups of the participants in relation to the various factors. In addition, perceived privacy risks were negatively correlated with all other factors.
CONCLUSION: Although there are negative factors such as privacy risks and ethical issues in AI adoption, perceived effectiveness and ease of use among individuals are leading to greater adoption of AI-enabled VAs.