Ethical AI in the Healthcare Sector: Investigating Key Drivers of Adoption through the Multi-Dimensional Ethical AI Adoption Model (MEAAM)

The adoption of Artificial Intelligence (AI) in the healthcare service industry presents numerous ethical challenges, yet current frameworks often fail to offer a comprehensive, empirical understanding of the multidimensional factors influencing ethical AI integration. Addressing this critical research gap, this study introduces the Multi-Dimensional Ethical AI Adoption Model (MEAAM), a novel theoretical framework that categorizes 13 critical ethical variables across four foundational dimensions of Ethical AI Fair AI, Responsible AI, Explainable AI, and Sustainable AI. These dimensions are further analyzed through three core ethical lenses: epistemic concerns (related to knowledge, transparency, and system trustworthiness), normative concerns (focused on justice, autonomy, dignity, and moral obligations), and overarching concerns (highlighting global, systemic, and long-term ethical implications). This study adopts a quantitative, cross-sectional research design using survey data collected from healthcare professionals and analyzed via Partial Least Squares Structural Equation Modeling (PLS-SEM). Employing PLS-SEM, this study empirically investigates the influence of these ethical constructs on two outcomes Operational AI Adoption and Systemic AI Adoption. Results indicate that normative concerns most significantly drive operational adoption decisions, while overarching concerns predominantly shape systemic adoption strategies and governance frameworks. Epistemic concerns play a facilitative role, enhancing the impact of ethical design principles on trust and transparency in AI systems. By validating the MEAAM framework, this research advances a holistic, actionable approach to ethical AI adoption in healthcare and provides critical insights for policymakers, technologists, and healthcare administrators striving to implement ethically grounded AI solutions.

A Latent Dirichlet Allocation (LDA) Semantic Text Analytics Approach to Explore Topical Features in Charity Crowdfunding Campaigns

Crowdfunding in the realm of the Social Web has received substantial attention, with prior research examining various aspects of campaigns, including project objectives, durations, and influential project categories for successful fundraising. These factors are crucial for entrepreneurs seeking donor support. However, the terrain of charity crowdfunding within the Social Web remains relatively unexplored, lacking comprehension of the motivations driving donations that often lack concrete reciprocation. Distinct from conventional crowdfunding that offers tangible returns, charity crowdfunding relies on intangible rewards like tax advantages, recognition posts, or advisory roles. Such details are often embedded within campaign narratives, yet, the analysis of textual content in charity crowdfunding is limited. This study introduces an inventive text analytics framework, utilizing Latent Dirichlet Allocation (LDA) to extract latent themes from textual descriptions of charity campaigns. The study has explored four different themes, two each in campaign and incentive descriptions. Campaign description themes are focused on child and elderly health mainly the ones who are diagnosed with terminal diseases. Incentive description themes are based on tax benefits, certificates, and appreciation posts. These themes, combined with numerical parameters, predict campaign success. The study was successful in using Random Forest Classifier to predict success of the campaign using both thematic and numerical parameters. The study distinguishes thematic categories, particularly medical need-based charity and general causes, based on project and incentive descriptions. In conclusion, this research bridges the gap by showcasing topic modelling utility in uncharted charity crowdfunding domains.