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    Assessing acceptance of AI nurses for outpatients with chronic diseases: From nurses’ perspective
    (Asia Pacific Academy of Science Pte Ltd, 2024) Uymaz, Ali Osman; Uymaz, Pelin E.; Akg?l, Yakup
    The primary objective of this article is to investigate and forecast nurses’ attitudes toward using AI nurses for outpatients with chronic diseases. AI technology is used in hospitals in a disease-centric manner. However, it is desired by healthcare regulators to be used in an individual-centric and holistic manner. The research model was developed based on the Unified Theory of Accepting and Using Technology. In determining the causes and consequences of the attitudes, actions, ideas, and beliefs of the nurses, the screening technique of causal comparison was used. Research data was collected from registered nurses who work in research hospitals and use intelligent health technologies for inpatients. Based on 494 responses, this study conducted a dual-phase assessment using Partial Least Squares Structural Equation Modeling as well as the creation of an AI method known as deep learning (artificial neural network). According to the results, nurses are convinced that AI is a suitable tool for their nursing tasks and increases their efficiency and productivity. It has been determined that nurses have intentions to use AI nurses for outpatients with chronic diseases. However, nurses have concerns about the reliability of ambulatory patient data. The policies and strategies of regulators will affect the acceptance of AI technology, not only for nurses but for all healthcare professionals and patients. © 2024 by author(s). Environment and Social Psychology is published by Asia Pacific Academy of Science Pte. Ltd.
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    The shift from disease-centric to patient-centric healthcare: Assessing physicians’ intention to use AI doctors
    (Asia Pacific Academy of Science Pte Ltd, 2024) Uymaz, Ali Osman; Uymaz, Pelin E.; Akg?l, Yakup
    This study examines physicians’ attitudes toward the intention to use AI doctors in healthcare. Currently, physicians use smart health technologies, health data, and AI in disease-focused research hospitals, and industry regulators hope that AI technology will be extensively used for each person, which means a shift from disease-centric to individual-centric healthcare. Using the theory of technology acceptance and use, a research model was developed to understand physicians’ intentions to use AI doctors for data collection, diagnosis, treatment planning, and patient follow-up. The causal comparison screening technique was used to determine the causes and consequences of physicians’ attitudes, behaviors, ideas, and beliefs. The responses of 478 physicians were evaluated using structural equation modeling and deep learning (an artificial neural network). It was discovered that physicians intend to use AI doctors first for diagnosis and treatment planning, and then for data collection and patient follow-up. According to the findings, the main constructs are performance expectancy, perceived task technology fit, high-tech habits, and hedonic motivation. © 2024 by author(s). Environment and Social Psychology is published by Asia Pacific Academy of Science Pte. Ltd.
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    Understanding mobile learning continuance after the COVID-19 pandemic: Deep learning-based dual stage partial least squares-structural equation modeling and artificial neural network analysis
    (Asia Pacific Academy of Science Pte Ltd, 2024) Akg?l, Yakup; Uymaz, Ali Osman; Uymaz, Pelin E.
    The influence of COVID-19 on educational processes has halted physical forms of teaching and learning and initiated online and mobile learning systems in most countries. The provision and usage of online and e-learning systems are becoming the main challenge for many universities during the COVID-19 pandemic. Due to the novelty of this situation, a substantial amount of research has been carried out to investigate the issue of m-learning adoption or acceptance. Nevertheless, little is known about studying to examine the continued use of m-learning, which is still in short supply and calls for further research. Five different theoretical models are integrated into this study to develop an integrated model that overcomes this limitation, including the technology acceptance model, the theory of planned behavior, the expectation-confirmation model, the Delone and McLean Information System Success Model, and the Unified Theory of Acceptance and Utilization of Technology 2. This conceptual framework shows novel relationships between variables by integrating trust, personal innovation, learning value, instructor quality, and course quality. Unlike extant literature, this study utilized a hybrid analysis methodology combining two-stage analysis using partial least squares structural equation modeling (PLS-SEM) and evolving artificial intelligence named deep learning (Artificial Neural Network [ANN]) on 250 usable responses. The sensitivity analysis results revealed that attitude has the most considerable effect on the continued use of m-learning, with 100% normalized importance, followed by perceived usefulness (88%), satisfaction (77%), and habit (61%). This research reveals that a “deep ANN architecture” may determine the non-linear relationships between variables in the theoretical model. Further theoretical and practical implications are also discussed. © 2024 by author(s). Environment and Social Psychology is published by Asia Pacific Academy of Science Pte. Ltd.