Impacts of User Expectation and Disconfirmation on Satisfaction and Behavior Intention: The Moderating Effect of Expectation Levels

Wang, Xiaorui; Zhou, Ronggang

doi:10.1080/10447318.2022.2095479

Cited by 11 publications

(3 citation statements)

References 80 publications

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“…Patients also need to use AI technology according to health professionals' needs. For AI nurses to work properly, it must also be user-friendly [51] for patients, as patients are the primary data source.…”

Section: Perceived Ease Of Use For Patients-peoupmentioning

confidence: 99%

Assessing acceptance of AI nurses for outpatients with chronic diseases: From nurses’ perspective

Uymaz,

Akgül

2024

Environ. Soc. Psychol.

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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.

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Section: Perceived Ease Of Use For Patients-peoupmentioning

confidence: 99%

Assessing acceptance of AI nurses for outpatients with chronic diseases: From nurses’ perspective

Uymaz,

Akgül

2024

Environ. Soc. Psychol.

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“…Last but not least, it is clear that there are diferent expectations about the capabilities of the system from different stakeholders, which infuence the levels of acceptance and trust [392]. An expectation management framework from the beginning of the development of the AI system is crucial for system designers and developers to design and develop a system that is more well-received according to user expectations [302].…”

Section: Challenges Conclusion and Future Directionsmentioning

confidence: 99%

Towards Risk‐Free Trustworthy Artificial Intelligence: Significance and Requirements

Alzubaidi,

Al-Sabaawi,

Bai

et al. 2023

International Journal of Intelligent Systems

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Given the tremendous potential and influence of artificial intelligence (AI) and algorithmic decision-making (DM), these systems have found wide-ranging applications across diverse fields, including education, business, healthcare industries, government, and justice sectors. While AI and DM offer significant benefits, they also carry the risk of unfavourable outcomes for users and society. As a result, ensuring the safety, reliability, and trustworthiness of these systems becomes crucial. This article aims to provide a comprehensive review of the synergy between AI and DM, focussing on the importance of trustworthiness. The review addresses the following four key questions, guiding readers towards a deeper understanding of this topic: (i) why do we need trustworthy AI? (ii) what are the requirements for trustworthy AI? In line with this second question, the key requirements that establish the trustworthiness of these systems have been explained, including explainability, accountability, robustness, fairness, acceptance of AI, privacy, accuracy, reproducibility, and human agency, and oversight. (iii) how can we have trustworthy data? and (iv) what are the priorities in terms of trustworthy requirements for challenging applications? Regarding this last question, six different applications have been discussed, including trustworthy AI in education, environmental science, 5G-based IoT networks, robotics for architecture, engineering and construction, financial technology, and healthcare. The review emphasises the need to address trustworthiness in AI systems before their deployment in order to achieve the AI goal for good. An example is provided that demonstrates how trustworthy AI can be employed to eliminate bias in human resources management systems. The insights and recommendations presented in this paper will serve as a valuable guide for AI researchers seeking to achieve trustworthiness in their applications.

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“…UX can be altered by contextual expectations and experiences of individual patients. The at-home dialysis machine should be able to achieve the contextual expectation of the user through familiar components, considering the challenge that user expectations influence their UX (Wang & Zhou, 2022).…”

Section: Service Fit To Intended Contextmentioning

confidence: 99%

Human Factors and User Experience in At-Home Dialysis Treatment Technology and Patient Procedures

Pavlov,

Montalvo,

Sasser

et al. 2023

Proceedings of the International Symposium on Human Factors and

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At-home dialysis treatments are becoming increasingly common as suitable options for renal disease treatment. However, the nature of self-administered, at-home dialysis presents specific medical risks and unsupervised patient experiences that require unique attention by health professionals. This study utilized Arhippainen’s ten user experience heuristics to evaluate and identify strengths and weaknesses of at-home dialysis treatments while suggesting methods to improve the at-home patient experience. Results of our heuristic assessment showed that some at-home dialysis providers consider patient needs and consider human factor design aspects. However, patient experience can still be improved by implementing UX principles aimed at providing a satisfying and positive interaction with products and services.

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Impacts of User Expectation and Disconfirmation on Satisfaction and Behavior Intention: The Moderating Effect of Expectation Levels

Cited by 11 publications

References 80 publications

Assessing acceptance of AI nurses for outpatients with chronic diseases: From nurses’ perspective

Assessing acceptance of AI nurses for outpatients with chronic diseases: From nurses’ perspective

Towards Risk‐Free Trustworthy Artificial Intelligence: Significance and Requirements

Human Factors and User Experience in At-Home Dialysis Treatment Technology and Patient Procedures

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