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Impact of Post-Use Satisfaction on Re-Purchase Intentions for AI-Driven Healthcare Smart Wearables
Abstract
The chapter examines the factors influencing the long-term use of smart wearables among users in Northern India. It integrates the Expectation-Confirmation Theory (ECT) and the Latent Variable Model to understand what drives users to continue using these devices. The study, involving 678 participants, employs Structural Equation Modeling (Smart PLS4) to analyze the effects of satisfaction, willingness to pay a premium, and accuracy of health information on the sustained use of smart wearables. The findings underscore that user satisfaction, precise health information, and the readiness to pay more significantly affect users' intentions to repurchase and keep using these devices. This research is particularly beneficial for smartwatch manufacturers and designers. By highlighting the importance of satisfaction, accurate health information, and price factors, it provides crucial insights for improving the design and marketing of smart wearables. These insights are key to fostering user loyalty and ensuring long-term engagement with these technologies.
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