Combining Deep Neural Network and PLS-SEM to Predict Patients’ Continuity with Telemedicine

Khondker Mohammad Zobair, Louis Sanzogni, Luke Houghton and Md. Zahidul Islam
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Khondker Mohammad Zobair: Department of Business Strategy and Innovation, Griffith Business School, Griffith University, Australia
Louis Sanzogni: Department of Business Strategy and Innovation, Griffith Business School, Griffith University, Australia
Luke Houghton: Department of Business Strategy and Innovation, Griffith Business School, Griffith University, Australia
Md. Zahidul Islam: Computer Science and Engineering Discipline, Science, Engineering and Technology School, Khulna University, Bangladesh

International Journal of Information Technology & Decision Making (IJITDM), 2022, vol. 21, issue 05, 1555-1589

Abstract: This study aims to adapt the Expectation Disconfirmation Theory and Technology Adoption Model to unveil provocative roles in patients’ satisfaction cognitions and subsequent continuity behaviors pertaining to telemedicine services in rural Bangladesh. A quantitative research model is developed and validated using a two-staged deep neural network and partial least squares structural equation modeling approach. The findings of this study provide evidence that five salient determinants; expectations, disconfirmation, performance, usefulness, and ease of use dominantly contribute to predicting patients’ satisfaction concerning continuity with telemedicine. This contributes to health informatics and behavioral literature by clarifying the complex interplay between patients’ satisfaction and determinants of continuity behavior in telemedicine’s domain. The findings provide novel insights into predictions of complex patients’ attitudes toward telemedicine continuity, and dynamic changes in adoption trends thereby assisting health professionals, global health experts, policymakers, and IS community in making higher quality informed decisions for people-centered future models of care.

Keywords: AI; Expectation disconfirmation theory; deep neural networks; PLS-SEM; IPMA; SHAP; TAM; telemedicine (search for similar items in EconPapers)
Date: 2022
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DOI: 10.1142/S0219622022500249

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