PRIOR EXPERIENCE DOES NOT MODIFY THE MOTIVATIONAL OR INHIBITORY COMPONENTS OF PREDISPOSITION TO TELEMEDICINE ADOPTION: MULTIGROUP EVIDENCE FOR BUILDING TECHNOLOGICAL READINESS IN THE BRAZILIAN AMAZON
Author(s)
Everaldo Marcelo Souza da Costa, Doctor of Administration1, Jorge Brantes Ferreira, Ph.D.2, Marcia Athayde Moreira, Doctor of Controllership and Accounting3, Edgar José Pereira Dias, Doctor of Administration1, Hudson Augusto Silva de Castro, Master of Administration1, Maria Bezerra Nobre, Master of Administration1, Fernanda Leao Ramos, Ph.D.4.
1Master's and Doctorate Program in Administration, University of the Amazon, Belém, Brazil, 2Business Administration, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, Brazil, 3Master's Program in Accounting, Federal University of Pará, Belém, Brazil, 4Business Administration, FGV EBAPE, Rio de Janeiro, Brazil.
1Master's and Doctorate Program in Administration, University of the Amazon, Belém, Brazil, 2Business Administration, Pontifical Catholic University of Rio de Janeiro, Rio de Janeiro, Brazil, 3Master's Program in Accounting, Federal University of Pará, Belém, Brazil, 4Business Administration, FGV EBAPE, Rio de Janeiro, Brazil.
OBJECTIVES: Telemedicine expands access where in-person care is scarce, yet availability does not ensure that underserved populations adopt it. Which psychosocial dispositions shape adoption predisposition, and whether they operate similarly across patients with different service trajectories, is decisive for converting telemedicine supply into use and value. This study examined how four technology readiness dimensions (optimism, innovativeness, discomfort, insecurity) compose telemedicine adoption predisposition among adults in the Brazilian Amazon, and whether prior experience changes that structure.
METHODS: An in-person survey reached 251 adults across 29 municipalities of Pará, Brazil, in 2024 (55% female; 51% earning at or below two minimum wages; 123 with prior telemedicine experience, 128 without). Sixteen TRI 2.0 items measured the dimensions, and a composite Telemedicine Adoption Predisposition Index captured the balance between motivating and inhibiting forces. Covariance-based structural equation modeling validated the measurement model, and MICOM-supported partial least squares multigroup analysis estimated and compared paths by experience.
RESULTS: Innovativeness was the strongest positive contributor to adoption predisposition (β=0.332; p<0.001), whereas discomfort (β=-0.304; p<0.001) and insecurity (β=-0.208; p<0.001) exerted significant negative effects. Optimism remained positive but marginal (β=0.076; p=0.077). The model showed moderate explanatory power and predictive relevance (R²=0.344; Q²=0.309). Multigroup analysis did not identify significant differences between experienced and inexperienced patients. Path differences were small and nonsignificant for discomfort (p=0.924), innovativeness (p=0.800), insecurity (p=0.802), and optimism (p=0.651), indicating compositional stability across groups.
CONCLUSIONS: Telemedicine adoption in underserved territories is shaped by motivational openness and persistent subjective barriers. Prior experience did not reconfigure technology readiness, suggesting that experience-based segmentation should not be the main managerial assumption. Health systems should prioritize universal strategies to reduce discomfort and insecurity, including technical support, transparent care pathways, data protection, and professional mediation. Forecasting adoption based only on infrastructure may overstate realized value, since uptake depends on sustained trust, assisted familiarization, and institutional integration with primary care.
METHODS: An in-person survey reached 251 adults across 29 municipalities of Pará, Brazil, in 2024 (55% female; 51% earning at or below two minimum wages; 123 with prior telemedicine experience, 128 without). Sixteen TRI 2.0 items measured the dimensions, and a composite Telemedicine Adoption Predisposition Index captured the balance between motivating and inhibiting forces. Covariance-based structural equation modeling validated the measurement model, and MICOM-supported partial least squares multigroup analysis estimated and compared paths by experience.
RESULTS: Innovativeness was the strongest positive contributor to adoption predisposition (β=0.332; p<0.001), whereas discomfort (β=-0.304; p<0.001) and insecurity (β=-0.208; p<0.001) exerted significant negative effects. Optimism remained positive but marginal (β=0.076; p=0.077). The model showed moderate explanatory power and predictive relevance (R²=0.344; Q²=0.309). Multigroup analysis did not identify significant differences between experienced and inexperienced patients. Path differences were small and nonsignificant for discomfort (p=0.924), innovativeness (p=0.800), insecurity (p=0.802), and optimism (p=0.651), indicating compositional stability across groups.
CONCLUSIONS: Telemedicine adoption in underserved territories is shaped by motivational openness and persistent subjective barriers. Prior experience did not reconfigure technology readiness, suggesting that experience-based segmentation should not be the main managerial assumption. Health systems should prioritize universal strategies to reduce discomfort and insecurity, including technical support, transparent care pathways, data protection, and professional mediation. Forecasting adoption based only on infrastructure may overstate realized value, since uptake depends on sustained trust, assisted familiarization, and institutional integration with primary care.
Conference/Value in Health Info
2026-11, ISPOR Europe 2026, Vienna, Austria
Value in Health, Volume 29, Issue 12S
Code
MT27
Topic
Medical Technologies, Patient-Centered Research
Topic Subcategory
Digital Health
Disease
No Additional Disease & Conditions/Specialized Treatment Areas