Cargando…
Factors Influencing Rural End-Users' Acceptance of e-Health in Developing Countries: A Study on Portable Health Clinic in Bangladesh
Background: Existing studies regarding e-health are mostly focused on information technology design and implementation, system architecture and infrastructure, and its importance in public health with ancillaries and barriers to mass adoption. However, not enough studies have been conducted to asses...
Autores principales: | , , , |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Mary Ann Liebert, Inc., publishers
2019
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6441281/ https://www.ncbi.nlm.nih.gov/pubmed/29664328 http://dx.doi.org/10.1089/tmj.2018.0039 |
Sumario: | Background: Existing studies regarding e-health are mostly focused on information technology design and implementation, system architecture and infrastructure, and its importance in public health with ancillaries and barriers to mass adoption. However, not enough studies have been conducted to assess the end-users' reaction and acceptance behavior toward e-health, especially from the perspective of rural communities in developing countries. Objective: The objective of this study is to explore the factors that influence rural end users' acceptance of e-health in Bangladesh. Methods: Data were collected between June and July 2016 through a field survey with structured questionnaire form 292 randomly selected rural respondents from Bheramara subdistrict, Bangladesh. Technology Acceptance Model was adopted as the research framework. Logistic regression analysis was performed to test the theoretical model. Results: The study found social reference as the most significantly influential variable (Coef. = 2.28, odds ratio [OR] = 9.73, p < 0.01) followed by advertisement (Coef. = 1.94, OR = 6.94, p < 0.01); attitude toward the system (Coef. = 1.52, OR = 4.56, p < 0.01); access to cellphone (Coef. = 1.37, OR = 3.92, p < 0.05), and perceived system effectiveness (Coef. = 0.74, OR = 2.10, p < 0.01). Among demographic variables, age, gender, and education were found significant while we did not find any significant impact of respondents' monthly family expenditure on their e-health acceptance behavior. The model explains 54.70% deviance (R(2) = 0.5470) in the response variable with its constructs. The “Hosmer-Lemeshow” goodness-of-fit score (0.539) is also above the standard threshold (0.05), which indicates that the data fit well with the model. Conclusion: The study provides guidelines for the successful adoption of e-health among rural communities in developing countries. This also creates an opportunity for e-health technology developers and service providers to have a better understanding of their end users. |
---|