Barua Zapan, Barua Adita
Department of Marketing, University of Chittagong, Chattogram 4331, Bangladesh.
Faculty of Business Administration, Cox's Bazar International University, Cox's Bazar 4700, Bangladesh.
J Migr Health. 2023 Jul 28;8:100201. doi: 10.1016/j.jmh.2023.100201. eCollection 2023.
While the healthcare facilities for the people is questionable in Bangladesh, Rohingya refugees is a burning issue for both Bangladesh and global community. Integrating Rohingya refugees into the framework of mHealth could be beneficial for both Bangladesh and Rohingya refugees in general, and in specific situation like COVID-19 outbreak However, no research has been found on what motivates Rohingya refugees to accept mHealth in Bangladesh. Drawing on the UTAUT2 model, this study investigates the predictors of acceptance of mHealth services technologies among Rohingya refugees. The study also seeks to clarify the roles of mHealth developers, the Bangladesh government, and non-governmental organizations working with the 1.1 million Rohingya refugees in Bangladesh. Quantitative data were collected from refugee camps with the permission of the Refugee Relief and Repatriation Commissioner (RRRC). The data were analyzed in two stages using a mixed approach that combines PLS-SEM and Artificial Neural Network (ANN). This study revealed that Effort expectancy (EE, with = 5.629, β = 0.313) and facilitating conditions (FC with = 4.442, β = 0.269) in PLS-SEM, and FC (with 100 percent importance) and Health consciousness (HC, with 94.88 percent importance) in ANN analysis were found to be the most substantial predictors of mHealth adoption. The study also revealed that EE and FC are more important for low education group, while PE and Situational Constraint (SC) are more important for the high education group of refugees. In addition to providing insights for mHealth developers, this study particularly focuses on the role of government institutions and non-governmental social workers in working with the subjects to increase FC and HC among Rohingya refugees and bring them under mHealth services.
虽然孟加拉国为民众提供的医疗保健设施存在问题，但罗兴亚难民问题对孟加拉国和国际社会来说都是一个亟待解决的问题。将罗兴亚难民纳入移动健康框架总体上可能对孟加拉国和罗兴亚难民都有益，在新冠疫情等特殊情况下更是如此。然而，尚未发现有研究探讨是什么促使孟加拉国的罗兴亚难民接受移动健康。本研究借鉴UTAUT2模型，调查了罗兴亚难民接受移动健康服务技术的预测因素。该研究还旨在阐明移动健康开发者、孟加拉国政府以及与孟加拉国110万罗兴亚难民合作的非政府组织所起的作用。在获得难民救济和遣返委员会（RRRC）许可后，从难民营收集了定量数据。使用结合了偏最小二乘结构方程模型（PLS-SEM）和人工神经网络（ANN）的混合方法对数据进行了两阶段分析。本研究表明，PLS-SEM中的努力期望（EE，χ² = 5.629，β = 0.313）和促进条件（FC，χ² = 4.442，β = 0.269），以及ANN分析中的FC（重要性为100%）和健康意识（HC，重要性为94.88%）是移动健康采用的最重要预测因素。该研究还表明，EE和FC对低教育群体更重要，而绩效期望（PE）和情境限制（SC）对高教育水平的难民群体更重要。除了为移动健康开发者提供见解外，本研究特别关注政府机构和非政府社会工作者在与研究对象合作以提高罗兴亚难民的FC和HC并使他们接受移动健康服务方面所起的作用。
