Comparison of the young aged and elderly female users' adoption of mHealth services.

The authors' aim in this study was to investigate the factors influencing the adoption of mHealth services between the young aged and the elderly female in Bangladesh. Using the extended Technology Acceptance Model (TAM) with three additional latent variables, i.e., perceived reliability, price value and technology anxiety, the proposed model was empirically examined based on the required data from a total survey of 542 respondents. Researchers revealed that perceived usefulness had significant impact on behavioral intention for both the young aged and the elderly female but perceived ease of use was insignificant for both groups. Perceived reliability and price value were found to have strong impact on actual usage. But the effect of technology anxiety was significant for elderly female but insignificant for young-aged female due to age characteristics. Therefore, this study is the first initiative to compare the adoption factors between young aged and elderly female in Bangladesh.

Factors influencing mHealth adoption and its impact on mental well-being during COVID-19 pandemic: A SEM-ANN approach.

The objectives of this study are to examine the factors affecting the intention and actual usage behavior on mHealth adoption, investigate the effect of actual usage behavior of mHealth on mental well-being of the end-users, and investigate the moderating role of self-quarantine on the intention-actual usage of mHealth under the coronavirus disease (COVID-19) pandemic situation. The required primary data were gathered from the end-users of mHealth in Bangladesh. Using the Unified Theory of Acceptance and Use of Technology (UTAUT2), this study has confirmed that performance expectancy, effort expectancy, social influence, hedonic motivation, and facilitating conditions have a positive influence on behavioral intention whereas health consciousness has an impact on both intention and actual usage behavior. mHealth usage behavior has an affirmative and meaningful effect on the mental well-being of the service users. Moreover, self-quarantine has strong influence on actual usage behavior but does not moderate the intention-behavior relationship. In addition, due to the existence of a non-linearity problem in the data set, the Artificial Neural Network (ANN) approach was engaged to sort out relatively significant predictors acquired from Structural Equation Modeling (SEM). However, this study contributes to the emergent mHealth literature by revealing how the use of the mHealth services elevates the quality of patients' mental well-being under this pandemic situation.