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

Alam, Mirza Mohammad Didarul; Alam, Mohammad Zahedul; Rahman, Syed Abidur; Taghizadeh, Seyedeh Khadijeh

doi:10.1016/j.jbi.2021.103722

Cited by 100 publications

(114 citation statements)

References 58 publications

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“…To such an extent, with the findings from Okumus et al (2018), health app users' readiness would increase if they felt the ease in operating it. Other research supports the result that the effort expectancy in the field of health technology is a factor that affects a user's to use such technology (Lee, S. and Lee, D. (2020), Alam et al, (2021) and Al Aufa et al, (2020).…”

Section: Effort Expectancymentioning

confidence: 73%

The Effects of Performance Expectancy, Effort Expectancy, Facilitating Condition, and Habit on Behavior Intention in Using Mobile Healthcare Application

Utomo

Kurniasari

Purnamaningsih

2021

IJCSE

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South Tangerang Health Office had the responsibility in giving outstanding healthcare services to its resident’s despite of its limitation due Covid-19 pandemic. Some programs were initiated to reduce maternal, babies and toddler mortality, and in the same time reduce the number of malnourished children. The integrated healthcare mobile application called Si Pandai Kemas TangSel had been launched and can be downloaded easily through smartphone. The study is expected to measure the effectiveness of Si Pandai Kemas TangSel using UTAUT approach by measuring the influence of performance expectancy, effort expectancy, facilitating condition and habit toward behavior intention in using Si Pandai Kemas TangSel mobile application. The study showed that the effort expectancy and habit can increase the intention to use Si Pandai Kemas TangSel application. Meanwhile, performance expectancy and facilitating conditions did not affect behavioral intention in using Si Pandai Kemas TangSel application.

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Section: Effort Expectancymentioning

confidence: 73%

The Effects of Performance Expectancy, Effort Expectancy, Facilitating Condition, and Habit on Behavior Intention in Using Mobile Healthcare Application

Utomo

Kurniasari

Purnamaningsih

2021

IJCSE

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“…Moreover, ANN provides the rank and validates the significant predictors of a model [ 20 ]. In addition, ANN deals with linear and nonlinear associations because it depends on the black box process of the algorithm, which is the basis for prediction [ 99 ]. Thus, the multilayer expectations of a feedforward backpropagation (BP) ANN algorithm have generated normalised importance [ 89 ].…”

Section: Discussionmentioning

confidence: 99%

“…To capture linear, testing hypothesis and nonlinear relationships amongst constructs, SEM and ANN analyses were combined to overcome the weaknesses of SEM [ 71 ]. Meanwhile, ANN helps assess the relative importance of the determinants of mobile app adoption in various industries [ 6 , 13 , 53 , 54 , 58 – 60 , 73 , 74 , 82 , 85 , 87 , 99 ]. In summary, using ANN with SEM provides a new perspective on building nonlinear models [ 7 , 65 ].…”

Section: Discussionmentioning

confidence: 99%

“…Simultaneously, environmental reactions and technological awareness are critical factors that affect smart metres [ 79 ]. The results of ANN show that collaboration is the most important indicator of Facebook’s adoption for academic purposes and mHealth apps, followed by resource sharing, perceived enjoyment and perceived benefits [ 80 , 81 , 98 , 99 ]. In accordance with ANN findings, community-oriented social responsibility is a crucial factor for increasing the competitiveness of universities [ 16 ].…”

Section: Discussionmentioning

confidence: 99%

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Hybrid artificial neural network and structural equation modelling techniques: a survey

Albahri

Alnoor

Zaidan

et al. 2021

Complex Intell. Syst.

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Topical treatments with structural equation modelling (SEM) and an artificial neural network (ANN), including a wide range of concepts, benefits, challenges and anxieties, have emerged in various fields and are becoming increasingly important. Although SEM can determine relationships amongst unobserved constructs (i.e. independent, mediator, moderator, control and dependent variables), it is insufficient for providing non-compensatory relationships amongst constructs. In contrast with previous studies, a newly proposed methodology that involves a dual-stage analysis of SEM and ANN was performed to provide linear and non-compensatory relationships amongst constructs. Consequently, numerous distinct types of studies in diverse sectors have conducted hybrid SEM–ANN analysis. Accordingly, the current work supplements the academic literature with a systematic review that includes all major SEM–ANN techniques used in 11 industries published in the past 6 years. This study presents a state-of-the-art SEM–ANN classification taxonomy based on industries and compares the effort in various domains to that classification. To achieve this objective, we examined the Web of Science, ScienceDirect, Scopus and IEEE Xplore® databases to retrieve 239 articles from 2016 to 2021. The obtained articles were filtered on the basis of inclusion criteria, and 60 studies were selected and classified under 11 categories. This multi-field systematic study uncovered new research possibilities, motivations, challenges, limitations and recommendations that must be addressed for the synergistic integration of multidisciplinary studies. It contributed two points of potential future work resulting from the developed taxonomy. First, the importance of the determinants of play, musical and art therapy adoption amongst autistic children within the healthcare sector is the most important consideration for future investigations. In this context, the second potential future work can use SEM–ANN to determine the barriers to adopting sensing-enhanced therapy amongst autistic children to satisfy the recommendations provided by the healthcare sector. The analysis indicates that the manufacturing and technology sectors have conducted the most number of investigations, whereas the construction and small- and medium-sized enterprise sectors have conducted the least. This study will provide a helpful reference to academics and practitioners by providing guidance and insightful knowledge for future studies.

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“…According to (Rather, 2021) survey, COVID19's perception of risk has a moderation impact on the relationship between customer brand engagement with a brand and the intent to visit a tourist attraction (Alam et al, 2021) also found that awareness of COVID19 risk had a moderate impact on the relationship between behavioral intention and use mHealth application in Bangladesh. The results of this study are inconsistent with previous studies in which risk perception does not moderate effect on the relationship between mHealth trust and transaction intention.…”

Section: Discussionmentioning

confidence: 96%

Perceived Risk of Covid-19 and Trust on m-Health Transaction Intention

Laili

Wahyudi

2021

JBMS

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The purpose of the study is to determine the impact of trust antecedents on the intent of transactions in mHealth applications. The antecedents of trust included perceived ease of use, perceived usefulness, perceived quality, perceived safety, and perceived Covid 19 risks. Perceived risks of COVID19 were also examined for moderating effects on the relationship between trust and m-Health transaction intention. The study used a quantitative method with a survey approach. Our surveys are distributed online via the Lime Survey app. 253 respondents responded to the questionnaire according to the criteria. Results showed that the antecedents that had a significant impact on trust were perceived usefulness (P = 0.012), perceived quality (P = 0.002), and perceived safety (P = 0.000). On the other hand, the perceived ease of use (P = 0.566) and the perceived risk (P = 0.871) did not significantly affect trust. Trust (P = 0.000) and perceived risks of COVID19 (P = 0.011) directly affect m-Health transaction intention. The study also found that the perceived risk of COVID19 did not impact the consumer trust in m-Health but directly impacted transaction intention.

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

Cited by 100 publications

References 58 publications

The Effects of Performance Expectancy, Effort Expectancy, Facilitating Condition, and Habit on Behavior Intention in Using Mobile Healthcare Application

The Effects of Performance Expectancy, Effort Expectancy, Facilitating Condition, and Habit on Behavior Intention in Using Mobile Healthcare Application

Hybrid artificial neural network and structural equation modelling techniques: a survey

Perceived Risk of Covid-19 and Trust on m-Health Transaction Intention

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