Determinants of the Adoption of Mobile Applications that Help Induce Healthy Eating Habits

Matlebjane, Dineo Adolphina; Ndayizigamiye, Patrick; Kante, Macire

doi:10.1007/978-3-031-12825-7_6

Cited by 2 publications

(1 citation statement)

References 26 publications

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“…The non-significant impact of performance and effort expectancy on the behavioral intention to use does not support previous studies which have indicated the positive effect of these two factors (PE and EE) on the adoption of mobile health services ( 38 , 39 , 41 , 66 , 90 , 91 ). Other studies, however, have corroborated our findings of the non-significant impact of performance expectancy and effort expectancy on the intention to adopt ( 51 , 92 ). In addition, other previous findings have also illustrated that performance ( 93 – 95 ) and effort expectancy ( 96 – 99 ) does not determine the intention to use.…”

Section: Discussionsupporting

confidence: 90%

The behavioral intention to adopt mobile health services: The moderating impact of mobile self-efficacy

Mensah

Zeng²,

Mwakapesa³

2022

Front. Public Health

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This study explored the moderating impact of mobile self-efficacy on the adoption of mobile health services. The UTAUT was used as the theoretical foundation for this study. The results have indicated that mobile self-efficacy was significant in moderating the impact of both performance expectancy (β = −0.005, p < 0.05) and effort expectancy (β = −010, p < 0.05) on the adoption of mobile health services. In addition, it was revealed to our surprise that both performance (β = 0.521, t = 9.311, p > 0.05) and effort expectancy (β = 0.406, t = 7.577, p > 0.05) do not determine the behavioral intention to use mobile health services. Effort expectancy and behavioral intention to use were also, respectively, not significant in influencing performance expectancy (β = 0.702, t = 12.601, p > 0.05) and intention to recommend the adoption of mobile health services (β = 0.866, t = 13.814, p > 0.05). Mobile self-efficacy, however, was found to significantly predict the citizen's intention to recommend the adoption of mobile health services (β = 0.139, t = 2.548, p < 0.05). The implications of these findings on mobile health are discussed.

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