Within-study measurement invariance of the UTAUT instrument: An assessment with user technology engagement variables

Parameswaran, Srikanth; Kishore, Rajiv; Li, Pu

doi:10.1016/j.im.2014.12.007

Cited by 45 publications

(25 citation statements)

References 52 publications

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“…Hypothesis 2-1 was dismissed because it indicated that effort expectancy did not have a statistical significance on intention to use smart health care service at the α=.05 and ß=.036, t=.417. This result did not support the preexisting studies [3,4,6,9]; it indicated that ease of use, ease of acquiring results and usefulness had nothing to do with user intention to use. Hypothesis 2-2 was accepted because it showed that social influence did have a statistical significance on user intention to use smart health care service at the α=.05 and ß=.167, t=1.911.…”

Section: Hypothesis Verification Using Multiple Regression Analysiscontrasting

confidence: 82%

“…Hypothesis 2-3 was accepted because it showed that performance expectancy did have a statistical significance on user intention to use smart health care service at the α=.05, ß=.289, and t=3.412. This result did support the preexisting studies [3,4,9,10]; it indicated that user intention to use smart health care service was positively affected by helping health enhancement, time saving, health condition improvement through the smart health care service. Hypothesis 2-4 was dismissed because it showed that facilitating conditions did not have a statistical significance on user intention to use smart health care service at the α=.05, ß=.094, and t=1.268.…”

Section: Hypothesis Verification Using Multiple Regression Analysissupporting

confidence: 61%

“…This study extracted variables such as effort expectancy, social influence, performance expectancy, facilitating conditions proposed by [3,4] as important factors affecting intention to use smart health care service. Also, this study added additional variables personal innovativeness and perceived enjoyment proposed by [5,6] using Davis' TAM (Technology Acceptance Model).…”

Section: Research Modelmentioning

confidence: 99%

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Analysis of Factors Influencing User Intention to Use Smart Health Care Services

Moon¹

2016

AJNC

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This study analyzes factors influencing user intention to use various smart health care services on the empirical level. The analysis shows that users of smart health care services have higher degree of effort expectancy and intention to use the service than non-users. Also, the high potential usages of the service are more positively correlated with the health enhancement, the performance expectancy, the recommendations from friends or family, and the perceived enjoyment or attractiveness of the service than the convenience of usage, the device compatibility with smart devices, and the personal innovativeness.

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Section: Hypothesis Verification Using Multiple Regression Analysiscontrasting

confidence: 82%

Section: Hypothesis Verification Using Multiple Regression Analysissupporting

confidence: 61%

Section: Research Modelmentioning

confidence: 99%

See 1 more Smart Citation

Analysis of Factors Influencing User Intention to Use Smart Health Care Services

Moon¹

2016

AJNC

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“…Gender has been widely studied as a main demographic variable related to ICT adoption (Parameswaran et al, 2015). Venkatesh et al (2003) suggested that performance expectancy is the strongest predictor for men, whereas effort expectancy, social influence, and facilitating conditions are the strongest predictors for women.…”

Section: Introductionmentioning

confidence: 99%

Acceptance of Mobile Health Applications: Examining Key Determinants and Moderators

2019

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Mobile health applications are increasingly numerous and varied. However, despite high expectations and large budgets involved in their development they are often rejected by potential users, and little is known on why this happens. This study aimed to fill this gap by investigating the determinants of technology acceptance and its moderators. Aligned with the Unified Theory of Acceptance and Use of Technology, we examined the moderating roles of age, gender, and smartphone experience in the relationship between technology acceptance determinants (performance expectancy, effort expectancy, social influence, and facilitating conditions) and the intention to use mobile health applications (N = 394, 18-65 years). A stepwise multiple linear regression was conducted. Results showed that the intention to use mobile health applications was determined by performance expectancy moderated by age and smartphone experience, and that the role of the other determinants depended on age and gender (e.g., more intention to use in older men if less effort, and in younger men if better facilitating conditions). These findings show that user characteristics are relevant moderators and should be considered when targeting specific populations to use mobile health applications.

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“…Penerimaan dan penggunaan sistem informasi telah menjadi fokus utama pada literatur dan penelitian yang masuk dalam lingkup sistem informasi (Parameswaran, Kishore, & Li, 2015 (Williams, Rana, & Dwivedi, 2015). Studi ini menggunakan model UTAUT untuk menganalisis persepsi aparatur desa terhadap penggunaan sistem e-village budgeting yang telah diwajibkan oleh Pemerintah Banyuwangi telah mewajibkan sistem e-filling sebagai implementasi kebijakan menteri dalam negeri (Permendagri) untuk menggunakan sistem informasi dalam mengelola dana desa.…”

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Anteseden Perilaku Penggunaan E-Budgeting: Kasus Sistem Informasi Keuangan Desa di Banyuwangi, Indonesia

Andriyanto¹,

Baridwan²,

Subekti³

2019

Journal of Accounting and Business Dynamics

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Within-study measurement invariance of the UTAUT instrument: An assessment with user technology engagement variables

Cited by 45 publications

References 52 publications

Analysis of Factors Influencing User Intention to Use Smart Health Care Services

Analysis of Factors Influencing User Intention to Use Smart Health Care Services

Acceptance of Mobile Health Applications: Examining Key Determinants and Moderators

Anteseden Perilaku Penggunaan E-Budgeting: Kasus Sistem Informasi Keuangan Desa di Banyuwangi, Indonesia

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