Development of user acceptance model for electronic medical record system

Arman, Arry Akhmad; Hartati, Sri

doi:10.1109/icitsi.2015.7437724

Cited by 25 publications

(27 citation statements)

References 12 publications

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“…This finding is supported by different literatures. A cross sectional study conducted in Ethiopia (P < 0.001) [29], South Dakota(β = 0.27,p < 0.01) [30], Indonesia(β = 0.30,p < 0.05) [31],Canada(β = 0.35,p < 0.001) [32],Taiwan(β = 0.29,p < 0.001) [33], south Korea (β =0.30, p < 0.05) [34] and Jordan(β = 0.39,p < 0.01) [35]) positively influence intention to use EMRs among health care providers. This study revealed that EMR systems are directly related to improvements in the performances of healthcare providers.…”

Section: Discussionmentioning

confidence: 99%

Intention to use electronic medical record and its predictors among health care providers at referral hospitals, north-West Ethiopia, 2019: using unified theory of acceptance and use technology 2(UTAUT2) model

Ahmed¹,

Bogale

Tilahun

et al. 2020

BMC Med Inform Decis Mak

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Background: Electronic Medical Records (EMRs) are systems to store patient information like medical histories, test results, and medications electronically. It helps to give quality service by improving data handling and communication in healthcare setting. EMR implementation in developing countries is increasing exponentially. But, only few of them are successfully implemented. Intention to use EMRs by health care provider is crucial for successful implementation and adoption of EMRs. However, intention of health care providers to use EMR in Ethiopia is unknown. Objective: The aim of this study was to assess health care provider's intention to use and its predictors towards Electronic Medical Record systems at three referral hospitals in northwest , Ethiopia, 2019. Methods: Institutional based cross-sectional explanatory study design was conducted from March to September among 420 health care providers working at three referral hospitals in northwest Ethiopia. Data were analyzed using structural equation model (SEM). Simple and multiple SEM were used to assess the determinants of health care providers intention to use EMRs. Critical ratio and standardized coefficients were used to measure the association of dependent and independent variables, 95% confidence intervals and P-value were calculated to evaluate statistical significance. Qualitative data was analyzed using thematic analysis.

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Section: Discussionmentioning

confidence: 99%

Intention to use electronic medical record and its predictors among health care providers at referral hospitals, north-West Ethiopia, 2019: using unified theory of acceptance and use technology 2(UTAUT2) model

Ahmed¹,

Bogale

Tilahun

et al. 2020

BMC Med Inform Decis Mak

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“…In a given context, intention to use AI is shaped and influenced by subjective norms and positive aspiration by the community (Zhou, 2011). Several researchers examined that social influence has a positive and significant influence on behavioral intention to use a technology (Arman & Hartati, 2015;Phichitchaisopa & Naenna, 2013;Zhang & Gutierrez, 2007). There were mixed opinions about the social influence effect on behavioral intention to adopt technology such as sometimes we found absenteeism of the effect of social influence on behavioral intention to use of AI (Chatterjee & Bhattacharjee, 2020).…”

Section: Social Influence and Intention To Usementioning

confidence: 99%

HR Professionals’ Intention to Adopt and Use of Artificial Intelligence in Recruiting Talents

Alam

Khan²,

Dhar

et al. 2020

business-perspective-review

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Purpose: The present study is an attempt to explore the antecedents of behavioral intention to use artificial intelligence (AI) in recruiting talents by the HR professionals in Bangladesh. Drawing on the principle of the unified theory of acceptance and use of technology (UTAUT), which was built on the premise of technology acceptance model, the theory of planned behavior, the theory of reasoned action, and so forth, the current study has been conducted in the context of Bangladesh. Method: Building on the understanding of the deductive reasoning approach, the investigation followed the positivism paradigm via a quantitative research strategy. We collected 226 replies from the end-users of AI through a self-administrative survey. We used structural equation modeling (SEM) via SmartPLS. Results: Henceforth, the results in the findings filmed that all the hypotheses were supported. Implications: One of the important implications of the present study is the use of the intervention mentioned in this study for the manufacturing and service firms. Limitation: The major limitation is the use of cross-sectional data which implies that future research must use both cross-sectional and longitudinal data for the generalizability of the observed findings.

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“…In line with our results, previous research did reveal non-significant effects of ease of use. [59][60][61][62] Dasgupta et al 62 assume that the perception of a particularly intuitive and simple handling system might be the cause that the degree of perceived ease of use did not affect behavioural intention. Arman and Hartati 60 argue that perceived ease of use is not a barrier to the acceptance of innovations for consumers with a high level of experience with electronic devices.…”

Section: Partial Least Squares-structural Equation Modelling Resultsmentioning

confidence: 99%

“…[59][60][61][62] Dasgupta et al 62 assume that the perception of a particularly intuitive and simple handling system might be the cause that the degree of perceived ease of use did not affect behavioural intention. Arman and Hartati 60 argue that perceived ease of use is not a barrier to the acceptance of innovations for consumers with a high level of experience with electronic devices. Accordingly, the non-significant effects of perceived ease of use may be due to a high perceived level of experience with electrical appliances and a pronounced belief in smart-lighting systems' user-friendliness among the test subjects.…”

Section: Partial Least Squares-structural Equation Modelling Resultsmentioning

confidence: 99%

An empirical analysis of consumer resistance to smart-lighting products

Juric

Lindenmeier

2018

Lighting Research & Technology

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Considering the low market penetration of smart-lighting technology, this study investigates the drivers and consequences of consumer resistance to these innovative lighting products. The paper builds on research on passive and active consumer resistance and on the technology acceptance theory to analyse individuals’ inclination to adopt or reject smart-lighting products. Moreover, the paper evaluates the moderating effects of gender and age. In addition to the influence of passive and active innovation resistance and based on a representative survey of German consumers ( N = 653), the empirical results identify performance expectancy, social pressure and compatibility and health concerns as major determinants of consumer behaviour. Furthermore, the results vary among consumers of different ages and genders. A follow-up study ( N = 115) investigates consumers’ health concerns more deeply. This study provides practical implications and avenues for future research.

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Development of user acceptance model for electronic medical record system

Cited by 25 publications

References 12 publications

Intention to use electronic medical record and its predictors among health care providers at referral hospitals, north-West Ethiopia, 2019: using unified theory of acceptance and use technology 2(UTAUT2) model

Intention to use electronic medical record and its predictors among health care providers at referral hospitals, north-West Ethiopia, 2019: using unified theory of acceptance and use technology 2(UTAUT2) model

HR Professionals’ Intention to Adopt and Use of Artificial Intelligence in Recruiting Talents

An empirical analysis of consumer resistance to smart-lighting products

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