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Determining Factors Affecting Nurses' Acceptance of a Care Plan System Using a Modified Technology Acceptance Model 3: Structural Equation Model With Cross-Sectional Data.

ABSTRACT: BACKGROUND:Health information technology is used in nursing practice worldwide, and holistic patient care planning can serve as a guide for nursing practice to ensure quality in patient-centered care. However, few studies have thoroughly analyzed users' acceptance of care plan systems to establish individual plans. OBJECTIVE:Based on the technology acceptance model 3 (TAM3), a user technology acceptance model was established to explore what determines the acceptance of care plan systems by users in clinical settings. METHODS:Cross-sectional quantitative data were obtained from 222 nurses at eight hospitals affiliated with public organizations in Taiwan. Using the modified TAM3, the collected data were employed to analyze the determinants of user acceptance of a care plan system through structural equation modeling (SEM). We also employed moderated multiple regression analysis and partial least squares-SEM to test the moderating effects. RESULTS:We verified all significant effects from the use of a care plan system among bivariate patterns in the modified TAM3, except for moderating effects. Our results revealed that the determinants of perceived usefulness and perceived ease of use significantly influenced perceived usefulness and perceived ease of use, respectively. The results also indicated that nurses' perceptions of subjective norm (path coefficient=.25, P<.001), perceived ease of use (path coefficient=.32, P<.001), and perceived usefulness (path coefficient=.31, P<.001) had significantly positive effects on their behavioral intention to use the care plan system, accounting for 69% of the total explained variance. CONCLUSIONS:By exploring nurses' acceptance of a care plan system, this study revealed relationships among the variables in TAM3. Our results confirm that the modified TAM3 is an innovative assessment instrument that can help managers understand nurses' acceptance of health information technology in nursing practice to enhance the adoption of health information technology.

SUBMITTER: Ho KF

PROVIDER: S-EPMC7238093 | biostudies-literature | 2020 May

REPOSITORIES: biostudies-literature

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Determining Factors Affecting Nurses' Acceptance of a Care Plan System Using a Modified Technology Acceptance Model 3: Structural Equation Model With Cross-Sectional Data.

Ho Kuei-Fang KF Chang Pi-Chen PC Kurniasari Maria Dyah MD Susanty Sri S Chung Min-Huey MH

JMIR medical informatics 20200505 5

<h4>Background</h4>Health information technology is used in nursing practice worldwide, and holistic patient care planning can serve as a guide for nursing practice to ensure quality in patient-centered care. However, few studies have thoroughly analyzed users' acceptance of care plan systems to establish individual plans.<h4>Objective</h4>Based on the technology acceptance model 3 (TAM3), a user technology acceptance model was established to explore what determines the acceptance of care plan s ...[more]

PMID: 32369033

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Project description:BackgroundThere are considerable gaps between the need for assistive technologies and the actual adoption of these technologies among older adults, although older adults are among the groups that most need assistive technologies. Consequently, research is needed in this area because older adults' technology acceptance and influencing factors may differ depending on their level of frailty.ObjectiveThe objective of this study was to compare frail, prefrail, and robust groups of South Korean adults regarding their behavioral intention to use daily living assistive technologies and the affecting factors-namely, technological context factors, health contexts and abilities, and attitudinal factors-based on a comprehensive senior technology acceptance model.MethodsA nationwide sample of 500 older South Korean adults (aged 55-92 years) was analyzed, and multivariate linear regression analyses of the robust, prefrail, and frail groups were performed. The independent and dependent variables consisted of 3 factors based on previous studies. First, technological context factors consisted of gerontechnology self-efficacy, gerontechnology anxiety, and facilitating conditions. Second, health contexts and abilities consisted of self-reported health conditions, cognitive ability, social relationships, psychological function, and physical function. Third and last, attitudinal factors consisted of behavioral intention to use assistive technologies, attitude toward use, perceived usefulness (PU), and perceived ease of use (PEOU).ResultsThe results of the analyses showed that technological context factors such as gerontechnology self-efficacy, health contexts and abilities such as self-reported health conditions and psychological function, and attitudinal factors such as attitude toward use, PU, and PEOU had significant effects on behavioral intention to use daily living assistive technologies. In particular, gerontechnology self-efficacy had a significant relationship with behavioral intention to use these technologies in the robust (r=0.120; P=.03) and prefrail (r=0.331; P<.001) groups. Psychological function (life satisfaction) had a significant relationship with behavioral intention to use these technologies in the robust group (r=-0.040; P=.02). Self-reported health conditions had a significant relationship with behavioral intention to use these technologies in the prefrail group (r=-0.169; P=.01). Although each group had a different significant relationship with the variables, attitudinal factors such as attitude toward use affected all groups (robust group: r=0.190; P=.03; prefrail group: r=0.235; P=.006; and frail group: r=0.526; P=.002). In addition, PU and PEOU in the attitudinal factors had a significant relationship with behavioral intention to use assistive technologies in the robust (PU: r=0.160; P=.01; and PEOU: r=0.350; P<.001) and prefrail (PU: r=0.265; P<.001; and PEOU: r=0.120; P=.04) groups.ConclusionsThis study found that the comprehensive senior technology acceptance model of daily living assistive technologies had different associations according to the frailty group. These findings provided insights into the consideration of interventions with daily living assistive technologies for older adults with varying levels of frailty.

Project description:BackgroundChronic pain is a complex disease with high prevalence rates, and many individuals who are affected do not receive adequate treatment. As a complement to conventional therapies, eHealth interventions could provide many benefits to a multimodal treatment approach for patients with chronic pain, whereby future use is associated with the acceptance of these interventions.ObjectiveThis study aims to assess the acceptance of eHealth pain management interventions among patients with chronic pain and identify the influencing factors on acceptance. A further objective of the study is to evaluate the viability of the Unified Theory of Acceptance and Use of Technology (UTAUT) model and compare it with its extended version in terms of explained variance of acceptance.MethodsWe performed a cross-sectional web-based study. In total, 307 participants with chronic pain, as defined according to the International Association for the Study of Pain criteria, were recruited through flyers, posters, and web-based inquiries between December 2020 and July 2021. In addition to sociodemographic and medical data, the assessment included validated psychometric instruments and an extended version of the well-established UTAUT model. For statistical analyses, group comparisons and multiple hierarchical regression analyses were performed.ResultsThe acceptance of eHealth pain management interventions among patients with chronic pain was overall moderate to high (mean 3.67, SD 0.89). There was significant difference in acceptance among age groups (W=9674.0; r=0.156; P=.04). Effort expectancy (β=.37; P<.001), performance expectancy (β=.33; P<.001), and social influence (β=.34; P<.001) proved to be the most important predictors of acceptance. The extended UTAUT (including the original UTAUT factors as well as sociodemographic, medical, and eHealth-related factors) model explained 66.4% of the variance in acceptance, thus supporting the viability of the model. Compared with the original UTAUT model (performance expectancy, effort expectancy, and social influence), the extended model explained significantly more variance (F25,278=1.74; P=.02).ConclusionsGiven the association between acceptance and future use, the knowledge of the influencing factors on acceptance should be used in the development and promotion of eHealth pain management interventions. Overall, the acceptance of eHealth pain management interventions was moderate to high. In total, 8 predictors proved to be significant predictors of acceptance. The UTAUT model is a valuable instrument for determining acceptance as well as the factors that influence acceptance of eHealth pain management interventions among patients with chronic pain. The extended UTAUT model provided the greatest predictive value for acceptance.

Project description:BackgroundTo our knowledge, no evidence is available on health care professionals' use of electronic personal health records (ePHRs) for their health management. We therefore focused on nurses' personal use of ePHRs using a modified technology acceptance model.ObjectivesTo examine (1) the psychometric properties of the ePHR acceptance model, (2) the associations of perceived usefulness, ease of use, data privacy and security protection, and perception of self as health-promoting role models to nurses' own ePHR use, and (3) the moderating influences of age, chronic illness and medication use, and providers' use of electronic health record (EHRs) on the associations between the ePHR acceptance constructs and ePHR use.MethodsA convenience sample of registered nurses, those working in one of 12 hospitals in the Maryland and Washington, DC areas and members of the nursing informatics community (AMIA and HIMSS), were invited to respond to an anonymous online survey; 847 responded. Multiple logistic regression identified associations between the model constructs and ePHR use, and the moderating effect.ResultsOverall, ePHRs were used by 47%. Sufficient reliability for all scales was found. Three constructs were significantly related to nurses' own ePHR use after adjusting for covariates: usefulness, data privacy and security protection, and health-promoting role model. Nurses with providers that used EHRs who perceived a higher level of data privacy and security protection had greater odds of ePHR use than those whose providers did not use EHRs. Older nurses with a higher self-perception as health-promoting role models had greater odds of ePHR use than younger nurses.ConclusionsNurses who use ePHRs for their personal health might promote adoption by the general public by serving as health-promoting role models. They can contribute to improvements in patient education and ePHR design, and serve as crucial resources when working with their individual patients.

Project description:The ubiquitous nature of smartphone ownership, its broad application and usage, along with its interactive delivery of timely feedback are appealing for health-related behavior change interventions via mobile apps. However, users' perspectives about such apps are vital in better bridging the gap between their design intention and effective practical usage. In this vein, a modified technology acceptance model (mTAM) is proposed here, to explain the relationship between users' perspectives when using an AI-based smartphone app for personalized nutrition and healthy living, namely, PROTEIN, and the mTAM constructs toward behavior change in their nutrition and physical activity habits. In particular, online survey data from 85 users of the PROTEIN app within a period of 2 months were subjected to confirmatory factor analysis (CFA) and regression analysis (RA) to reveal the relationship of the mTAM constructs, i.e., perceived usefulness (PU), perceived ease of use (PEoU), perceived novelty (PN), perceived personalization (PP), usage attitude (UA), and usage intention (UI) with the users' behavior change (BC), as expressed via the acceptance/rejection of six related hypotheses (H1-H6), respectively. The resulted CFA-related parameters, i.e., factor loading (FL) with the related p-value, average variance extracted (AVE), and composite reliability (CR), along with the RA results, have shown that all hypotheses H1-H6 can be accepted (p < 0.001). In particular, it was found that, in all cases, FL > 0.5, CR > 0.7, AVE > 0.5, indicating that the items/constructs within the mTAM framework have good convergent validity. Moreover, the adjusted coefficient of determination (R 2) was found within the range of 0.224-0.732, justifying the positive effect of PU, PEoU, PN, and PP on the UA, that in turn positively affects the UI, leading to the BC. Additionally, using a hierarchical RA, a significant change in the prediction of BC from UA when the UI is used as a mediating variable was identified. The explored mTAM framework provides the means for explaining the role of each construct in the functionality of the PROTEIN app as a supportive tool for the users to improve their healthy living by adopting behavior change in their dietary and physical activity habits. The findings herein offer insights and references for formulating new strategies and policies to improve the collaboration among app designers, developers, behavior scientists, nutritionists, physical activity/exercise physiology experts, and marketing experts for app design/development toward behavior change.

Project description:BackgroundAdopting mobile electronic medical record (MEMR) systems is expected to be one of the superior approaches for improving nurses' bedside and point of care services. However, nurses may use the functions for far fewer tasks than the MEMR supports. This may depend on their technological personality associated to MEMR acceptance. The purpose of this study is to investigate nurses' personality traits in regard to technology readiness toward MEMR acceptance.MethodsThe study used a self-administered questionnaire to collect 665 valid responses from a large hospital in Taiwan. Structural Equation modeling was utilized to analyze the collected data.ResultsOf the four personality traits of the technology readiness, the results posit that nurses are optimistic, innovative, secure but uncomfortable about technology. Furthermore, these four personality traits were all proven to have a significant impact on the perceived ease of use of MEMR while the perceived usefulness of MEMR was significantly influenced by the optimism trait only. The results also confirmed the relationships between the perceived components of ease of use, usefulness, and behavioral intention in the Technology Acceptance Model toward MEMR usage.ConclusionsContinuous educational programs can be provided for nurses to enhance their information technology literacy, minimizing their stress and discomfort about information technology. Further, hospital should recruit, either internally or externally, more optimistic nurses as champions of MEMR by leveraging the instrument proposed in this study. Besides, nurses' requirements must be fully understood during the development of MEMR to ensure that MEMR can meet the real needs of nurses. The friendliness of user interfaces of MEMR and the compatibility of nurses' work practices as these will also greatly enhance nurses' willingness to use MEMR. Finally, the effects of technology personality should not be ignored, indicating that hospitals should also include more employees' characteristics beyond socio-demographic profiles in their personnel databases.

Dataset Information

Determining Factors Affecting Nurses' Acceptance of a Care Plan System Using a Modified Technology Acceptance Model 3: Structural Equation Model With Cross-Sectional Data.

Publications

Determining Factors Affecting Nurses' Acceptance of a Care Plan System Using a Modified Technology Acceptance Model 3: Structural Equation Model With Cross-Sectional Data.

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