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An investigation of the effect of nurses' technology readiness on the acceptance of mobile electronic medical record systems.

Kuo KM, Liu CF, Ma CC - BMC Med Inform Decis Mak (2013)

Bottom Line: Adopting mobile electronic medical record (MEMR) systems is expected to be one of the superior approaches for improving nurses' bedside and point of care services.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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Healthcare Administration, I-Shou University, No.8, Yida Rd, Yanchao District, Kaohsiung City 82445, Taiwan, R.O.C.

ABSTRACT

Background: Adopting 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.

Methods: The 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.

Results: Of 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.

Conclusions: Continuous 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.

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Structural model results.
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Figure 2: Structural model results.

Mentions: The estimates for path coefficients focus on the sign, magnitude, and significance of the path relationships in the structural model [76]. Regarding the significance testing of the path coefficients, the study employed the bootstrapping resampling strategy. Based on the results, eight out of eleven hypotheses (H1a, H1b, H2a, H3a, H4a, H5, H6, and H7) were supported. Optimism, innovativeness, discomfort, and insecurity jointly explained about 32.06% of PEOU, while PU was explained by about 49.29% of variance. Further, the model explained about 35.16% of behavioral intention’s variance. These figures are lower than average TAM studies. However, they are higher than other healthcare studies that also recruited nurses as subjects [29,31,32]. As far as the authors are aware of, only Wu et al. [44] and Kummer et al. [80] achieved higher results. Properties of the causal path, including standardized path coefficients and hypotheses testing results, are presented in Table 5 and Figure 2.


An investigation of the effect of nurses' technology readiness on the acceptance of mobile electronic medical record systems.

Kuo KM, Liu CF, Ma CC - BMC Med Inform Decis Mak (2013)

Structural model results.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC3750758&req=5

Figure 2: Structural model results.
Mentions: The estimates for path coefficients focus on the sign, magnitude, and significance of the path relationships in the structural model [76]. Regarding the significance testing of the path coefficients, the study employed the bootstrapping resampling strategy. Based on the results, eight out of eleven hypotheses (H1a, H1b, H2a, H3a, H4a, H5, H6, and H7) were supported. Optimism, innovativeness, discomfort, and insecurity jointly explained about 32.06% of PEOU, while PU was explained by about 49.29% of variance. Further, the model explained about 35.16% of behavioral intention’s variance. These figures are lower than average TAM studies. However, they are higher than other healthcare studies that also recruited nurses as subjects [29,31,32]. As far as the authors are aware of, only Wu et al. [44] and Kummer et al. [80] achieved higher results. Properties of the causal path, including standardized path coefficients and hypotheses testing results, are presented in Table 5 and Figure 2.

Bottom Line: Adopting mobile electronic medical record (MEMR) systems is expected to be one of the superior approaches for improving nurses' bedside and point of care services.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.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.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Healthcare Administration, I-Shou University, No.8, Yida Rd, Yanchao District, Kaohsiung City 82445, Taiwan, R.O.C.

ABSTRACT

Background: Adopting 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.

Methods: The 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.

Results: Of 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.

Conclusions: Continuous 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.

Show MeSH
Related in: MedlinePlus