summary
RESEARCH ARTICLE
Theoretical integration of user satisfaction
and technology acceptance of the nursing
process information system
Kuei-Fang Ho1, Cheng-Hsun Ho2, Min-Huey ChungID 1,3*
1 School of Nursing, College of Nursing, Taipei Medical University, Taipei, Taiwan, 2 Graduate Institute of
Information Management, National Taipei University, New Taipei City, Taiwan, 3 Department of Nursing,
Shuang Ho Hospital, Taipei Medical University, New Taipei City, Taiwan
* minhuey300@tmu.edu.tw
Abstract
Background
The nursing process system (NPS) is used to establish the nursing process involving
assessment, diagnosis, planning, intervention, and evaluation in solving the health prob-
lems of patients.
Objectives
The factors influencing the use of the NPS by nurses were analyzed based on user satisfac-
tion and technology acceptance within the 3Q (service quality, information quality, and sys-
tem quality) model.
Methods
In this cross-sectional quantitative study, the valid responses of 222 nurses to a question-
naire were obtained; these nurses worked at eight hospitals affiliated with public organiza-
tions in Taiwan. Structural equation modeling was used to analyze information quality,
system quality, service quality, user satisfaction, perceived usefulness, perceived ease of
use, perceived enjoyment, behavioral attitude, and intention after the nurses had used the
NPS system for more than 1 month.
Results
Information quality, service quality, and system quality influenced user satisfaction. User
satisfaction affected perceived usefulness, perceived ease of use, and perceived enjoyment
and had the highest explanatory power (R2 = 0.75). Furthermore, perceived usefulness, per-
ceived ease of use, and perceived enjoyment influenced behavioral attitude and intention to
use the system. The proposed model explained 53% of the variance in the intention to use
the NPS.
PLOS ONE | https://doi.org/10.1371/journal.pone.0217622 June 4, 2019 1 / 14
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OPEN ACCESS
Citation: Ho K-F, Ho C-H, Chung M-H (2019)
Theoretical integration of user satisfaction and
technology acceptance of the nursing process
information system. PLoS ONE 14(6): e0217622.
https://doi.org/10.1371/journal.pone.0217622
Editor: Katie MacLure, Robert Gordon University,
UNITED KINGDOM
Received: November 13, 2018
Accepted: May 15, 2019
Published: June 4, 2019
Copyright: © 2019 Ho et al. This is an open access article distributed under the terms of the Creative
Commons Attribution License, which permits
unrestricted use, distribution, and reproduction in
any medium, provided the original author and
source are credited.
Data Availability Statement: All relevant data are
within the manuscript and its Supporting
Information files.
Funding: The author(s) received no specific
funding for this work.
Competing interests: The authors have declared
that no competing interests exist.
Conclusions
The relationships between the variables of the 3Q model were successfully used to examine
the intention of nurses toward using the NPS. Using the findings of this study, designers
and programmers can comprehensively understand the perceptions of nurses and further
improve the performance of the NPS.
1 Introduction
Nurses manage electronic health and clinical care records in routine practice; their usage of
electronic health records has correspondingly increased to up to 80% [1]. The nursing infor-
mation system (NIS) is crucial to delivering eHealth services [2], and this system comprises an
integrated module of electronic health records [3]. The NIS can provide technological assis-
tance for the management of all aspects of a task and can improve workflow efficiency [3–5].
The nursing process system (NPS) is a part of the NIS and is widely used by nurses to improve
the quality of care. The NPS is different from decision support systems, which help nurses
make decisions. The NPS involves assessment, diagnosis, planning, intervention, and contin-
ual evaluation of the effectiveness of the patient care plan by using information technology.
The nursing process is the core of practice through which nurses from various domains deliver
holistic and patient-focused care [6].
The challenges of using the NIS include operational failures and mismatches between the
process flow of the system and the workflow of nurses [3, 7]. NPS programmers, who do not
have medical experience, rely on senior nurses’ experience and knowledge to analyze the prac-
tical nursing workflow and nursing documents when completing interface design and pro-
gramming. Therefore, programmers who design, troubleshoot, and maintain the NPS must
consider nurses’ opinions for the successful implementation of the NPS. Users have positive
perceptions of using these systems to enhance work performance [8]. Understanding the fac-
tors that influence the intention of professionals to use the NPS is crucial [9].
The Wixom and Todd (WT) model involves a combination of user satisfaction (US) and
technology acceptance, which are the two primary research streams for investigating the per-
ception of information system success [8]. Within the WT model, US comprises object-based
beliefs (information quality [IQ] and system quality [SysQ]) and object-based attitudes (infor-
mation satisfaction and system satisfaction). Technology acceptance of WT model comprises
behavioral beliefs (perceived usefulness [PU] and perceived ease of use [PEOU]), behavioral
attitude (BA), and intention. IQ is an indicator of a user’s perception of the quality of the sys-
tem’s conveyance of semantic meaning or communication of knowledge. For instance, the
degree to which the information obtained from system is error-free is defined as the accuracy
in IQ. SysQ is a user’s evaluation of the information system’s capabilities, the usability of the
system, and the way of delivering information. For example, whether the system is readily
accessible during a task is a part of SysQ. Information satisfaction and system satisfaction rep-
resent a user’s attitude toward the information system, and this attitude is primarily measured
using IQ and SysQ. PU represents the behavioral belief that the information provided by an
information system enhances work performance. Whether a system is judged to be easy to use
is named PEOU. The BA construct represents a user’s point of view toward the usage of the
technology. In summary, object-based attitudes are external variables that influence behavioral
beliefs, and behavioral beliefs have been found to influence BA and intention [8].
User satisfaction and technology acceptance of the nursing process information system
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Xu et al. [10] added SQ, service satisfaction, and perceived enjoyment (PE) to the WT
model and explored the relationships between SQ, IQ, and SysQ to construct a 3Q model.
They asserted that SQ depends on users’ overall evaluation of an information system and their
opinions regarding the provided service. For example, the personal attention provided by a
system in response to users’ concerns and specific needs during tasks is defined as empathy
and responsiveness in SQ. Service satisfaction is an object-based attitude and is a cognitive and
emotional reaction to SQ. PE indicates whether a user finds the system enjoyable, entertaining,
and interesting to use [11]. Moreover, the 3Q model emphasizes the impact of service satisfac-
tion on PU and PE in addition to how PE influences PEOU and BA.
Extensive research has been conducted regarding the acceptance of and intention to use
medical information technology [12–15]. Previous studies focused on certain characteristics of
the behavioral intention regarding the use and acceptance of the information system by health-
care professionals [16, 17]. Some studies have investigated the influence of SQ, IQ, and SysQ
with technology acceptance on US [9], the relationship between perceived quality and intrinsic
individual perceptions [16], and the individual effects of IQ and SysQ on US [17]. However,
behavioral beliefs were not considered, the comprehensive effects of 3Q (i.e., IQ, SysQ, and
SQ) was not determined, and the effects of 3Q model for using the NPS were not explored. In
accordance with the North American Nursing Diagnosis Association standard, the NPS inves-
tigated in this study was developed by a public organization and was validated in a previous
study [4]. In the present study, we used the 3Q model as the framework and employed struc-
ture equation modeling (SEM) to examine the contextual factors underlying the intention of
nurses to use the NPS. Associations between object-based beliefs (SQ, IQ, and SysQ), object-
based attitude (US), behavioral beliefs (PE, PEOU, and PU), and BA and the effects of these
variables on the intention to use the NPS were also examined. The assumption model adopted
in this study is displayed in Fig 1.
2 Materials and methods
2.1 Theoretical framework
Based on US within the 3Q model, this study hypothesized the relationships between SysQ
and IQ (H1), SysQ and SQ (H2), and IQ and SQ (H3). The success of an information system is
determined by the users’ satisfaction with the overall quality of the system [18]. Delone and
McLean [19] proposed that IQ, SysQ, and SQ affect US. Therefore, we combined service satis-
faction, information satisfaction, and system satisfaction to determine US with the NPS. We
hypothesized the research framework for SysQ (H4), IQ (H5), and SQ (H6) to determine
US with the NPS. On the basis of the object-based attitude affected by behavioral beliefs, we
hypothesized the relationships between US and PU (H7), PEOU (H8), and PE (H9). According
to technology acceptance in the 3Q model, we hypothesized the relationships of PE with
PEOU (H10) and PEOU with PU (H11). In addition, on the basis of the influence of behav-
ioral beliefs on BA and intention, we hypothesized the relationships of BA with PU (H13),
PEOU (H14), PE (H15), and intention (H16).
2.2 Sample
This study recruited nurses from eight hospitals affiliated with public organizations in Taiwan.
The NPS used by the nurses had not been changed in the previous 6 months. The inclusion cri-
teria for the participants were as follows: (1) older than 20 years, (2) with a nursing license, (3)
consenting to participate, and (4) having used the NPS for more than 1 month. According to
Cohen [20], when considering three of the maximum number of predictors, a minimum sam-
ple size of 76 is required to achieve a statistical power of 0.8 and a medium effect size of 0.15
User satisfaction and technology acceptance of the nursing process information system
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with p< 0.05. The minimum sample size was 200 for partial-least-squares (PLS)-SEM [21, 22], and an attrition rate of 25% was expected; thus, we recruited 250 nurses. After excluding par-
ticipants with missing data (n = 28), this study included the data of 222 nurses obtained from
questionnaire surveys.
2.3 Design and measures
In this cross-sectional study, a questionnaire (S1 and S2 Files) was used to understand the
nurses’ experiences and intentions regarding the NPS. The research questionnaire was based
on the 3Q model proposed by Xu et al. [10]. The 3Q model is an extension of the theoretical
integration of US and technology acceptance [8].
After obtaining approval from the original author [10], we employed a structured 81-item
questionnaire assessing the perspective of nurses. The questionnaire evaluated (1) object-
based beliefs of SQ, such as the empathy, service reliability, tangibility, assurance, and respon-
siveness of the service delivered by the NPS; (2) object-based beliefs of SysQ, including the reli-
ability, accessibility, timeliness, and flexibility of the NPS; (3) object-based beliefs of IQ, such
as the currency, completeness, format, and accuracy of the NPS; (4) object-based attitudes of
US; (5) behavioral beliefs, such as PU, PEOU, and PE; and (6) BA and intention. All items
of the questionnaire based on the 3Q model were measured using a Likert scale ranging from
−5 (completely disagree) to 5 (completely agree), with 0 as the neutral score. The internal
Fig 1. The 3Q model adopted in this study.
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User satisfaction and technology acceptance of the nursing process information system
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consistency of the variables was 0.71–0.97, and composite reliability (CR) was 0.84–0.98 [10].
Discriminant validity also satisfied, as the square root of the average variance extracted (AVE)
exceeded inter-construct correlations [10].
2.4 Ethics and data collection
The self-reported questionnaire was collected from NPS users. By investigating the nurses’
experiences of using the information system, we explored the relationships between the vari-
ables in the research framework. We considered that the NPS was utilized by the staff for
assessing patient signs and symptoms. We also explored the etiology of nursing diagnoses
and determined the nursing outcomes and interventions for analyzing the perceptions of the
nurses. This research plan was approved by the Medical Ethics Committee of Tri-Service Gen-
eral Hospital (TSGHIRB No B-104-13). We explained the procedure and theme of the study
to the participants. After the participants provided written consent, we collected the question-
naires and entered the data into a computer. We provided gifts to the nurses who participated
in this study.
2.5 Data analysis
The sociodemographic variables of the nurses and usage characteristics of the information sys-
tem were analyzed using SPSS version 20.0 (Chicago, IL, USA) for Windows. PLS-SEM soft-
ware was used to assess the statistical data and test the hypotheses. We used SmartPLS version
3.0 (University of Hamburg, Germany) to evaluate the causal model and perform confirmatory
factor analysis.
The validity and reliability of the measurement model were assessed by determining inter-
nal consistency reliability, indicator reliability, convergent validity, and discriminant validity
[23]. We also employed a Cronbach’s α higher than the recommended value of 0.7 to indicate internal consistency [24]. Hair, Ringle [23] suggested the following guidelines for model evalu-
ation using SmartPLS: (a) internal consistency reliability: CR > 0.7; (b) indicator reliability:
factor loading > 0.7; (c) convergent validity: AVE> 0.50; and (d) discriminant validity: the
square root of the AVE for each construct should be higher than all of their cross loadings. To
avoid multicollinearity, we applied the criterion that the coefficients of correlation between
two variables must be<0.85 [25].
SEM is the appropriate statistical methodology for analyzing multivariate models [10]. We
used SEM to estimate the variance of perceived IQ, SysQ, and SQ of the NPS based on the
experimental design. The nonparametric bootstrapping procedure was employed to test the
hypotheses and analyze path coefficients. Path coefficients were obtained by bootstrapping 222
cases and 5,000 samples. The structural model comprised path coefficients, R2 values of the
dependent variables, and p values. The significance of path coefficients for p values was consid- ered. If the significance indicator value was less than 0.05, the variable was concluded to have
considerable influence.
Henseler, Dijkstra [26] suggested the use of the standardized root-mean-square residual
(SRMR) for assessing the goodness of fit of the structural model. In this study, the SRMR was
thus used to determine the goodness of fit for the PLS-SEM by using the average magnitude
of the observed correlation and model-implied correlation matrix. We assessed the goodness
of fit of the PLS path models according to the suggestions of Hu and Bentler [27]. The SRMR
was lower than 0.08, which indicated a good model fit [26]. Low, moderate, and high levels
of explanatory power are represented by R2 values of 0.25, 0.50, and 0.75, respectively [23].
User satisfaction and technology acceptance of the nursing process information system
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3 Results
3.1 Participant characteristics
Most of the participating nurses were women (95.5%). More than half (54.05%) had a bache-
lor’s degree in nursing, 32.43% (n = 72) had less than 6 years of clinical experience, 85.59%
had less than 6 years of hospital information system experience, and 63.51% did not experience
pressure when using a computer (Table 1).
3.2 Measurement model
Table 2 lists the Cronbach’s α, factor loading, CR, and AVE values. In this study, the Cron- bach’s α was between 0.71 and 0.95 for the variables. The CR ranged from 0.87 to 0.97. These values were higher than the established acceptance levels, which indicated high internal consis-
tency. The factor loadings of all the items were greater than 0.7. The AVEs of the study vari-
ables were between 0.72 and 0.91. The factor loadings implied satisfactory indicator reliability,
and the AVEs of all the constructs indicated satisfactory convergent validity. Table 3 details
the correlation coefficients and AVE values. In this study, the square roots of the AVE for each
variable were higher than the corresponding correlations, and all bivariate correlations had
coefficients less than 0.85. Thus, the discriminant validity criteria were satisfied.
3.3 Structural model
The SRMR in this study was 0.056, indicating that the model of this study exhibited a satisfactory
model fit. The significant antecedents of IQ were currency (β = 0.14, p< 0.05), completeness (β = 0.29, p< 0.001), and accuracy (β = 0.23, p< 0.01), and those of SysQ were reliability
Table 1. Demographics of the participants (n = 222).
Variable Sample Size %
Gender
Male 10 4.50
Female 212 95.50
Education level
Senior vocational school 4 1.80
Associate degree 88 39.64
Bachelor’s degree 120 54.05
Master’s degree 10 4.50
Position
Staff 215 96.85
Supervisor 7 3.15
Total experience (y)
0–5 72 32.43
6–10 61 27.48
11–15 44 19.82
16–20 28 12.61
>20 17 7.66
Experience of using Hospital Information System (y)
0–5 190 85.59
6–10 21 9.46
>10 11 4.95
Experiencing pressure when using a computer
Yes 81 36.49
No 141 63.51
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(β = 0.45, p< 0.001) and timeliness (β = 0.17, p< 0.05). The significant antecedents of SQ were tangibles (β = 0.16, p< 0.05), responsiveness (β = 0.14, p< 0.05), service reliability (β = 0.15, p< 0.05), and assurance (β = 0.13, p< 0.05). Fig 2 and Table 4 present the PLS analysis results. PU and BA explained 53% of the variance in intention (R2 = 0.53). PU, PEOU, and PE explained
66% of the variance in BA (R2 = 0.66). The explained variance (R2) of the model was 0.42–0.75,
which indicated that the level of the variance explained by the model was higher than moderate.
The PLS analysis results supported 15 of the 16 hypotheses (Fig 2 and Table 4). BA (β = 0.33, p< 0.001) and PU (β = 0.46, p< 0.001) had significant path coefficients for intention. Hypothesis H2 was not supported (β = 0.10), but according to statistical significance, all other 15 hypotheses were supported.
Table 5 presents the total effect and direct effect of the constructs on intention to use the
NPS. PU had the strongest total effect (0.58) and direct effect (0.46) on intention. US had the
second strongest total effect (0.53). US had significant direct effects on PE (0.65), PEOU (0.51),
and PU (0.36).
4 Discussion
4.1 Principal findings
The results supported 15 of our hypotheses, validating the 3Q model proposed by Xu, Benbasat
[10]. This model can be employed to understand the behavioral intention of nurses toward
Table 2. Factor loadings, CR, and AVE for the study variables.
Construct Factor Loadings CR AVE Cronbach’s α R2
Currency 0.81–0.95 0.91 0.77 0.85
Completeness 0.85–0.94 0.92 0.79 0.87
Format 0.92–0.94 0.93 0.86 0.84
Accuracy 0.91–0.92 0.91 0.84 0.81
Information Quality 0.92–0.95 0.95 0.87 0.92 0.74
Reliability 0.80–0.90 0.88 0.72 0.80
Accessibility 0.89–0.93 0.94 0.84 0.90
Flexibility 0.93–0.95 0.91 0.83 0.80
Timeliness 0.85–0.89 0.91 0.76 0.84
System Quality 0.95–0.96 0.95 0.91 0.90 0.56
Empathy 0.91–0.92 0.91 0.84 0.81
Service Reliability 0.84–0.95 0.91 0.78 0.86
Tangible 0.93–0.94 0.95 0.87 0.93
Assurance 0.91–0.92 0.94 0.84 0.90
Responsiveness 0.93–0.94 0.93 0.88 0.86
Service Quality 0.84–0.91 0.87 0.77 0.71 0.75
User Satisfaction 0.80–0.89 0.89 0.72 0.81 0.75
Perceived Enjoyment 0.94–0.95 0.96 0.89 0.94 0.42
Perceived Ease Of Use 0.78–0.92 0.91 0.73 0.87 0.61
Perceived Usefulness 0.91–0.94 0.96 0.86 0.94 0.59
Behavioral Attitude 0.92–0.95 0.95 0.87 0.93 0.66
Intention 0.94–0.96 0.97 0.90 0.95 0.53
CR = composite reliability; AVE = average variance extracted; CUR = Currency; COM = Completeness; FOR = Format; ACU = Accuracy; IQ = Information Quality;
REL = Reliability; ACE = Accessibility; FLE = Flexibility; TIM = Timeliness; SysQ = System Quality; EMP = Empathy; SER = Service Reliability; TAN = Tangible;
ASS = Assurance; RES = Responsiveness; SQ = Service Quality; US = User Satisfaction; PEOU = Perceived Ease of Use; PU = Perceived Usefulness; PE = Perceived
Enjoyment; BA = Behavioral Attitude
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using the NPS. We successfully used technology acceptance and US to explore the factors
affecting intention to use the NPS. When designing an NIS, the behavioral beliefs of nurses
regarding technology acceptance must be considered to ensure they will use the system. More-
over, the results indicated that SQ, IQ, and SysQ affected US, which had strong total effects
on intention. This study is the first to indicate that the 3Q model crucially indicates whether
nurses use an NPS.
4.2 Comparison with prior work
Regarding behavioral beliefs, PU had the strongest total effect (0.58) and direct effect (0.46)
on intention. This strongest total effect of PU on intention was influenced by the paths from
PEOU to PU and from US to PU. The results indicated that the system may assist nurses in
providing clinical care; thus, nurses strongly perceive the system’s usefulness. Furthermore,
PU was affected by PEOU, which is consistent with the results of previous studies [8, 10, 28–
33]. The total effect of PU on intention (0.58) was stronger than that of PEOU (0.35), which is
consistent with reported findings [8, 10, 29–32, 34, 35]. When users quickly find the function
they require, they perceive the information system as being easy to use, which increases the
system’s usefulness. However, some studies have demonstrated that PEOU has a stronger
influence on intention than does PU [28, 33, 36, 37]. This difference between the results of
our study and those of the aforementioned studies may be because the NPS investigated in this
study provides access to medical information and assists nurses in establishing and recording
the daily care plan of patients, helping them perform their care-related tasks. In summary,
Table 3. Correlation coefficients and AVE for the study variables.
Variable 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22
1. CUR 0.88
2. COM 0.62 0.89
3. FOR 0.64 0.80 0.93
4. ACU 0.62 0.72 0.77 0.92
5. IQ 0.65 0.79 0.76 0.76 0.93
6. REL 0.65 0.70 0.65 0.60 0.67 0.85
7. ACE 0.76 0.69 0.67 0.63 0.71 0.70 0.91
8. FLE 0.68 0.65 0.62 0.63 0.72 0.64 0.78 0.91
9. TIM 0.77 0.63 0.62 0.62 0.62 0.60 0.75 0.63 0.87
10. SysQ 0.60 0.78 0.78 0.74 0.78 0.70 0.64 0.61 0.60 0.95
11. EMP 0.60 0.69 0.70 0.76 0.71 0.57 0.61 0.58 0.57 0.73 0.92
12. SER 0.62 0.68 0.69 0.70 0.74 0.62 0.69 0.61 0.58 0.69 0.74 0.88
13. TAN 0.49 0.71 0.72 0.70 0.74 0.55 0.58 0.59 0.52 0.75 0.66 0.70 0.93
14. ASS 0.54 0.71 0.76 0.75 0.73 0.62 0.57 0.57 0.54 0.75 0.74 0.72 0.78 0.91
15. RES 0.59 0.67 0.69 0.72 0.69 0.61 0.61 0.57 0.61 0.66 0.64 0.72 0.67 0.78 0.94
16. SQ 0.57 0.77 0.73 0.68 0.79 0.68 0.65 0.62 0.57 0.75 0.75 0.75 0.72 0.77 0.73 0.88
17. US 0.65 0.79 0.77 0.76 0.79 0.69 0.69 0.68 0.65 0.80 0.78 0.70 0.72 0.76 0.70 0.79 0.85
18. PE 0.41 0.74 0.59 0.53 0.64 0.61 0.51 0.53 0.44 0.65 0.58 0.58 0.57 0.60 0.53 0.70 0.64 0.94
19. PEOU 0.51 0.73 0.62 0.55 0.65 0.69 0.59 0.59 0.51 0.65 0.58 0.58 0.57 0.58 0.54 0.72 0.74 0.68 0.85
20. PU 0.53 0.65 0.62 0.61 0.65 0.79 0.56 0.57 0.49 0.67 0.57 0.55 0.57 0.61 0.54 0.68 0.70 0.58 0.73 0.93
21. BA 0.50 0.75 0.67 0.64 0.73 0.64 0.55 0.59 0.52 0.69 0.61 0.60 0.66 0.64 0.60 0.74 0.78 0.70 0.72 0.73 0.93
22. Intention 0.38 0.64 0.56 0.49 0.53 0.65 0.45 0.42 0.37 0.56 0.51 0.47 0.47 0.53 0.49 0.59 0.62 0.55 0.67 0.69 0.66 0.95
The square root of the AVE for each latent variable is displayed in bold. Values below the diagonal line are Pearson’s correlation coefficients.
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when nurses have a positive perception of the usefulness and ease of use of the NIS, they can
perform their work with high efficiency and have a high intention to use the system.
US exerted the second strongest total effect on intention, which indicated that US affected
intention through the behavioral belief of technology acceptance. Our research results indi-
cated that when users found the information system enjoyable and easy to use, they had a posi-
tive BA. Thus, US influenced the degree of acceptance of the information system. Users were
more satisfied with the performance of the information system when they had a stronger per-
ception of the system being useful and easy to use [8]. The present study focused on the NPS
and did not analyze the information success of the clinical decision support system. We rec-
ommend exploring the user perceptions of nursing process decision support systems through
object-based beliefs (SysQ, IQ, and SQ) in the future.
In this study, SysQ, IQ, and SQ explained 75% of the variance in US (R2 = 0.75). SysQ, IQ,
and SQ are critical constructs for exploring the intention of NPS users. The NIS should be
designed considering information-related quality. For the NIS, IQ, SQ, and SysQ can only be
obtained from nurses. In clinical institutions, programmers and users often identify inconsis-
tencies in a system’s function and requirements. Lu, Hsiao [34] investigated nurses’ acceptance
of a hospital information system and discovered that SysQ, IQ, and SQ positively influenced
PEOU and PU. According to previous studies, the overall support provided by an information
Fig 2. Analysis path of the structural model. Note: �p< 0.05, ��p< 0.01, and ���p< 0.001.
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system department can be assessed by examining SQ [10, 38], which has been widely applied
in offline and new online domains [10]. Using types of information-related quality, especially
SQ, to evaluate medical information systems has not been widely discussed. We established
16 hypotheses on the basis of the 3Q model and analyzed the influence of SysQ, IQ, and SQ
on US.
Medical information systems are not yet completely paperless. Thus, nursing personnel
must enter patient information and then print medical records by using printers or other
equipment. When nurses experience a setback, their evaluation of the quality of the informa-
tion system is affected. Senior staff can use the system easily because of their clinical experience
and ability. When using the NPS to establish a care plan, nurses rely on their personal knowl-
edge, care skills, and experience. New staff members require the assistance of others or relevant
information because of their lack of experience. Therefore, the impact of SysQ on SQ may be
nonsignificant.
5 Limitations
Our study used the extended WT model to construct a 3Q model, which was originally pro-
posed by Xu, Benbasat [10]; only one model was discussed and used as our research frame-
work, which may have decreased the strength of this research. Furthermore, we referred to the
information system success model employed in medical information system research [19] and
combined service satisfaction, information satisfaction, and system satisfaction to determine
the effect of US on intention to use the NPS. Because this is inconsistent with the research by
Xu et al.Xu, Benbasat [10], we could not determine the influence of object-based beliefs on
each object-based attitude. Future research should consider these relationships when con-
structing the 3Q model.
Table 4. Path coefficients, VIF, and results of the research hypotheses.
Hypothesis Relationship Path Coefficient T-Value Result
H1 SysQ! IQ 0.23��� 3.27 Supported
H2 SysQ! SQ 0.10 1.65 Not Supported
H3 IQ! SQ 0.25�� 3.35 Supported
H4 SysQ! US 0.37��� 5.90 Supported
H5 IQ! US 0.25��� 3.42 Supported
H6 SQ! US 0.32��� 4.47 Supported
H7 US! PU 0.36��� 4.36 Supported
H8 US! PEOU 0.51��� 8.45 Supported
H9 US! PE 0.65��� 10.23 Supported
H10 PE! PEOU 0.35��� 4.95 Supported
H11 PEOU! PU 0.47��� 6.16 Supported
H12 PU! Intention 0.46��� 5.56 Supported
H13 PU! BA 0.36��� 3.60 Supported
H14 PEOU!BA 0.24� 2.48 Supported
H15 PE!BA 0.33��� 4.54 Supported
H16 BA! Intention 0.33��� 3.69 Supported
�p < 0.05;
��p < 0.01;
���p < 0.001
https://doi.org/10.1371/journal.pone.0217622.t004
User satisfaction and technology acceptance of the nursing process information system
PLOS ONE | https://doi.org/10.1371/journal.pone.0217622 June 4, 2019 10 / 14
The second limitation of this study is that it had a cross-sectional design, and the data were
collected at only one time point. Longitudinal studies conducted in other information system
fields have examined the moderating effects on the relationships between latent constructs [39,
40]. We recommend that in the future, scholars should explore the effects of the moderators,
such as user experience and voluntary use.
Another limitation of this study is that we proposed the constructs of IQ and SysQ before
those of SQ to explore user behavior intention toward the NPS. The results did not show the
effects among the constructs of object-based beliefs and incompletely represent nurses’ percep-
tions of SQ and its effect on SysQ. Future research should consider the influence of the services
provided by the programmer on SysQ and IQ and the changes that may occur in the service
environment.
6 Conclusion
We investigated the intention of nursing staff to use the NPS by employing a 3Q (IQ, SQ, and
SysQ) model that integrated US and technology acceptance. Our study provides a methodol-
ogy for exploring users’ beliefs, attitudes, and intentions.
In this study, we obtained empirical evidence to determine the critical factors influencing
nurses’ perceptions of the NPS. To increase the intention of users, NPS designers must
consider the workflow of care-related tasks, features of nursing routines, and nurses’ require-
ments to design and implement an NIS that satisfies users and is easy to use. Furthermore,
Table 5. Total and direct effects of the variables on the intention for using the NPS.
Variable Total effect Direct effect
Intention on intention IQ SySQ SQ US PE PEOU PU BA Intention
CUR 0.02� 0.14�
COM 0.05�� 0.29���
FOR 0.01 0.08
ACU 0.04�� 0.23���
IQ 0.17��� 0.25�� 0.25���
REL 0.11��� 0.45���
ACE 0.03 0.10
FLE 0.03 0.14
TIM 0.04� 0.17�
SysQ 0.25��� 0.23��� 0.10 0.37���
EMP 0.03 0.16�
SER 0.02 0.14�
TAN 0.01 0.05
ASS 0.02 0.15�
RES 0.03 0.13�
SQ 0.17��� 0.32���
US 0.53��� 0.65��� 0.51��� 0.36���
PE 0.23��� 0.35��� 0.33���
PEOU 0.35��� 0.47��� 0.24�
PU 0.58��� 0.36��� 0.46���
BA 0.33��� 0.33���
�p < 0.05;
��p < 0.01;
���p < 0.001
https://doi.org/10.1371/journal.pone.0217622.t005
User satisfaction and technology acceptance of the nursing process information system
PLOS ONE | https://doi.org/10.1371/journal.pone.0217622 June 4, 2019 11 / 14
programmers and designers must attach considerable importance to IQ, SQ, and SysQ to
develop a successful NIS. To control the characteristics of the NPS and satisfy users, the system
should be designed to be appropriate and user friendly and to assist nurses in their duties.
Supporting information
S1 File. Questionnaire in Chinese.
(DOCX)
S2 File. Questionnaire in English.
(DOCX)
S3 File. Relevant data.
(CSV)
Author Contributions
Conceptualization: Kuei-Fang Ho, Cheng-Hsun Ho, Min-Huey Chung.
Data curation: Kuei-Fang Ho.
Formal analysis: Kuei-Fang Ho, Min-Huey Chung.
Investigation: Kuei-Fang Ho.
Methodology: Kuei-Fang Ho, Cheng-Hsun Ho, Min-Huey Chung.
Supervision: Cheng-Hsun Ho, Min-Huey Chung.
Validation: Cheng-Hsun Ho, Min-Huey Chung.
Writing – original draft: Kuei-Fang Ho.
Writing – review & editing: Kuei-Fang Ho, Cheng-Hsun Ho, Min-Huey Chung.
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