CIN: Computers, Informatics, Nursing
& Vol. 32, No. 3, 129–137 & Copyright B 2014 Wolters Kluwer Health | Lippincott Williams & Wilkins
F E A T U R E A R T I C L E
Mobile Nursing Information System Utilization The Task-Technology Fit Perspective TUNG-CHENG LIN, PhD
Nursing tasks are information-intensive. Nurses must regularly access patient information to accomplish their patient care planning, implementation, and evaluation tasks.1 Furthermore, ready access to accurate patient, physician, and institutional information is critical to ensuring care quality.2 Therefore, the ability of nursing information systems (ISs) to update patient-condition information in real time is critical to support nurses in their daily tasks.3 Today, advanced-technology portable medical devices facilitate effective point-of-care decisions by providing improved access to information, streamlining workflows, and promoting evidence-based practice.4 The mobile nursing IS (MNIS) integrates laptop computers, tablet computers, and PDAs via wireless communications and networks. The MNIS gives nurses information portability and instant mobile access to hospital IS data, to increase efficiency and effectiveness in the performance of regular nursing tasks.5,6 Despite the enormous benefits of MNIS, implementation of such a system goes beyond the purchase decision to involve complex interactions among user, technical, and organizational factors.5,7 Implementation of MNIS significantly affects both the daily routines of nurses and the overall management strategy of the hospital.7 Mobile nursing IS is a subset of mobile health information systems (MHIS) which apply mobile information and wireless telecommunication technologies to healthcare. However, evidence indicates that MHIS adoption is low among Taiwan medical professionals.8 The Technology Acceptance Model (TAM) and TaskTechnology fit (TTF) are two widely used theoretical models that address new technology adoption behavior.9 The TAM assumes that perceived usefulness and perceived ease of use significantly affect the willingness of users to adopt a
Task-technology fit theory considers how technology may best be deployed to support individuals and facilitate the completion of tasks. This study separates the fit construct into the two realms of task-technology fit and technologyindividual fit and integrates organization readiness with the objective of investigating the effectiveness of mobile nursing information systems in terms of helping nursing staff to accomplish daily clinical tasks. Study participants were clinical professionals with system usage experience who work at one medical center. Results indicated that technologyindividual fit is the factor that most strongly influences usage, followed respectively by task-technology fit and organization readiness. Therefore, strategies designed to implement mobile nursing information systems should focus greater effort on fitting the system to system users by making these systems easy to learn and use, and training easy to complete. System functions should not only facilitate accomplishment of daily clinical tasks such as quickly obtaining information and accurate data but also be portable and provide a user-friendly, easy-to-operate interface. Organizational readiness, the commitment and support of top management, and nursing staff willingness to learn and use the new system are also important factors that influence system usage. KEY WORDS Mobile nursing information system & Task-technology fit & Technology-individual fit & Technology use
technology. Because it is easy to understand and apply, TAM has been used to investigate many technology fields, including job and non–job-related topics, microcomputer and non–microcomputer topics, and participants including students and nonstudents.10 The TTF proposed by Author Affiliation: Department of Information Management, National Taipei University of Nursing and Health Sciences, Taipei, Taiwan. The author has disclosed that he has no significant relationships with, or financial interest in, any commercial companies pertaining to this article. Corresponding author: Tung-Cheng Lin, PhD, No. 365, Ming Te Rd, Peitou District, Taipei City 11219, Taiwan, ROC ([email protected]). DOI: 10.1097/CIN.0000000000000039
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Goodhue and Thompson11 and Goodhue12 focuses on the correspondence among task requirements, individual abilities, and technology functionality. Therefore, TTF is more applicable than TAM in mandatory-use situations that investigate task characteristics and the appropriateness of a particular technology to either business or personal requirements.7,13 In the healthcare context, TTF has been used to investigate factors that influence the adoption of health-related technologies such as MNIS,5 picture archiving and communications systems,14 and electronic medical records systems.15 However, these works have all focused on the fit between task and technology. Because the TTF also considers the fit between technology and individual abilities,11–13,16 Goodhue et al13 further decomposed TTF into two categories, technology fit with task and technology fit with individual. Moreover, because technical systems are embedded into socialorganizational environments,7 organizational factors such as training support, senior management commitment, and user participation also influence the adoption of information technology (IT) by healthcare professionals.6,17 It is imperative that healthcare providers in Taiwan adopt and use MNIS. Therefore, this study applies TTF theory and follows the approach of Goodhue et al13 in subdividing the concept of fit into two components: the clinical tasks– MNIS technology fit (TaTeF) and the MNIS technology– individual fit (TeIF). The developed research model further considers organization readiness to explore MNIS usage more comprehensively.
LITERATURE REVIEW Mobile Nursing Information System Nursing is a highly mobile profession because nurses regularly move among various wards, offices, conference rooms, and outpatient clinics.3 Healthcare information that is resident in patients and clinicians must be available and accessible across the nursing workspace.2 The MNIS uses mobile devices and communication systems to integrate all nurse IS functions into a mobile format that allows pointof-care portability to patient bedsides.2 The MNIS should handle patient information management, assessment, diagnosis, treatment, disposition, and discharge functions as well as extended functions to maximize nursing-job effectiveness and reduce the risk of medication errors.3
Task-Technology Fit Theory The TTF theory, proposed by Goodhue and Thompson11 and Goodhue,12 links the concepts of utilization and fit. A precise definition of TTF has been given both as ‘‘The 130
correspondence between task requirements, individual abilities, and the functionality of the technology’’11(p217) and ‘‘the extent that technology functionality matches task requirements and individual abilities.’’12(p1829) However, the TTF label and the multifaceted definition of TTF of Goodhue and Thompson are easily confused. For example, the two statements a ‘‘more accurate label of fit construct would be task-individual-technology fit, but the simpler TTF (task-technology fit) label is easier to use’’ and ‘‘TTF is the degree to which a technology assists an individual in performing his or her portfolio of tasks’’11(p216) seem to conflict with the precise definition. Due to the simpler TTF label, many TTF-based studies, including those by Yen et al,9 Zigurs and Buckland,18 Dishaw and Strong,19 and Klopping and McKinney,20 defined fit as the match between task needs and technology characteristics. In healthcare settings, Hsiao and Chen,5 Lepanto et al,14 and Kilmon et al15 also concentrated on the fit between clinical tasks and health-related technology. Ammenwerth et al7 further developed the FITT framework (fit between individuals, tasks, and technology). However, their work proposed only the conceptual framework and did not develop relevant measures. Cane and McCarthy21 conducted a meta-analysis to investigate studies related to TTF. They found significant interstudy differences in terms of the TTF definition, the research methodology, and the measurements used.21 For example, Goodhue and Thompson11 and Goodhue12 treated the fit construct as a multidimensional construct and measured it using 32 and 34 items. In contrast, Yen et al,9 Klopping and McKinney,20 and Lin and Huang22 treated the fit construct as a uniconstruct and measured it using four to eight items. In the healthcare setting, Hsiao and Chen5 treated fit as a multidimensional construct, with dimensions including information identification, information acquisition, information integration, and interpretation. Furthermore, Lepanto et al14 adapted 25 items from Goodhue’s scale. Finally, Kilmon et al15 adapted 12 items from Goodhue’s scale and used one item for each dimension. These examples highlight the lack of uniformity in terms of both TTF definition and research methodology.
RESEARCH FRAMEWORK Clinical Task Characteristics, Mobile Nursing Information System Characteristics, and Their Fit Tasks are the actions taken by individuals to turn input into output.11 Task characteristic facets include interdependence, complexity, variety, nonroutineness, and difficulty.11,12 Clinical tasks also share the characteristics of complexity, variety, and interdependence. Examples of
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complexity include changes in patient profiles and new legal documentation requirements and the necessity for healthcare professionals to communicate and coordinate with one another.7,23 An example of variety is the following: strategies for the surgical/nonsurgical treatment of cancer should consider various options such as x-ray therapy, biopsy, bronchoscopy, computer tomography, magnetic resonance imaging, and positron emission tomography scan.24 An example of interdependence is the following: patient information should be shared across the healthcare team to foster the discussion and collaboration necessary to elicit and implement optimal solutions. Technology provides the tools for individuals to perform required tasks. Computer systems are always tools in the realm of IT.11 Usability is an important characteristic to consider in any IS implementation.25 Mobility and portability are significant features of mobile ISs, which typically comprise portable devices, wireless networks, and applications.26 Mobile ISs are particularly suited to healthcare applications because of the highly mobile nature of clinical tasks in the healthcare environment.27 An MNIS uses a mobile IS platform. Patient data accessed via an MNIS should be appropriate to both the needs of the clinical routine and the needs of physicians. To effectively support clinical tasks, this system should be highly reliable and user-friendly and provide regular access to current and past medical information.28 Many studies support the statement that task and technology characteristics influence fit in TTF.9,22,29 The MNIS enables clinicians and nurses to access medical information immediately and handle core clinical tasks such as requesting services, providing care, accessing databases, tracking inventory, communicating, and exchanging data via a mobile platform.30 Through the MNIS, healthcare professionals may accomplish clinical tasks across locational, temporal, and contextual boundaries.24 Based on the above, the study proposed the following hypotheses: H1: Clinical task characteristics are positively associated with fit between clinical task characteristics and MNIS (hereafter TaTeF). H2: MNIS characteristics are positively associated with fit between clinical task characteristics and MNIS (TaTeF).
The Computer Self-Efficacy of Nurses and the Fit With Mobile Nursing Information System Self-efficacy or computer self-efficacy is an important IS characteristic. Computer self-efficacy reflects an individual’s ability to use a computer.31 High computer self-efficacy users are typically able to find and access required data and obtain more benefit from ISs.12,29 Computer self-efficacy has been reported to influence TTF in the implementation
of knowledge management systems as well as the adoption of mobile technologies and mobile-hospital IS usage.22,32,33 The TTF theory may be subdivided into technology fit for the individual and technology fit for the usage context.13,25 Item measurements for TTF universally include ease of use and training, reflecting the degree of fit between technology and individuals.11,12 For medical professionals, Tsiknakis and Kouroubali25 also indicated ease of use as a key technology fit problem with individuals when implementing innovative new medical technologies. Based on the above, the study proposed the following additional hypotheses: H3: MNIS characteristics are positively associated with fit between MNIS and nursing professional computer selfefficacy (hereafter TeIF). H4: Nursing professional computer self-efficacy are positively associated with fit between MNIS and nursing professional computer self-efficacy (TeIF).
Task-Technology Fit, Technology-Individual Fit, and System Usage In the TTF model, fit affects performance positively; a closer fit between technology and user needs facilitates higher performance due to improvements in efficiency, effectiveness, and quality.11–13 In terms of TTF, empirical studies have shown that fit influences the usage of support tools for software, knowledge management systems, and e-learning as well as mobile technology adoption.19,22,29,32 In terms of technology-individual fit, Goodhue et al13 used an experimental design to manipulate technologyindividual fit by training to examine its relationship with performance and found that appropriate training results in faster job completion. Liu et al34 also investigated the effect of technology-individual fit within a multicriteria decision support system context, as well as used the individualtechnology fit construct to find that this fit significantly and positively affected attitudes toward the decision support system in semistructured and unstructured task settings. Based on the above, this study proposed the following additional hypotheses: H5: Fit between clinical task characteristics and MNIS is positively associated with system usage. H6: Fit between MNIS and nursing professional computer self-efficacy is positively associated with system usage.
Organizational Readiness and System Usage Organizational readiness is an important factor that affects IT implementation viability. This factor includes top management support, the IS literacy of project team members, and process reengineering.17 Tsiknakis and Kouroubali25
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investigated the implementation process of an innovative eHealth service (Regional Health Information Networks) in Greece and found managerial commitment and strategies of organizational change to be important factors. Obviously, an organization that does not provide adequate support or preparation in terms of, for example, budget, training, or promotion will delay or even inhibit effective IS implementation and adoption and fail to achieve desired performance improvements. Based on the above, this study proposed the following additional hypothesis. H7: Organizational readiness is positively associated with system usage.
After the literature was reviewed and the hypotheses were considered, the study proposed a research model comprising the seven constructs of clinical task characteristics, MNIS characteristics, nursing professional computer self-efficacy, fit between clinical task characteristics and MNIS (TaTeF), fit between MNIS and nursing professional computer self efficacy (TeIF), organizational readiness, and system usage (Figure 1).
INSTRUMENTS AND PARTICIPANTS This study adopted and modified a scale that had been applied previously in research. Table 1 summarizes the construct definitions, construct measurement variables, and construct references. Four clinical task characteristic measures included three (ill-defined, nonroutine, and interdependence with other business functions) from a scale developed by Goodhue and Thompson11 and one (interdependence with others) from Lin and Huang.22 Four MNIS characteristic measures included two (support function and service support) suggestions related to MNIS derived from Hsiao and Chen5 and two (user interface and portability)
adopted from Gebauer et al.32 The three nursing professional computer self-efficacy measures were all adopted from the scale of Wu et al33 developed to assess mobile healthcare system self-efficacy. The TaTeF measures included four items (proper level of detail, accessibility, system reliability, and accuracy) from an instrument developed by Goodhue and Thompson.11 The TeIF measures included three items (easy to learn how to use, easy to get training, and easy to use) from an instrument developed by Goodhue and Thompson.11 The organizational readiness measures included four items (managerial commitment, training support, and user involvement and user support) based on the suggestions of Tsiknakis and Kouroubali25 and the concept of Ammenwerth et al7 of IT adoption in clinical environments. The system usage measures included three items (efficiency, effectiveness, and overall performance evaluation), also adopted from the scale of Goodhue and Thompson.11 To ensure instrument content validity, initial measurement items in each construct were further revised by a fiveperson expert panel comprising two experts who worked in a medical center IT department and had MNIS implementation experience and three experts who were nurses and had MNIS usage experience. The revised questionnaire had 25 items scored using a 7-point Likert scale, with scores ranging from 1 (completely disagree) to 7 (completely agree). The study conducted a pretest of the questionnaire on 30 medical center nursing staff experienced in using a mobile nursing cart (a type of MNIS). Cronbach’s ! values for the seven constructs were .866, .890, .800, .800, .884, .924, and .948, respectively, indicating that initial items had very high internal consistency for each construct. The formal study invited medical center nursing staff with MNIS experience to volunteer to participate in this research. The MNIS in this medical center consisted of
FIGURE 1. Results of the structural model.
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CIN: Computers, Informatics, Nursing & March 2014 Copyright © 2014 Wolters Kluwer Health | Lippincott Williams & Wilkins. Unauthorized reproduction of this article is prohibited.
T a b l e 1 Construct Definitions, Measurement Variables, and References Construct (Abbreviation)
Definition
Clinical tasks characteristic (CTC)
The characteristics of clinical tasks performed by medical professionals
Mobile nursing information system characteristic (MNISC) Nursing professional computer self-efficacy (NPCSE)
The technical features of MINSs
Task-technology fit (TaTeF)
Organizational readiness (OR)
Degree to which MNIS capabilities match clinical tasks Degree to which MNIS capabilities match with nursing professional requirements The readiness of an organization to implement MNIS
System usage (SU)
The efficacy of MNIS
Technology-individual fit (TeIF)
A nursing professional’s confidence in his/her ability to use MNIS to execute clinical tasks
two primary elements: (1) a tablet personal computer (PC) through which users could access the nursing IS, perform nursing evaluation tasks, adjust nursing staff schedules, check outpatient clinic status, and access online bulletins and daily agenda sheets and (2) a mobile e-nursing cart installed with a tablet PC and nurse IS with which nursing staff could do evaluations, set caring goals, deliver treatments, and conduct related nursing activities all at patients’ bedsides via a wireless network. The cart could connect with nursing station in real time via the nurse IS.
Measurement Variables Ill-defined, nonroutine, dependent on cooperation from other teams, dependent on cooperation from colleagues User interface, portability, retrieve information when needed, service support MNIS may be operated without external guidance, MNIS may be operated if user has no previous MNIS experience, MNIS may be operated if user has previous experience on a similar system Appropriate level of detail, accessibility, system reliability, and accuracy Easy to learn to operate, easy to get training, easy to use
Management commitment, training support, user involvement, user support Efficiency, effectiveness, and overall performance evaluation
References Goodhue and Thompson11 and Lin and Huang22 Hsiao and Chen5 and Gebauer et al32 Wu et al33
Goodhue and Thompson11 Goodhue and Thompson11
Ammenwerth et al7 and Tsiknakis and Kouroubali25 Goodhue and Thompson11
two phases: measurement model and structural model.8 In the measurement model, confirmatory factor analysis was used to assess the psychometric properties of all constructs for reliability and validity. The structural model examined the causal relationships among the constructs in the research model. To verify these relationships, the standardized path coefficients and their statistical significance were used as estimates for testing the hypotheses and R2 was examined to assess the predictive validity of the model.
Ethical Considerations
Data Analysis This study used IBM SPSS 20.0 (IBM, Armonk, NY) and SmartPLS 2.0 (partial least squares [PLS]) for statistical analysis. The PLS method is a component-based structural equation modeling technique35 that does not require multivariate data normality and typically allows smaller sample sizes.8 The PLS method is best suited to assessing the validity of antecedent variables.22 Because the sample size was relatively small (n = 144) and the study purpose was to verify the factors that influence MNIS usage, this study chose SmartPLS 2.0, developed by Ringle et al,36 to analyze data. The study conducted model fit evaluation in
All participants were assured anonymity and researchers informed them that participation was wholly voluntary and that they could withdraw at any time.
RESULTS Demographics The study collected 144 valid questionnaires. Participants were all female nurses; two-thirds (92, 63.9%) held
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baccalaureate degrees, with most of the remainder (41, 28.5%) holding college degrees; all (144, 100%) had computer usage experience; 59.0% (85) had worked for 10 years or more; and 41.0% (59) had worked for 10 years or more in the healthcare industry. In terms of grade level, 9.7% (14) were N0*, 20.1% (29) were N1, 45.8% (66) were N2, 11.8% (17) were N3, 6.3% (9) were N4, and 6.3% (9) were in other level categories; 96.5% (139) had mobile device usage experience (eg, smartphone, tablet PC, PDA); and 93.1% (134) held a positive attitude toward MNIS implementation.
Measure Model Assessment This study assessed convergent validity using factor loadings for all items (criterion 90.7), composite reliability (CR; criterion 90.8), and average variance extracted (AVE; criterion 90.5).8 In terms of discriminant validity, the study compared the square root of the AVE with correlations among constructs. A square root for the AVE greater than the correlation with other constructs indicated good discriminant validity.8 Results showed that factor loading values for all items were between 0.773 and 0.956; CR values for all constructs were between 0.887 and 0.966; and AVE values for all constructs were between 0.663 and 0.904. All obtained values were above the suggested threshold value, indicating good convergent validity (Table 2). Discriminant validity was obtained by comparing the square root of the AVE (bold figures on the diagonal) with correlations among constructs. Results indicated each construct more closely related to its own measures than those of other constructs, supporting discriminant validity (Table 3). This study used Harman’s one-factor test to examine common method variance.37 Results found multiple factors, with the first accounting for 42.09% of variance, lower than the 50% threshold value suggested by Xu et al.37
Structure Model Assessment and Hypothesis Testing The PLS technique investigated the structural model by examining the effects among the seven constructs. Figure 1 shows the standardized path coefficients among the constructs and the explained construct variances (R2 value) for the conceptual model. As hypothesized, the paths from clinical task characteristics to TaTeF (H1) and from MNIS characteristics to TaTeF (H2) were found to be positive and significant, with path coefficients of 0.352 (t = 4.762, *N0-N4: nursing ladder system grade designations used in Taiwan: N0, new nurse within 3 years; N1, able to provide general patient care; N2, able to provide critical care; N3, able to handle quality assurance; N4, able to handle administrative responsibilities.
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P G .001) and 0.298 (t = 4.550, P G .001), respectively. The paths from MNIS characteristics to TeIF (H3) and from nursing professional computer self-efficacy to TeIF (H4) had strong significant and positive effects, with path coefficients of 0.354 (t = 5.531, P G .001) and 0.193 (t = 2.849, P G .01), respectively. The paths from TaTeF to MNIS usage (H5), from TeIF to MNIS usage (H6), and from organization readiness to MNIS usage (H7) were also found to be positive and significant, with path coefficients of 0.300 (t = 3.839, P G .001), 0.362 (t = 4.250, P G .001), and 0.227 (t = 2.267, P G .01), respectively. Hence, hypotheses H1 to H7 were supported. Furthermore, the R2 value of 0.553 demonstrated that TaTeF, TeIF, and organization readiness each explained a significant amount of MNIS usage variance.
DISCUSSION Goodhue and Thompson11 and Goodhue12 proposed TTF to describe how ISs are used. Because TTF considers not only technology and users but also IT system support tasks,21 the theory has been used in various contexts, including healthcare. However, Hsiao and Chen,5 Lepanto et al,14 and Kilmon et al15 focused only on the TTF when exploring the adoption of IT in the healthcare setting. Although Ammenwerth et al7 extended the TTF model to consider TTF, technology-individual fit, and taskindividual fit to better understand IT adoption in healthcare settings, the study lacked measurement items. This study focused on technology and took the further step of separating TTF into TTF and technology-individual fit to gain more precise measurements. This approach was supported by empirical data. This study further examined the pure TTF model in which model indicators of TTF were all indicators of TaTeF and TeIF in the proposed model (decomposing TTF into TaTeF and TeIF) by the same dataset. The pure TTF model earned a lower R2 value than the proposed model (0.428 vs 0.553), indicating that the proposed model provides superior explanatory power. To provide a stronger theoretical basis to explore factors of influence in different IT application contexts, many researchers have previously integrated TTF with other theories such as TAM,9,20,29 the postacceptance model,38 and social cognition theory.22 However, the total effect of TTF was no longer the strongest factor after integration. Positing that TTF, perceived usefulness, and perceived ease of use all influence attitudes toward use, Dishaw and Strong29 integrated TTF with TAM to explain software utilization and found that tool experience had a stronger effect on actual tool use than TTF did. Klopping and McKinney20 combined TTF and TAM to predict online shopping activity and found that perceived usefulness affected intention to use more than TTF did. Lin and Huang22 integrated the social cognitive theory and TTF to determine
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T a b l e 2 Reliability and Convergent Validity Constructs CTC
MNISC
NPCSE
TaTeF
TeIF
OR
SU
Items
Mean
SD
Factor Loading
Cronbach’s !
CR
Average Variance Extracted
CTC_1 CTC_2 CTC_3 CTC_4 MNISC_1 MNISC_2 MNISC_3 MNISC_4 NPCSE_1 NPCSE_2 NPCSE_3 TaTeF_1 TaTeF_2 TaTeF_3 TaTeF_4 TeIF_1 TeIF_2 TeIF_3 OR_1 OR_2 OR_3 OR_4 PI_1 PI_2 PI_3
6.160 6.042 6.063 5.910 6.465 6.493 6.576 6.382 5.333 5.236 5.215 5.708 5.840 5.819 5.854 5.708 5.819 5.819 5.792 5.813 5.646 5.778 6.035 6.076 6.042
0.799 0.792 0.830 0.827 0.647 0.626 0.621 0.748 0.908 0.901 0.976 0.844 0.754 0.790 0.766 0.923 0.929 0.951 0.876 0.844 1.067 1.067 0.788 0.794 0.792
0.832 0.806 0.825 0.795 0.884 0.918 0.937 0.827 0.954 0.953 0.944 0.845 0.922 0.935 0.923 0.882 0.956 0.944 0.826 0.773 0.825 0.863 0.955 0.942 0.936
.831
0.887
0.663
.914
0.940
0.797
.947
0.966
0.904
.927
0.949
0.822
.919
0.949
0.861
.840
0.893
0.676
.939
0.961
0.892
Abbreviations: CTC, clinical task characteristics; MNISC, mobile nursing information system characteristics; NPCSE, nursing professional computer self-efficacy; TaTeF, task-technology fit; TeIF, technology-individual fit; OR, organization readiness; SU, system usage.
the key factors affecting knowledge management systems usage and found the influence of knowledge management systems self-efficacy was greater than TTF. Yen et al9 integrated TAM and TTF models to elicit determinants of organizational users’ intention to use wireless technology and found that the combined effect of perceived usefulness and perceived ease of use was greater than that of TTF. The study found that technology-individual fit had a stronger effect on MNIS usage than TTF did.
Gurses et al39 suggested that a system designed to facilitate nursing tasks should be compatible with the mobile nature of such task and support quick information processing in its design. Alsos et al40 also argued the importance of using an attention-easy user interface to ensure that physicians continue to rely more on their own cognitive resources than the device for patient care. Research findings are consistent with these criteria. The characteristics of MNIS are one important predictor of TTF and
T a b l e 3 Discriminant Validity and Correlations CTC MNISC NPCSE TaTeF TeIF OR SU
CTC
MNISC
NPCSE
TaTeF
TeIF
OR
SU
.814 .491 .387 .498 .320 .342 .449
– .893 .278 .471 .396 .454 .504
– – .951 .309 .277 .341 .408
– – – .907 .523 .495 .606
– – – – .928 .580 .640
– – – – – .822 .589
– – – – – – .944
Diagonal values (in bold): square root of AVE; nondiagonal values: correlations among constructs. Abbreviations: CTC, clinical task characteristics; MNISC, mobile nursing information system characteristics; NPCSE, nursing professional computer self-efficacy; TaTeF, task-technology fit; TeIF, technology-individual fit; OR, organization readiness; SU, system usage.
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technology-individual fit. In the proposal model, MNIS characteristic indicator addresses system portability and user interface. Organizational readiness significantly affects MNIS usage. Ammenwerth et al7 argued that because technical systems are embedded in the social-organizational environment, organizational change necessarily accompanies IT implementation. Tsiknakis and Kouroubali25 also indicated that IT adoption or diffusion requires work practice adaptation, reorientation, and organizational change, especially in the healthcare domain. This suggests that organizational change factors such as senior management support, training, and user involvement are important to IT adoption.
CONCLUSIONS The TTF provides an important theoretical basis for understanding the behavior of users with regard to adopting new or implemented IT. The current study expands upon the original TTF proposed by Goodhue and Thompson11 by considering organizational readiness and dividing the TTF construct into distinct technology-task fit and technologyindividual fit constructs. Results show that TTF, technology-individual fit, and organizational readiness each significantly affect MNIS adoption and usage. This study also found that the technologyindividual fit has significantly more influence than the technology-task fit on MNIS performance. This finding is consistent with other studies that integrated TTF with other theories and found that TTF is not the most important factor of influence on system usage. Empirical results in the current study suggest that new system implementation should focus more effort on fitting the system to system users by making it easy to use, easy to learn, and easy to complete training. In terms of technology, MNIS functions not only support daily clinical tasks such as accessing information and acquiring accurate data but also provide portability and a user-friendly interface. In terms of users, nursing professionals should have a level of computer self-efficacy adequate to operate the MNIS and its various functions. Organizational readiness is an important factor of influence on MNIS usage. Therefore, senior hospital administrators should provide commitment and support sufficient for successful MNIS implementation. Nursing staffs should also be actively involved in the implementation stage to realize full system benefits.
Acknowledgments The author would like to thanks Ms Hsu, Hsiao-Wen for helping in sending out and collecting questionnaires. 136
REFERENCES 1. Carter-Templeton H. Nurses’ information appraisal within the clinical setting. Comput Inform Nurs. 2013;31(4):167–175. 2. Choi J, Chun J, Lee K, et al. MobileNurse: hand-held information system for point of nursing care. Comput Methods Programs Biomed. 2004;74:245–254. 3. Su KW, Liu CL. A mobile nursing information system based on human-computer interaction design for improving quality of nursing. J Med Syst. 2012;36:1139–1153. 4. Lu YC, Yan X, Sears A, Jacko JA. A review and a framework of handheld computer adoption in healthcare. Int J Med Inform. 2005; 74(5):409–422. 5. Hsiao JL, Chen RF. An investigation on task-technology fit of mobile nursing information systems for nursing performance. Comput Inform Nurs. 2012;30(5):265–273. 6. Hsiao SJ, Li YC, Chen YL, Ko HC. Critical factors for the adoption of mobile nursing information systems in Taiwan: the nursing department administrators’ perspective. J Med Syst. 2009;33:369–377. 7. Ammenwerth E, Iller C, Mahler C. IT-adoption and the interaction of task, technology and individuals: a fit framework and a case study. BMC Med Inform Decis. 2006;6(3). doi:10.1186/14726947-6-3. Accessed September 2012. 8. Wu IL, Li JY, Fu CY. The adoption of mobile healthcare by hospital’s professionals: an integrative perspective. Decis Support Syst. 2011;51:587–596. 9. Yen DC, Wu CS, Cheng FF, Huang YW. Determinants of users’ intention to adopt wireless technology: an empirical study by integrating TTF with TAM. Comput Hum Behav. 2010;26:906–915. 10. Schepers J, Wetzels M. A meta-analysis of the Technology Acceptance Model: investigating subjective norm and moderation effects. Inform Manage. 2007;44:90–103. 11. Goodhue DL, Thompson RL. Task-technology fit and individual performance. MIS Q. 1995;19(2):213–236. 12. Goodhue DL. Understanding user evaluations of information systems. Manage Sci. 1995;41(12):1827–1844. 13. Goodhue DL, Klein BD, March ST. User evaluations of IS as surrogates for objective performance. Inform Manage. 2000;38:87–101. 14. Lepanto L, Sicotte C, Lehoux P. Assessing task-technology fit in a PACS upgrade: do users’ and developers’ appraisals converge? J Digit Imaging. 2011;24:951–958. 15. Kilmon C, Fagan MH, Pandey V, Belt T. Using the task technology fit model as a diagnostic tool for electronic medical records systems evaluation. Issues Inf Syst. 2008;IX(2):196–204. 16. Goodhue DL. Development and measurement validity of a tasktechnology fit instrument for user evaluations of information systems. Decis Sci. 1998;29(1):105–138. 17. Gagnon MP, Desmartis M, Labrecque M, et al. Systematic review of factors influencing the adoption of information and communication technologies by healthcare professionals. J Med Syst. 2012; 36(1):241–277. 18. Zigurs I, Buckland BK. A theory of task/technology fit and group support systems effectiveness. MIS Q. 1998;22(3):313–334. 19. Dishaw MT, Strong DM. Supporting software maintenance with software engineering tools. J Syst Software. 1998;44(2):107–120. 20. Klopping IM, McKinney E. Extending the Technology Acceptance Model and the task-technology fit model to consumer e-commerce. Inform Technol Learn Perform J. 2004;22(1):35–48. 21. Cane S, McCarthy R. Analyzing the factors that affect information systems use: a task-technology fit meta-analysis. J Comput Inform Syst. 2009;50(1):108–123. 22. Lin TC, Huang CC. Understanding knowledge management system usage antecedents: an integration of social cognitive theory and task technology fit. Inform Manage. 2008;45:410–417. 23. Chatterjee S, Chakraborty S, Sarker S, Sarker S, Lau FY. Examining the success factors for mobile work in healthcare: a deductive study. Decis Support Syst. 2009;46:620–633. 24. Doherty G, McKnight J, Luz S. Fieldwork for requirements: frameworks for mobile healthcare applications. Int J Hum Comput Stud. 2010;68:760–776. 25. Tsiknakis M, Kouroubali A. Adoption of innovative eHealth services: a case study employing the FITT framework. Int J Med Inform. 2009;78(1):39–52.
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26. Liang TP, Huang CW, Yeh YH, Lin B. Adoption of mobile technology in business: a fit-viability model. Ind Manage Data Syst. 2007;107(8):1154–1169. 27. Ammenwerth E, Buchauer A, Bludau B, Haux R. Mobile information and communication tools in the hospital. Int J Med Inform. 2000;57:21–40. 28. Varshney U. Pervasive healthcare and wireless health monitoring. Mobile Netw Appl. 2007;12(2-3):113–127. 29. Dishaw MT, Strong DM. Extending the Technology Acceptance Model with task-technology fit constructs. Inform Manage. 1999;36(1):9–21. 30. Siau K, Shen Z. Mobile healthcare informatics. Med Inform Internet. 2006;31(2):89–99. 31. Strong DM, Dishaw MT, Bandy DB. Extending task technology fit with computer self-efficacy. ACM SIGMIS Database. 2006;37(2–3): 96–107. 32. Gebauer J, Shaw MJ, Gribbins ML. Task-technology fit for mobile information systems. J Inform Technol. 2010;25(3):259–272. 33. Wu JH, Wang SC, Lin LM. Mobile computing acceptance factors in the healthcare industry: a structural equation model. Int J Med Inform. 2007;76:66–77.
34. Liu Y, Lee Y, Chen ANK. Evaluating the effects of task-individualtechnology fit in multi-DSS models context: a two-phase view. Decis Support Syst. 2011;51:688–700. 35. Chin WW, Marcolin BL, Newsted PR. A partial least square latent variable modeling approach for measuring interaction effects: results from a Monte Carlo simulation study and an electronic-mail emotion/adoption study. Inform Syst Res. 2003;14(2):189–217. 36. Ringle CM, Wende S, Will S. SmartPLS 2.0 (M3). Hamburg, Germany: Beta; 2005. http://www.smartpls.de. Accessed September 2012. 37. Xu C, Ryan S, Prybutok V, Wen C. It is not for fun: an examination of social network site usage. Inform Manage. 2012;49:210–217. 38. Larsen TJ, Sorebo AM, Sorebo O. The role of task-technology fit as users’ motivation to continue information system use. Comput Hum Behav. 2009;25:778–784. 39. Gurses AP, Xiao Y, Hu P. User-designed information tools to support communication and care coordination in a trauma hospital. J Biomed Inform. 2009;42(4):667–677. 40. Alsos OA, Das A, Svanæs D. Mobile health IT: the effect of user interface and form factor on doctor-patient communication. Int J Med Inform. 2012;81:12–18.
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& Vol. 32, No. 3, 129–137 & Copyright B 2014 Wolters Kluwer Health | Lippincott Williams & Wilkins
F E A T U R E A R T I C L E
Mobile Nursing Information System Utilization The Task-Technology Fit Perspective TUNG-CHENG LIN, PhD
Nursing tasks are information-intensive. Nurses must regularly access patient information to accomplish their patient care planning, implementation, and evaluation tasks.1 Furthermore, ready access to accurate patient, physician, and institutional information is critical to ensuring care quality.2 Therefore, the ability of nursing information systems (ISs) to update patient-condition information in real time is critical to support nurses in their daily tasks.3 Today, advanced-technology portable medical devices facilitate effective point-of-care decisions by providing improved access to information, streamlining workflows, and promoting evidence-based practice.4 The mobile nursing IS (MNIS) integrates laptop computers, tablet computers, and PDAs via wireless communications and networks. The MNIS gives nurses information portability and instant mobile access to hospital IS data, to increase efficiency and effectiveness in the performance of regular nursing tasks.5,6 Despite the enormous benefits of MNIS, implementation of such a system goes beyond the purchase decision to involve complex interactions among user, technical, and organizational factors.5,7 Implementation of MNIS significantly affects both the daily routines of nurses and the overall management strategy of the hospital.7 Mobile nursing IS is a subset of mobile health information systems (MHIS) which apply mobile information and wireless telecommunication technologies to healthcare. However, evidence indicates that MHIS adoption is low among Taiwan medical professionals.8 The Technology Acceptance Model (TAM) and TaskTechnology fit (TTF) are two widely used theoretical models that address new technology adoption behavior.9 The TAM assumes that perceived usefulness and perceived ease of use significantly affect the willingness of users to adopt a
Task-technology fit theory considers how technology may best be deployed to support individuals and facilitate the completion of tasks. This study separates the fit construct into the two realms of task-technology fit and technologyindividual fit and integrates organization readiness with the objective of investigating the effectiveness of mobile nursing information systems in terms of helping nursing staff to accomplish daily clinical tasks. Study participants were clinical professionals with system usage experience who work at one medical center. Results indicated that technologyindividual fit is the factor that most strongly influences usage, followed respectively by task-technology fit and organization readiness. Therefore, strategies designed to implement mobile nursing information systems should focus greater effort on fitting the system to system users by making these systems easy to learn and use, and training easy to complete. System functions should not only facilitate accomplishment of daily clinical tasks such as quickly obtaining information and accurate data but also be portable and provide a user-friendly, easy-to-operate interface. Organizational readiness, the commitment and support of top management, and nursing staff willingness to learn and use the new system are also important factors that influence system usage. KEY WORDS Mobile nursing information system & Task-technology fit & Technology-individual fit & Technology use
technology. Because it is easy to understand and apply, TAM has been used to investigate many technology fields, including job and non–job-related topics, microcomputer and non–microcomputer topics, and participants including students and nonstudents.10 The TTF proposed by Author Affiliation: Department of Information Management, National Taipei University of Nursing and Health Sciences, Taipei, Taiwan. The author has disclosed that he has no significant relationships with, or financial interest in, any commercial companies pertaining to this article. Corresponding author: Tung-Cheng Lin, PhD, No. 365, Ming Te Rd, Peitou District, Taipei City 11219, Taiwan, ROC ([email protected]). DOI: 10.1097/CIN.0000000000000039
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Goodhue and Thompson11 and Goodhue12 focuses on the correspondence among task requirements, individual abilities, and technology functionality. Therefore, TTF is more applicable than TAM in mandatory-use situations that investigate task characteristics and the appropriateness of a particular technology to either business or personal requirements.7,13 In the healthcare context, TTF has been used to investigate factors that influence the adoption of health-related technologies such as MNIS,5 picture archiving and communications systems,14 and electronic medical records systems.15 However, these works have all focused on the fit between task and technology. Because the TTF also considers the fit between technology and individual abilities,11–13,16 Goodhue et al13 further decomposed TTF into two categories, technology fit with task and technology fit with individual. Moreover, because technical systems are embedded into socialorganizational environments,7 organizational factors such as training support, senior management commitment, and user participation also influence the adoption of information technology (IT) by healthcare professionals.6,17 It is imperative that healthcare providers in Taiwan adopt and use MNIS. Therefore, this study applies TTF theory and follows the approach of Goodhue et al13 in subdividing the concept of fit into two components: the clinical tasks– MNIS technology fit (TaTeF) and the MNIS technology– individual fit (TeIF). The developed research model further considers organization readiness to explore MNIS usage more comprehensively.
LITERATURE REVIEW Mobile Nursing Information System Nursing is a highly mobile profession because nurses regularly move among various wards, offices, conference rooms, and outpatient clinics.3 Healthcare information that is resident in patients and clinicians must be available and accessible across the nursing workspace.2 The MNIS uses mobile devices and communication systems to integrate all nurse IS functions into a mobile format that allows pointof-care portability to patient bedsides.2 The MNIS should handle patient information management, assessment, diagnosis, treatment, disposition, and discharge functions as well as extended functions to maximize nursing-job effectiveness and reduce the risk of medication errors.3
Task-Technology Fit Theory The TTF theory, proposed by Goodhue and Thompson11 and Goodhue,12 links the concepts of utilization and fit. A precise definition of TTF has been given both as ‘‘The 130
correspondence between task requirements, individual abilities, and the functionality of the technology’’11(p217) and ‘‘the extent that technology functionality matches task requirements and individual abilities.’’12(p1829) However, the TTF label and the multifaceted definition of TTF of Goodhue and Thompson are easily confused. For example, the two statements a ‘‘more accurate label of fit construct would be task-individual-technology fit, but the simpler TTF (task-technology fit) label is easier to use’’ and ‘‘TTF is the degree to which a technology assists an individual in performing his or her portfolio of tasks’’11(p216) seem to conflict with the precise definition. Due to the simpler TTF label, many TTF-based studies, including those by Yen et al,9 Zigurs and Buckland,18 Dishaw and Strong,19 and Klopping and McKinney,20 defined fit as the match between task needs and technology characteristics. In healthcare settings, Hsiao and Chen,5 Lepanto et al,14 and Kilmon et al15 also concentrated on the fit between clinical tasks and health-related technology. Ammenwerth et al7 further developed the FITT framework (fit between individuals, tasks, and technology). However, their work proposed only the conceptual framework and did not develop relevant measures. Cane and McCarthy21 conducted a meta-analysis to investigate studies related to TTF. They found significant interstudy differences in terms of the TTF definition, the research methodology, and the measurements used.21 For example, Goodhue and Thompson11 and Goodhue12 treated the fit construct as a multidimensional construct and measured it using 32 and 34 items. In contrast, Yen et al,9 Klopping and McKinney,20 and Lin and Huang22 treated the fit construct as a uniconstruct and measured it using four to eight items. In the healthcare setting, Hsiao and Chen5 treated fit as a multidimensional construct, with dimensions including information identification, information acquisition, information integration, and interpretation. Furthermore, Lepanto et al14 adapted 25 items from Goodhue’s scale. Finally, Kilmon et al15 adapted 12 items from Goodhue’s scale and used one item for each dimension. These examples highlight the lack of uniformity in terms of both TTF definition and research methodology.
RESEARCH FRAMEWORK Clinical Task Characteristics, Mobile Nursing Information System Characteristics, and Their Fit Tasks are the actions taken by individuals to turn input into output.11 Task characteristic facets include interdependence, complexity, variety, nonroutineness, and difficulty.11,12 Clinical tasks also share the characteristics of complexity, variety, and interdependence. Examples of
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complexity include changes in patient profiles and new legal documentation requirements and the necessity for healthcare professionals to communicate and coordinate with one another.7,23 An example of variety is the following: strategies for the surgical/nonsurgical treatment of cancer should consider various options such as x-ray therapy, biopsy, bronchoscopy, computer tomography, magnetic resonance imaging, and positron emission tomography scan.24 An example of interdependence is the following: patient information should be shared across the healthcare team to foster the discussion and collaboration necessary to elicit and implement optimal solutions. Technology provides the tools for individuals to perform required tasks. Computer systems are always tools in the realm of IT.11 Usability is an important characteristic to consider in any IS implementation.25 Mobility and portability are significant features of mobile ISs, which typically comprise portable devices, wireless networks, and applications.26 Mobile ISs are particularly suited to healthcare applications because of the highly mobile nature of clinical tasks in the healthcare environment.27 An MNIS uses a mobile IS platform. Patient data accessed via an MNIS should be appropriate to both the needs of the clinical routine and the needs of physicians. To effectively support clinical tasks, this system should be highly reliable and user-friendly and provide regular access to current and past medical information.28 Many studies support the statement that task and technology characteristics influence fit in TTF.9,22,29 The MNIS enables clinicians and nurses to access medical information immediately and handle core clinical tasks such as requesting services, providing care, accessing databases, tracking inventory, communicating, and exchanging data via a mobile platform.30 Through the MNIS, healthcare professionals may accomplish clinical tasks across locational, temporal, and contextual boundaries.24 Based on the above, the study proposed the following hypotheses: H1: Clinical task characteristics are positively associated with fit between clinical task characteristics and MNIS (hereafter TaTeF). H2: MNIS characteristics are positively associated with fit between clinical task characteristics and MNIS (TaTeF).
The Computer Self-Efficacy of Nurses and the Fit With Mobile Nursing Information System Self-efficacy or computer self-efficacy is an important IS characteristic. Computer self-efficacy reflects an individual’s ability to use a computer.31 High computer self-efficacy users are typically able to find and access required data and obtain more benefit from ISs.12,29 Computer self-efficacy has been reported to influence TTF in the implementation
of knowledge management systems as well as the adoption of mobile technologies and mobile-hospital IS usage.22,32,33 The TTF theory may be subdivided into technology fit for the individual and technology fit for the usage context.13,25 Item measurements for TTF universally include ease of use and training, reflecting the degree of fit between technology and individuals.11,12 For medical professionals, Tsiknakis and Kouroubali25 also indicated ease of use as a key technology fit problem with individuals when implementing innovative new medical technologies. Based on the above, the study proposed the following additional hypotheses: H3: MNIS characteristics are positively associated with fit between MNIS and nursing professional computer selfefficacy (hereafter TeIF). H4: Nursing professional computer self-efficacy are positively associated with fit between MNIS and nursing professional computer self-efficacy (TeIF).
Task-Technology Fit, Technology-Individual Fit, and System Usage In the TTF model, fit affects performance positively; a closer fit between technology and user needs facilitates higher performance due to improvements in efficiency, effectiveness, and quality.11–13 In terms of TTF, empirical studies have shown that fit influences the usage of support tools for software, knowledge management systems, and e-learning as well as mobile technology adoption.19,22,29,32 In terms of technology-individual fit, Goodhue et al13 used an experimental design to manipulate technologyindividual fit by training to examine its relationship with performance and found that appropriate training results in faster job completion. Liu et al34 also investigated the effect of technology-individual fit within a multicriteria decision support system context, as well as used the individualtechnology fit construct to find that this fit significantly and positively affected attitudes toward the decision support system in semistructured and unstructured task settings. Based on the above, this study proposed the following additional hypotheses: H5: Fit between clinical task characteristics and MNIS is positively associated with system usage. H6: Fit between MNIS and nursing professional computer self-efficacy is positively associated with system usage.
Organizational Readiness and System Usage Organizational readiness is an important factor that affects IT implementation viability. This factor includes top management support, the IS literacy of project team members, and process reengineering.17 Tsiknakis and Kouroubali25
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investigated the implementation process of an innovative eHealth service (Regional Health Information Networks) in Greece and found managerial commitment and strategies of organizational change to be important factors. Obviously, an organization that does not provide adequate support or preparation in terms of, for example, budget, training, or promotion will delay or even inhibit effective IS implementation and adoption and fail to achieve desired performance improvements. Based on the above, this study proposed the following additional hypothesis. H7: Organizational readiness is positively associated with system usage.
After the literature was reviewed and the hypotheses were considered, the study proposed a research model comprising the seven constructs of clinical task characteristics, MNIS characteristics, nursing professional computer self-efficacy, fit between clinical task characteristics and MNIS (TaTeF), fit between MNIS and nursing professional computer self efficacy (TeIF), organizational readiness, and system usage (Figure 1).
INSTRUMENTS AND PARTICIPANTS This study adopted and modified a scale that had been applied previously in research. Table 1 summarizes the construct definitions, construct measurement variables, and construct references. Four clinical task characteristic measures included three (ill-defined, nonroutine, and interdependence with other business functions) from a scale developed by Goodhue and Thompson11 and one (interdependence with others) from Lin and Huang.22 Four MNIS characteristic measures included two (support function and service support) suggestions related to MNIS derived from Hsiao and Chen5 and two (user interface and portability)
adopted from Gebauer et al.32 The three nursing professional computer self-efficacy measures were all adopted from the scale of Wu et al33 developed to assess mobile healthcare system self-efficacy. The TaTeF measures included four items (proper level of detail, accessibility, system reliability, and accuracy) from an instrument developed by Goodhue and Thompson.11 The TeIF measures included three items (easy to learn how to use, easy to get training, and easy to use) from an instrument developed by Goodhue and Thompson.11 The organizational readiness measures included four items (managerial commitment, training support, and user involvement and user support) based on the suggestions of Tsiknakis and Kouroubali25 and the concept of Ammenwerth et al7 of IT adoption in clinical environments. The system usage measures included three items (efficiency, effectiveness, and overall performance evaluation), also adopted from the scale of Goodhue and Thompson.11 To ensure instrument content validity, initial measurement items in each construct were further revised by a fiveperson expert panel comprising two experts who worked in a medical center IT department and had MNIS implementation experience and three experts who were nurses and had MNIS usage experience. The revised questionnaire had 25 items scored using a 7-point Likert scale, with scores ranging from 1 (completely disagree) to 7 (completely agree). The study conducted a pretest of the questionnaire on 30 medical center nursing staff experienced in using a mobile nursing cart (a type of MNIS). Cronbach’s ! values for the seven constructs were .866, .890, .800, .800, .884, .924, and .948, respectively, indicating that initial items had very high internal consistency for each construct. The formal study invited medical center nursing staff with MNIS experience to volunteer to participate in this research. The MNIS in this medical center consisted of
FIGURE 1. Results of the structural model.
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T a b l e 1 Construct Definitions, Measurement Variables, and References Construct (Abbreviation)
Definition
Clinical tasks characteristic (CTC)
The characteristics of clinical tasks performed by medical professionals
Mobile nursing information system characteristic (MNISC) Nursing professional computer self-efficacy (NPCSE)
The technical features of MINSs
Task-technology fit (TaTeF)
Organizational readiness (OR)
Degree to which MNIS capabilities match clinical tasks Degree to which MNIS capabilities match with nursing professional requirements The readiness of an organization to implement MNIS
System usage (SU)
The efficacy of MNIS
Technology-individual fit (TeIF)
A nursing professional’s confidence in his/her ability to use MNIS to execute clinical tasks
two primary elements: (1) a tablet personal computer (PC) through which users could access the nursing IS, perform nursing evaluation tasks, adjust nursing staff schedules, check outpatient clinic status, and access online bulletins and daily agenda sheets and (2) a mobile e-nursing cart installed with a tablet PC and nurse IS with which nursing staff could do evaluations, set caring goals, deliver treatments, and conduct related nursing activities all at patients’ bedsides via a wireless network. The cart could connect with nursing station in real time via the nurse IS.
Measurement Variables Ill-defined, nonroutine, dependent on cooperation from other teams, dependent on cooperation from colleagues User interface, portability, retrieve information when needed, service support MNIS may be operated without external guidance, MNIS may be operated if user has no previous MNIS experience, MNIS may be operated if user has previous experience on a similar system Appropriate level of detail, accessibility, system reliability, and accuracy Easy to learn to operate, easy to get training, easy to use
Management commitment, training support, user involvement, user support Efficiency, effectiveness, and overall performance evaluation
References Goodhue and Thompson11 and Lin and Huang22 Hsiao and Chen5 and Gebauer et al32 Wu et al33
Goodhue and Thompson11 Goodhue and Thompson11
Ammenwerth et al7 and Tsiknakis and Kouroubali25 Goodhue and Thompson11
two phases: measurement model and structural model.8 In the measurement model, confirmatory factor analysis was used to assess the psychometric properties of all constructs for reliability and validity. The structural model examined the causal relationships among the constructs in the research model. To verify these relationships, the standardized path coefficients and their statistical significance were used as estimates for testing the hypotheses and R2 was examined to assess the predictive validity of the model.
Ethical Considerations
Data Analysis This study used IBM SPSS 20.0 (IBM, Armonk, NY) and SmartPLS 2.0 (partial least squares [PLS]) for statistical analysis. The PLS method is a component-based structural equation modeling technique35 that does not require multivariate data normality and typically allows smaller sample sizes.8 The PLS method is best suited to assessing the validity of antecedent variables.22 Because the sample size was relatively small (n = 144) and the study purpose was to verify the factors that influence MNIS usage, this study chose SmartPLS 2.0, developed by Ringle et al,36 to analyze data. The study conducted model fit evaluation in
All participants were assured anonymity and researchers informed them that participation was wholly voluntary and that they could withdraw at any time.
RESULTS Demographics The study collected 144 valid questionnaires. Participants were all female nurses; two-thirds (92, 63.9%) held
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baccalaureate degrees, with most of the remainder (41, 28.5%) holding college degrees; all (144, 100%) had computer usage experience; 59.0% (85) had worked for 10 years or more; and 41.0% (59) had worked for 10 years or more in the healthcare industry. In terms of grade level, 9.7% (14) were N0*, 20.1% (29) were N1, 45.8% (66) were N2, 11.8% (17) were N3, 6.3% (9) were N4, and 6.3% (9) were in other level categories; 96.5% (139) had mobile device usage experience (eg, smartphone, tablet PC, PDA); and 93.1% (134) held a positive attitude toward MNIS implementation.
Measure Model Assessment This study assessed convergent validity using factor loadings for all items (criterion 90.7), composite reliability (CR; criterion 90.8), and average variance extracted (AVE; criterion 90.5).8 In terms of discriminant validity, the study compared the square root of the AVE with correlations among constructs. A square root for the AVE greater than the correlation with other constructs indicated good discriminant validity.8 Results showed that factor loading values for all items were between 0.773 and 0.956; CR values for all constructs were between 0.887 and 0.966; and AVE values for all constructs were between 0.663 and 0.904. All obtained values were above the suggested threshold value, indicating good convergent validity (Table 2). Discriminant validity was obtained by comparing the square root of the AVE (bold figures on the diagonal) with correlations among constructs. Results indicated each construct more closely related to its own measures than those of other constructs, supporting discriminant validity (Table 3). This study used Harman’s one-factor test to examine common method variance.37 Results found multiple factors, with the first accounting for 42.09% of variance, lower than the 50% threshold value suggested by Xu et al.37
Structure Model Assessment and Hypothesis Testing The PLS technique investigated the structural model by examining the effects among the seven constructs. Figure 1 shows the standardized path coefficients among the constructs and the explained construct variances (R2 value) for the conceptual model. As hypothesized, the paths from clinical task characteristics to TaTeF (H1) and from MNIS characteristics to TaTeF (H2) were found to be positive and significant, with path coefficients of 0.352 (t = 4.762, *N0-N4: nursing ladder system grade designations used in Taiwan: N0, new nurse within 3 years; N1, able to provide general patient care; N2, able to provide critical care; N3, able to handle quality assurance; N4, able to handle administrative responsibilities.
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P G .001) and 0.298 (t = 4.550, P G .001), respectively. The paths from MNIS characteristics to TeIF (H3) and from nursing professional computer self-efficacy to TeIF (H4) had strong significant and positive effects, with path coefficients of 0.354 (t = 5.531, P G .001) and 0.193 (t = 2.849, P G .01), respectively. The paths from TaTeF to MNIS usage (H5), from TeIF to MNIS usage (H6), and from organization readiness to MNIS usage (H7) were also found to be positive and significant, with path coefficients of 0.300 (t = 3.839, P G .001), 0.362 (t = 4.250, P G .001), and 0.227 (t = 2.267, P G .01), respectively. Hence, hypotheses H1 to H7 were supported. Furthermore, the R2 value of 0.553 demonstrated that TaTeF, TeIF, and organization readiness each explained a significant amount of MNIS usage variance.
DISCUSSION Goodhue and Thompson11 and Goodhue12 proposed TTF to describe how ISs are used. Because TTF considers not only technology and users but also IT system support tasks,21 the theory has been used in various contexts, including healthcare. However, Hsiao and Chen,5 Lepanto et al,14 and Kilmon et al15 focused only on the TTF when exploring the adoption of IT in the healthcare setting. Although Ammenwerth et al7 extended the TTF model to consider TTF, technology-individual fit, and taskindividual fit to better understand IT adoption in healthcare settings, the study lacked measurement items. This study focused on technology and took the further step of separating TTF into TTF and technology-individual fit to gain more precise measurements. This approach was supported by empirical data. This study further examined the pure TTF model in which model indicators of TTF were all indicators of TaTeF and TeIF in the proposed model (decomposing TTF into TaTeF and TeIF) by the same dataset. The pure TTF model earned a lower R2 value than the proposed model (0.428 vs 0.553), indicating that the proposed model provides superior explanatory power. To provide a stronger theoretical basis to explore factors of influence in different IT application contexts, many researchers have previously integrated TTF with other theories such as TAM,9,20,29 the postacceptance model,38 and social cognition theory.22 However, the total effect of TTF was no longer the strongest factor after integration. Positing that TTF, perceived usefulness, and perceived ease of use all influence attitudes toward use, Dishaw and Strong29 integrated TTF with TAM to explain software utilization and found that tool experience had a stronger effect on actual tool use than TTF did. Klopping and McKinney20 combined TTF and TAM to predict online shopping activity and found that perceived usefulness affected intention to use more than TTF did. Lin and Huang22 integrated the social cognitive theory and TTF to determine
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T a b l e 2 Reliability and Convergent Validity Constructs CTC
MNISC
NPCSE
TaTeF
TeIF
OR
SU
Items
Mean
SD
Factor Loading
Cronbach’s !
CR
Average Variance Extracted
CTC_1 CTC_2 CTC_3 CTC_4 MNISC_1 MNISC_2 MNISC_3 MNISC_4 NPCSE_1 NPCSE_2 NPCSE_3 TaTeF_1 TaTeF_2 TaTeF_3 TaTeF_4 TeIF_1 TeIF_2 TeIF_3 OR_1 OR_2 OR_3 OR_4 PI_1 PI_2 PI_3
6.160 6.042 6.063 5.910 6.465 6.493 6.576 6.382 5.333 5.236 5.215 5.708 5.840 5.819 5.854 5.708 5.819 5.819 5.792 5.813 5.646 5.778 6.035 6.076 6.042
0.799 0.792 0.830 0.827 0.647 0.626 0.621 0.748 0.908 0.901 0.976 0.844 0.754 0.790 0.766 0.923 0.929 0.951 0.876 0.844 1.067 1.067 0.788 0.794 0.792
0.832 0.806 0.825 0.795 0.884 0.918 0.937 0.827 0.954 0.953 0.944 0.845 0.922 0.935 0.923 0.882 0.956 0.944 0.826 0.773 0.825 0.863 0.955 0.942 0.936
.831
0.887
0.663
.914
0.940
0.797
.947
0.966
0.904
.927
0.949
0.822
.919
0.949
0.861
.840
0.893
0.676
.939
0.961
0.892
Abbreviations: CTC, clinical task characteristics; MNISC, mobile nursing information system characteristics; NPCSE, nursing professional computer self-efficacy; TaTeF, task-technology fit; TeIF, technology-individual fit; OR, organization readiness; SU, system usage.
the key factors affecting knowledge management systems usage and found the influence of knowledge management systems self-efficacy was greater than TTF. Yen et al9 integrated TAM and TTF models to elicit determinants of organizational users’ intention to use wireless technology and found that the combined effect of perceived usefulness and perceived ease of use was greater than that of TTF. The study found that technology-individual fit had a stronger effect on MNIS usage than TTF did.
Gurses et al39 suggested that a system designed to facilitate nursing tasks should be compatible with the mobile nature of such task and support quick information processing in its design. Alsos et al40 also argued the importance of using an attention-easy user interface to ensure that physicians continue to rely more on their own cognitive resources than the device for patient care. Research findings are consistent with these criteria. The characteristics of MNIS are one important predictor of TTF and
T a b l e 3 Discriminant Validity and Correlations CTC MNISC NPCSE TaTeF TeIF OR SU
CTC
MNISC
NPCSE
TaTeF
TeIF
OR
SU
.814 .491 .387 .498 .320 .342 .449
– .893 .278 .471 .396 .454 .504
– – .951 .309 .277 .341 .408
– – – .907 .523 .495 .606
– – – – .928 .580 .640
– – – – – .822 .589
– – – – – – .944
Diagonal values (in bold): square root of AVE; nondiagonal values: correlations among constructs. Abbreviations: CTC, clinical task characteristics; MNISC, mobile nursing information system characteristics; NPCSE, nursing professional computer self-efficacy; TaTeF, task-technology fit; TeIF, technology-individual fit; OR, organization readiness; SU, system usage.
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technology-individual fit. In the proposal model, MNIS characteristic indicator addresses system portability and user interface. Organizational readiness significantly affects MNIS usage. Ammenwerth et al7 argued that because technical systems are embedded in the social-organizational environment, organizational change necessarily accompanies IT implementation. Tsiknakis and Kouroubali25 also indicated that IT adoption or diffusion requires work practice adaptation, reorientation, and organizational change, especially in the healthcare domain. This suggests that organizational change factors such as senior management support, training, and user involvement are important to IT adoption.
CONCLUSIONS The TTF provides an important theoretical basis for understanding the behavior of users with regard to adopting new or implemented IT. The current study expands upon the original TTF proposed by Goodhue and Thompson11 by considering organizational readiness and dividing the TTF construct into distinct technology-task fit and technologyindividual fit constructs. Results show that TTF, technology-individual fit, and organizational readiness each significantly affect MNIS adoption and usage. This study also found that the technologyindividual fit has significantly more influence than the technology-task fit on MNIS performance. This finding is consistent with other studies that integrated TTF with other theories and found that TTF is not the most important factor of influence on system usage. Empirical results in the current study suggest that new system implementation should focus more effort on fitting the system to system users by making it easy to use, easy to learn, and easy to complete training. In terms of technology, MNIS functions not only support daily clinical tasks such as accessing information and acquiring accurate data but also provide portability and a user-friendly interface. In terms of users, nursing professionals should have a level of computer self-efficacy adequate to operate the MNIS and its various functions. Organizational readiness is an important factor of influence on MNIS usage. Therefore, senior hospital administrators should provide commitment and support sufficient for successful MNIS implementation. Nursing staffs should also be actively involved in the implementation stage to realize full system benefits.
Acknowledgments The author would like to thanks Ms Hsu, Hsiao-Wen for helping in sending out and collecting questionnaires. 136
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