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A R T I C L E

The Technology Acceptance Model Predicting Nurses’ Intention to Use Telemedicine Technology (eICU)

YANIKA KOWITLAWAKUL, PhD, RN

BACKGROUND/SIGNIFICANCE

Information technology has been used in healthcare de- livery systems to improve patient safety and patient care outcomes worldwide.1–3 The importance of infor- mation technology was recognized by the Institute of Medicine4 in 2000 when they released the report entitled, To Err Is Human; the report recommended increased efforts to incorporate information technology into the de- livery of patient care, and since that time, there has been a remarkable effort on the part of many organizations (Leapfrog Group, the National Patient Safety Foundation, the Institute for Healthcare Improvement, and the Joint Commission) to improve patient safety by supporting the use of information technology. All of these organizations have encouraged the implementation of information tech- nology to prevent human error.5

A high incidence of adverse events and medical errors has been found in critical care settings (ICUs).6–8 The Leapfrog Group, the National Quality Forum, and the Agency for Healthcare Research and Quality have all recommended that the ICUs be staffed exclusively with board-certified critical care physicians (intensivists) who will respond immediately to provide patient manage- ment, thus decreasing medical errors and adverse events and reducing hospital mortality rates.7,8

A shortage of critical care physicians and nurses makes it difficult to comply with the recommendation. During the night shift and weekend hours, it is more difficult to have critical care physicians covering for the ICU patients.8 Therefore, it has been proposed that tele- medicine technology, eICU (remote ICU or electronic

ICU) be used as a possible alternative solution that allows critical care nurses and physician intensivists to monitor ICU patients from off-site locations. The rationale is that patients can be more intensively monitored, thus decreas- ing adverse events and improving patient outcomes.5,6,8

As of 2008, this technology, eICU, was implemented in 28 states covering more than 200 hospitals and 40 healthcare systems.9 According to several studies,6,8–11

the eICU technology system has many advantages over

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CIN: Computers, Informatics, Nursing & Vol. 29, No. 7, 411–418 & Copyright B 2011 Wolters Kluwer Health | Lippincott Williams & Wilkins

The purposes of this study were to determine

factors and predictors that influence nurses’ in- tention to use the eICU technology, to examine the applicability of the Technology Acceptance

Model in explaining nurses’ intention to use the eICU technology in healthcare settings, and to provide psychometric evidence of the measure-

ment scales used in the study. The study involved 117 participants from two healthcare systems. The Telemedicine Technology Acceptance Model was developed based on the original Technology Ac-

ceptance Model that was initially developed by Fred Davis in 1986. The eICU Acceptance Survey was used as an instrument for the study. Content

validity was examined, and the reliability of the instrument was tested. The results show that per- ceived usefulness is the most influential factor that

influences nurses’ intention to use the eICU tech- nology. The principal factors that influence per- ceived usefulness are perceived ease of use, support from physicians, and years working in the

hospital. The model fit was reasonably adequate and able to explain 58% of the variance (R

2 =

0.58) in intention to use the eICU technology with

the nursing sample.

K E Y W O R D S

Acceptance & eICU & Nurses’ attitude &

Nurses’ intention & Nursing & Technology

Author Affiliation: School of Nursing, The George Washington Univer- sity, N.W. Washington, DC.

Corresponding author: Yanika Kowitlawakul, PhD, RN, School of Nursing, The George Washington University, Virginia Campus, 45085 University Drive, Suite 201K, Ashburn, VA 20147 ([email protected]).

DOI: 10.1097/NCN.0b013e3181f9dd4a

the traditional ICU systems that exist today, such as decreasing in-hospital mortality, decreasing ICU length of stay, decreasing cost, and increasing high quality of care.

The eICU unit is a secure telemedicine center where a team of critical care physicians and nurses provides oversight surveillance for the patients in off-site inten- sive care units. This monitoring utilizes various technol- ogies such as video assessment, two-way communication directly into the patient’s room, access to hospital in- formation systems, and the use of eCareManagement (VISICU Inc, Baltimore, MD). Data, including vital signs from bedside monitors, intake/output, blood glucose, laboratory results, and current medications are all in- terfaced with the computer database system. Thus, the eICU team can review all of the medical data through the computer system and have immediate communication with and access to the on-site nurses and physicians. Bedside nurses have the role of closely monitoring the patient and cooperating with the eICU team in assessment and management.

The intent of this study was to determine significant factors and predictors of nurses’ intention to use (ITU) the telemedicine technology (eICU) and to provide psy- chometric data, further supporting the evidence for the reliability and content validity of the measurement scale strategy. Nurses have been identified as computer end users in healthcare settings. In the 1980s, they were often apprehensive about integrating computer systems into their nursing practice.12 Recently, a common fear of nurses is that the information technology will take over their roles, and their roles might be replaced by machines.1 Also, nurses’ frustrations when using a new technology system have been associated with the fears of increasing work- load and an unfriendly technical system.13 Stafford et al14

conducted an ethnography study and examined the col- laborative communication between on-site (bedside) and off-site (eICU) nurses. These researchers found that the on-site nurses felt uncomfortable, as if they were being watched; some were resentful, and these nurses ques- tioned the eICU nurses about their commitment to nurs- ing practice.14

Based on the author’s critical care experiences, nurses complained when they received a telephone call from the eICU nurses. On-site nurses felt as though someone was watching over their shoulder. They complained about having to do extra work, losing autonomy, receiving contradictory orders from two different doctors (from on-site and off-site), and creating duplicate documen- tation. While several nurses had positive attitudes to- ward eICU, they struggled when they had to deal with technical problems with the computer program. As a result, some nurses were not willing to communicate with the eICU nurse and refused to take advice from the eICU team.

The successful implementation of clinical information technology systems is highly dependent on user accep- tance.15 Patient safety and quality will not be achieved if nursing staff are not willing to use the technologies.15

Therefore, it is critical to understand the readiness and willingness of hospital nurses to implement new infor- mation technology and efficiently integrate it into their practice.16

One model that has attempted to explain acceptance of technology systems is the Technology Acceptance Model (TAM); according to TAM, the intention generates the actual behavior.17 Thus, behavior should be predictable from measures of behavioral intention and other factors that influence intention directly and indirectly.18 Since re- cognizing that the ITU new technology has become a very significant issue, it is conjectured that TAM may be a useful conceptual framework for examining nurses’ ITU tele- medicine technology.19

CONCEPTUAL FRAMEWORK/ RESEARCH GAP

The TAM was initially developed by Davis et al in 1986.17

The TAM provides a framework for understanding the determinants of computer acceptance that explain user behavior with a variety of end-user populations. The the- oretical framework has the potential to identify, explain, and predict the factors, such as internal beliefs and atti- tudes, which have an effect on the intentions of tech- nology end users.17,18

The original TAM has five constructs: perceived use- fulness (PU), perceived ease of use (PEOU), attitude toward using, ITU, and actual system use.17 The key determinants of computer acceptance in TAM are the belief that the computer system will help to improve job performance (PU) and the belief that using the computer system re- quires only a minimal level of mental effort; in other words, it is easy to use.17 Those two determinants are con- sidered to be the basis for evaluating the attitudes toward using particular computer systems and ultimately generat- ing the ITU. The ITU a particular system then leads to actual end-user behavior.

The TAM has evolved over time, being used with different populations and various technology systems; the framework has been used extensively in information technology, education, and business.20–29 However, there have been few studies in healthcare settings,20,22,25,27

and even fewer with regard to nursing practice.16,25,30

While nurse researchers have focused on developing and testing instruments that measure nurses’ attitudes toward using new computer technology,1,2,12,13,31–33 very few studies have examined the ITU the technology in the practice of nursing.16,30

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PROPOSED CONCEPTUAL MODEL

A number of nursing studies have demonstrated that the primary factors influencing nurses’ attitudes toward using computers are age, years of nursing experience, and years of experience with computers.3,16,33 Based on interviews with five nursing experts in the field and pre- vious studies, three additional potential factors that in- fluence the individual’s belief and attitudes toward using telemedicine technology (eICU) were the number of years working in the hospital, support from admin- istrators, and support from physicians.16,34 According to Shoham and Gonen,16 support from the nursing ad- ministrator is a significant factor of influence on the nurses’ intentions to use the computers. Taken together, these six factors were selected as the external variables in the proposed conceptual model that underpins the current study.

The TAM provides the related constructs of PU, PEOU, attitude toward using, and ITU. PU and PEOU are the known primary key determinants for computer acceptance behaviors. Both determinants influence atti- tudes, leading to ITU and ultimately to actual individual usage behavior.17

Interestingly, according to the Pearson correlation results in this study, there were no statistically signifi- cant correlations with the three constructs that were drawn from the original TAM: age, years of experience in nursing, and years of experience with computers. The literature reveals that the relationships between the latter variables and attitudes toward computerization were unclear with most of the studies,3,15,32 having been done in different settings and with different computer programs. The eICU technology has been implemented in nursing practice for only a few years.9,14 It would appear that as technology changes and the demands of practice change, the attitudes of nurses will be shaped

and formed by their earlier interface with technology, as well as their experiences with technology in nursing education.

In the current study, the author sought and received permission from the creators of the TAM to revise the original model, and renamed it the Telemedicine TAM (TTAM; Figure 1). The TTAM utilizes four constructs drawn from the original TAM (PU, PEOU, attitude toward using, and ITU) and the three previously identified external variables (years working in the hos- pital, support from administrators, and support from physicians). Age, years of experience in nursing, and years of experience with computers were omitted from the model because they were not statistically supported by the data.

RESEARCH QUESTIONS

1. What are the relationships among the external variables,

the key constructs of TTAM, nurses’ attitude toward

computerization, and the ITU telemedicine technology

(eICU)?

2. Which variables are most influential in predicting ITU

telemedicine technology (eICU)?

3. Is the proposed hypothesized model consistent

with the empirical data in the study (in other words,

goodness-of-fit)?

METHODS

Measurement

The eICU Acceptance Survey was used as an instrument for the study. The instrument was modified from three

FIGURE 1. TTAM with the results of R2 and " values.

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original instruments, which are (1) PU and PEOU,35 (2) Nurses’ Computer Attitudes Inventory,31 and (3) ITU.20

The content validity was examined by five nursing tech- nology experts. The questionnaire for the experts con- sisted of 10 questions. The experts were asked about the content of each construct for clarity, appropriateness, the relationship of overall items, and whether those items measured the constructs. The experts were given 1 week to complete the questionnaire, and then the researcher dis- cussed the results with each expert. The results from five experts showed that they all agreed or strongly agreed on each item. Therefore, overall results of content validity for this study were satisfied.

The internal consistency of the instrument constructs was evaluated using coefficient ! (Cronbach !), which showed ranges of .91 to .96 with a total coefficient ! of .96 (Table 1).

Sample

Potential nursing participants were RNs, employed in critical care units, where the eICU technology had not yet been implemented. The nurses employed in these units had a nurse-to-patient ratio of 1:2. Nurses in this study were caring for patients who were in critical con- ditions that might require life support, such as a ven- tilator or vasopressive medications. Nurse managers or directors of the critical care units and the nurses who had worked in the unit that has implemented eICU tech- nology before were excluded.

There were 139 potential participants in two metro- politan healthcare systems; of these, 131 nurses partici- pated for a 94% response. Of the 131 responses, three (2.16 %) were excluded because of incomplete answers, and 11 (7.19%) did not meet the criteria (two partici- pants were managers, and nine participants were not employed in the critical care units). The final sample was composed of 117 nurses. Given a moderate effect size (0.13) with an ! of .05 and a power level of 0.80, the sam- ple of 117 was adequate for multiple regression and path analysis based on Marsh and colleagues (1988) as cited in Kelloway,36 Bollen,37 and Harris and Schaubroek.38

Data Collection/Data Analysis

The study was implemented after approval by the human subjects review boards of the participating university and the two healthcare systems. Nurses who indicated a willingness to participate were provided a cover letter, an informed consent form, and the study questionnaire in a personal meeting with the researcher. During the meeting, the researcher explained the purpose of the study and reviewed the informed consent form and the questionnaire. Signed participant consent forms were placed in an envelope that was kept separate from the questionnaire. Participants were instructed to complete the questionnaire and return it to the researcher in a sealed envelope. Once collected, both consent forms and questionnaires were stored separately in a locked drawer.

Descriptive statistics, regressions, and multiple re- gressions (for path analysis) were accomplished with the SPSS version 15.0 data analysis software (SPSS Inc, Chicago, IL). LISREL 8.8 (Scientific Software Interna- tional, Lincolnville, IL) was used to test the goodness of fit with the proposed model (TTAM). Statistical sig- nificance for all of the analyses was set as P G .05. Data screening was performed for missing data and outliers, and the assumptions of multiple regression analysis meth- ods were considered.39,40

RESULTS

Descriptive Analysis

The average age of the participants was 35.4 (SD, 9.37) years. There were 107 women (91.5%) and 10 men (8.5%). Most of the participants were employed on the day shift (56 [47.9%]), whereas 35 (29.9%) were em- ployed on the night shift, and 26 (22.25%) worked both day and night shifts. Sixty-four of the participants (54.7%) had bachelor degrees in nursing, 31 (26.5%) had associate degrees in nursing, and three (2.6%) had graduate degrees in nursing. Most of the participants (54 [46.2%]) had heard about eICU from nurses who had prior experience with eICU technology; however, 39 (33.3%) had heard about eICU from both nurses who had prior experience and nurses who had not used the technology. Only one participant had learned about eICU technology from the Internet.

Most participants (101 [86.3%]) had never attended a conference on eICU technology, and 98 (83.35%) had never been trained to use eICU technology. There were only 16 participants (13.7%) who reported that they had attended a conference on eICU technology and 19 (16.2%) who had been trained to use the eICU tech- nology before they worked in their current units. Most of the participants (110 [94%]) reported that there were

T a b l e 1

Reliability in This Study

Constructs No. of Items

Item Mean Coefficient !

PU 7 2.8 .96 PEOU 6 3.4 .94 Nurse attitudes toward eICU (NATE)

21 3.3 .91

Intention to use 6 3.2 .95 Total 40 3.2 .96

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technology support personnel available in their units, while seven (6.0%) reported there were no available sup- port personnel.

The participants had worked in nursing an average of 10.44 (SD, 9.13) years, and in the present hospital for 6.83 (SD, 6.98) years. The average number of years for working in the critical care units was 7.64 (SD, 7.82) years. The average number of years that nurses had known about eICU was 2.41 (SD, 1.45) years, and the average number of years working with any type of computer technology was 14.77 (SD, 5.97) years.

Path Analysis

A path analysis was performed to determine the causal effects among the variables in the proposed model, TTAM. Multiple regressions identified four paths based on the assumptions of the causal closure of the path diagram (Figure 1).

According to the results of regression 1, the follow- ing path coefficients were statistically significant: years working in the hospital to PU (" = j.200, P = .010), support from physicians to PU (" = .270, P = .003), and PEOU to PU (" = .420, P = .000). The path coefficient of support from administrators to PU was not signifi- cant (" = .051, P = .576).

The results of regression 2 provided a path coefficient from support from administrators to PEOU that was sta- tistically significant (" = .242, P = .009).

The results of regression 3 provided two path coef- ficients that were statistically significant: from PU to atti- tudes toward using (" = .297, P = .000) and from ease of use to attitudes toward using (" = .466, P = .000).

Finally, the results of regression 4 provided two path coefficients that were statistically significant: from PU to ITU (" = .506, P = .000) and from attitudes toward using to ITU (" = .364, P = .000).

Goodness-of-Fit/Fit Indices

LISREL provided an analysis of ‘‘fit’’ index values that was used to examine and determine the model fit for the data collected in this study. The overall model fit was guided by using multiple fit indices as suggested in the literature20,36,41–43 and is presented in Table 2. The re- sults show that the model fit was reasonably adequate. Furthermore, the LISREL program provided the results of squared multiple correlations for structural equa- tions (R2) that explain the power of the model for indi- vidual constructs.

Together, years working in the hospital, support from physicians, support from administrators, and PEOU ex- plained 35% of the variance observed in PU. Support from administrators explained only 6% of the variance

observed in PEOU. However, PU and PEOU were able to explain 44% of the variance observed in nurses’ attitudes toward the eICU technology system. Finally, PU and nurses’ attitudes toward using eICU technology were able to explain 58% of the variance observed in ITU in the eICU technology.

DISCUSSION/IMPLICATIONS

This study evaluated the usefulness of the TTAM in ex- plaining nurses’ ITU eICU technology systems. The TTAM was able to explain 58% of the variance in ITU eICU, and the ITU was predicted by PU and attitude toward using. Compared with a prior TAM study in nurs- ing settings,30 the TTAM in this study appeared to be more useful in explaining the intention of nurses to use the technology.

According to the model fit results, the model fit in- dices were within the reference range. Therefore, the TTAM in this study appears to adequately specify the intention of nurses to use the eICU technology system and has the ability to reproduce a correlation matrix with this nursing sample as did the original TAM. The suitability and applicability of TTAM in the nursing sample were confirmed as indicated by reasonable model fit indices. Nevertheless, ‘‘it is important for re- searchers to recognize that ‘model fit’ does not equate to ‘truth’ or ‘validity.’ Finding the expected pattern of correlation is a necessary but not sufficient condition for the validity of the theory that generated the model predictions.’’36(p40)

In terms of prediction, in agreement with the original TAM and previous studies20,30,35,43 PU was found to be a key determinant that has a statistically significant and strong influence on nurses’ intentions to use the eICU technology system. This suggests that nurses in criti- cal care units tend to focus on the usefulness of this

T a b l e 2

Analysis of Overall Model Goodness-of-Fit Using Common Fit Indices

Model Goodness-of-Fit Indices

Recommended

Value

Results Obtained From This Study

Goodness-of-fit index Q0.90 0.95

Adjusted goodness-of-fit index

Q0.80 0.85

Normalized fit index Q0.90 0.94

Non-normalized fit index Q0.90 0.91 Comparative fit index Q0.90 0.96 Root-mean-square

residual

e0.10 0.05

Root-mean-square error of approximation

e0.10 0.10

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technology itself. In this study, PU was significantly im- pacted by PEOU as TAM hypothesizes, contrary to what Hu and colleagues20 found, namely, that PEOU had no significant effect on PU of the telemedicine technology. However, the population of the study of Hu et al20 was not nurses, but physicians. The nature of the population might well play a role for this contradictory result.

According to the results of this study, PEOU was found to have more significant effect on nurses’ attitudes toward using the eICU technology system than PU. This result could reflect that most of the critical care nurses in the current study were familiar with using computer technol- ogy equipment (average number of years using computers was 14.77). They had already been charging, ordering, documenting, and using computers with various types of software programs. Moreover, 94% of nurses in this study reported that they had personnel support in their facilities to help them solve technical problems whenever they were struggling with new technology features or operations.

Nurses’ attitudes toward the eICU technology system were also a significant factor that predicted the ITU in this technology, even though it contributed less to pre- dicting the ITU in this technology than PU. The results demonstrated that nurses’ attitudes were also relatively important to nurses’ ITU in the eICU technology system. Nursing practice requires particular knowledge and skill in dealing with patient care within the critical care en- vironment. Nurses are very focused on their patients because those patients are acutely ill. Any new technol- ogy that appears to take the nurses away from patient care would lead to the belief that it would not be useful or helpful. The TTAM does appear to explain the fac- tors that influenced nurses’ attitudes toward using the eICU technology system in a manner different from the original TAM. This may be due to the unique character- istics of the healthcare setting and the nature of the nurs- ing profession.

According to the results, the numbers of years working in the hospital had a negative statistically significant cor- relation with PU, suggesting that nurses who worked in the hospital longer might believe less in the usefulness of the technology. Nurses who worked longer might need to receive information at the early stage of implementation. Therefore, an information and training program that out- lines the purpose, usefulness, and features of eICU tech- nology should be provided. This program would outline effective communication strategies among healthcare pro- viders to foster better communication and avoid conflicts. The information and training program would primarily focus on how the eICU technology system can help im- prove patients’ safety and outcomes.

PU was also influenced by support from physicians. Physicians had a great impact on nurses’ belief about how this technology would be beneficial for their patients.

Therefore, to increase nurses’ ITU in the eICU technology system, it is necessary to have support and cooperation from the physicians as suggested by the findings. Com- munication between physicians and nurses needs to be clear and have precise direction. A physician who em- braces computer technology must be selected as the one in charge of patient care management and should be iden- tified before the eICU technology system is implemented. Protocols that outline the plan of escalation of treatment must be clearly stated, thus enabling nurses to deliver quality care and promote patient safety.

The support from administrators, an external variable from this study, showed a significant influence on PEOU, but not a significant influence on PU. Most of the ad- ministrators or directors in critical care units were RNs. They have similar backgrounds with nurses who work at the patient’s bedside. However, the nursing administra- tors had a different focus on the eICU technology system. Their focus was on how to provide all nurses with user support and proper training before the technology was implemented. The administrators often reassure the nurses that using this technology would not be difficult and that it would improve patient outcomes.

Prior to introducing the new eICU technology sys- tem to intensive care units, administrators can increase the ITU the technology system by assessing nurses’ and physicians’ perceptions. Nurses and physicians should be involved in the planning and implementation process. Since PEOU is a main factor in predicting attitude to- ward using, nurse administrators could support this fac- tor by having on-site user training and reassuring the staff that they will have personnel support at the units at all times.

LIMITATIONS AND RECOMMENDATIONS

Participants in this study were volunteers and subject to self-selection biases. Additional research is needed to address construct validity with a larger sample size and improved model fit. The indirect path coefficient (ease of use and ITU), which is statistically significant, and the direct path coefficient (support from administrator and usefulness), which is not statistically significant, may need to have further investigation for model modification to improve the ‘‘fit’’ of the research model.

The external variables (years working in the hospital, support from administrators, and support from phy- sicians) were the primary factors that influenced the two key determinants (PU and PEOU). In the health- care setting, there might have been more than three factors that influenced those two key determinants specified in the TTAM. More investigation on external variables, such as knowledge, participation in the decision-making process, peer support, and individual awareness, is needed.

416 CIN: Computers, Informatics, Nursing & July 2011

CONCLUSION

To improve the nurses’ ITU the eICU technology system, cultivating PU and attitudes toward using this technol- ogy are important. In critical care units, nurses have high autonomy and are competent in patient care. Since patients’ outcomes are of utmost concern, it behooves administrators to support the autonomy and compe- tency of the nurses by providing them with the educa- tional opportunities that allow them to adapt to new technology and new environments.

In addition, this nursing study used and adopted a theoretical model and instruments that have been de- veloped from the discipline of social psychology. The TTAM shows promise as a valuable model for predict- ing nurses’ ITU in the eICU technology in healthcare as demonstrated by the fact that 58% of the variance (R2 = 0.58) in ITU the technology is explained by the model in this study. Furthermore, the study suggests that the TTAM has applicability in identifying, explaining, and predicting the ITU telemedicine technology in nursing practice. The replication of this study is also highly recommended.

Acknowledgments

The author thanks Dr Catharine A Kopac, associate professor at the George Washington University, and Dr George Crossman for editing the article. Also, the author thanks Dr Jean Burley Moore, Dr Heibatollah Baghi, and Dr Gregory Guagnano, George Mason Univer- sity, who have assisted me throughout the study.

REFERENCES

1. Toofany S. Nursing and information technology. Nurs Mang. 2006; 13(7):18–19.

2. Alquraini H, Alhashem AM, Shah MA, Chowdhury RI. Factors influencing nurses’ attitudes towards the use of computerized health information systems in Kuwaiti hospitals. J Adv Nurs. 2007;57(4): 375–381.

3. Davis PB, Duchene P. Putting change theory into practice with IT adoption, part I. Program Apollo for Healthcare Professionals. 2006. http://www.hhnmostwired.com/hhnmostwired/jsp/articledisplay.jsp. Accessed January 12, 2008.

4. Kohn LT, Corrigan JM, Donaldson MS. To Err Is Human: Build- ing a Safer Health System. Washington, DC: Institute of Medicine; 2000.

5. Rothschild JM, Landrigan CP, Cronin JW, et al. The critical care safety study: the incidence and nature of adverse events and serious medical errors in intensive care. Crit Care Med. 2005;33(8):1694–1700.

6. Breslow MJ, Rosenfeld BA, Doerfler M, et al. Effect of a multiple-site intensive care unit telemedicine program on clinical and economic outcomes: an alternative paradigm for intensivist staffing. Crit Care Med. 2004;32(1):31–38.

7. Spiegler P. Time of admission and mortality in the intensive care unit. Clin Pulm Med. 2004;11(2):118–119.

8. Myers MA, Reed KD. The virtual ICU (vICU): a new dimension for criti- cal care nursing. Crit Care Nurs Clin North Am. 2008;20:435–439.

9. Rajecki R. Electronic ICU monitoring: Big brother, great friend. RN Web. 2008. http://rn.modernmedicine.com/rnweb/Nursing+ Top+Story/Electronic-ICU-monitoring-Big-Brother-great-friend/ ArticleStandard/Article/detail/561190. Accessed October 2, 2009.

10. Webber D. From a distance: saving lives through remote care: a combination of eICU technology, intensivists and in-house staff- ing delivers significant improvements in ICU patient care. Health Manage Tech. 2007. http://archive.healthmgttech.com/archives/ 0307/0307saving_lives.htm. Accessed January 12, 2008.

11. Berenson RA, Grossman JM, November EA. Does telemonitoring of patients-the eICU-Improve intensive care? Health Aff. 2009. http://content.healthaffairs.org/cgi/content/full/28/5/w937?maxto show=&HITS=10&hits=10&RESULTFORMAT=&fulltext=eicu &andorexactfulltext=and&searchid=1&FIRSTINDEX=0& resourcetype=HWCIT. Accessed September 30, 2009.

12. Stronge JH, Brodt A. Assessment of nurses’ attitudes toward com- puterization. CIN. 1985;3(4):154–158.

13. Smith K, Smith V, Krugman M, Oman K. Evaluating the impact of computerized clinical documentation. CIN. 2005;23(3):132–138.

14. Stafford TB, Myers MA, Young A, Foster JG, Huber JT. Work- ing in an eICU Unit: life in the box. Crit Care Nurs Clin North Am. 2008;20:441–450.

15. Asaro PV. Measuring the effect of a computerized nursing docu- mentation system with objective measures and reported percep- tions. Ann Emerg Med. 2004;44:131–132.

16. Shoham S, Gonen A. Intentions of hospital nurses to work with computers. CIN. 2008;26(2):106–116.

17. Davis FD, Bagozzi RP, Warshaw PR. User acceptance of computer technology: a comparison of two theoretical models. Manage Sci. 1989;35(8):982–1003.

18. Mathieson K. Predicting user intentions: comparing the Technology Acceptance Model with the theory of planned behaviour. Inform Sys Res 1991;2(3):173–191.

19. Igbaria M, Zinatelli N, Cragg P, Cavaye ALM. Personal comput- ing acceptance factors in small firms: a structural equation model. MIS Q. 1997;21(3):279–305.

20. Hu PJ, Chau PYK, Lie Sheng OR, Tam KY. Examining the Tech- nology Acceptance Model using physician acceptance of telemedi- cine technology. J Manage Inform Syst. 1999;16(2):91–112.

21. Venkatesh V. Determinants of perceived ease of use: integration control, intrinsic motivation, and emotion into the Technology Acceptance Model. Inform Syst Res. 2000;11(4):342–365.

22. Handy J, Hunter I, Whiddett R. User acceptance of inter- organizational electronic medical records. Health Info J. 2001;7: 103–107.

23. Lu J, Yu CS, Liu C, Yao JE. Technology Acceptance Model for wireless Internet. Internet Res. 2003;13(3):206–222.

24. Ammenwerth E, Mansmann U, Iller C, Eichstadter R. Factors af- fecting and affected by user acceptance of computer-based nurs- ing documentation: results of a two-year study. JAMIA. 2003;10(1): 69–84.

25. Chang BL, Bakken S, Brown SS, et al. Bridging the digital divide: reaching vulnerable populations. JAMIA. 2004;11(6):448–457.

26. An JY. Correlates and Predictors of Consumers’ Health Informa- tion and Services Usage Behavior on the Internet: A Structural Equation Modeling Approach [dissertation]. New York: New York University; 2005.

27. Cheung W, Huang W. Proposing a framework to assess Internet usage in university education: an empirical investigation from a student’s perspective. Br J Educ Technol. 2005;36(2):237–253.

28. Drennan J, Kennedy J, Pisarki A. Factors affecting student atti- tudes toward flexible online learning in management education. J Educ Res. 2005. http://findarticles.com/p/articles/mi_hb3507/is_ 200507/ai_n15147680/. Accessed October 8, 2005.

29. Tung FC, Chang SC, Chou CM. An extension of trust and TAM model with IDT in the adoption of the electronic logistics infor- mation system in HIS in the medical industry. Int J Med Info. 2007; 77(5):324–335.

30. Chen J, Yang KF, Tang FI, Huang CH, Yu S. Applying the Technology Acceptance Model to explore public health nurses’ in- tentions towards Web-based learning: a cross-sectional question- naire survey. Int J Nurs Stud. 2006;45:869–878.

31. Jayasuriya R, Caputi P. Computer attitude and computer anxiety

CIN: Computers, Informatics, Nursing & July 2011 417

in nursing: validation of an instrument using an Australian sam- ple. CIN. 1996;14(6):340–345.

32. Moody L, Slocumb E, Berg B, Jackson D. Electronic health re- cords documentation in nursing: nurses’ perceptions, attitudes, and preferences. CIN. 2004;22(6):337–344.

33. Brumini G, Kovic I, Zombori D, Lulic I, Petrovecki M. Nurses’ attitudes toward computers: cross sectional questionnaire study. Croatia Med J. 2005;46(1):101–104

34. Weiner M, Gress T, Thiemann DR, et al. Contrasting views of physicians and nurses about an inpatient computer-based pro- vider order-entry-system. JAMIA. 1999;6(3):234–244.

35. Davis FD. Perceived usefulness, perceived ease of use, and user ac- ceptance of information technology. MIS Q. 1989;13(3):319–339.

36. Kelloway EK. Using LISREL for Structural Equation Modeling: A Research’s Guide. Thousand Oaks, CA: SAGE Publications, Inc; 1998.

37. Bollen KA. Structural Equations With Latent Variables. New York: Wiley Interscience; 1989.

38. Harris M, Schaubroek J. Confirmatory modeling in organiza- tional behavior, human resource management: issue and applica- tions. J Manage. 1990;16(2):337–360.

39. Mertler CA, Vannatta RA. Advanced and Multivariate Statistical Methods. 2nd ed. Los Angeles, CA: Pyrczak Publishing; 2002.

40. Tabachnick AG, Fidell LS. Using Multivariate Statistics. 4th ed. Needham Heights, MA: Allyn and Bacon; 2001.

41. Hartwick J, Barki H. Explaining the role of user participation in information systems use. Manage Sci. 1994;40(4):440–465.

42. Hoyle RH. Structural equation modeling: concepts, issues, and appli- cations. Thousand Oaks, CA: Sage; 1995.

43. Segars AH, Grover V. Re-examining perceived ease of use and usefulness: a confirmatory factor analysis. MIS Q. 1993;17(4): 517–525.

418 CIN: Computers, Informatics, Nursing & July 2011