Abstract

Objectives

Medical institutions are eager to introduce new information technology to improve patient safety and clinical efficiency. However, the acceptance of new information technology by medical personnel plays a key role in its adoption and application. This study aims to investigate whether perceived organizational learning capability (OLC) is associated with user acceptance of information technology among operating room nurse staff.

Materials and methods

Nurse anesthetists and operating room nurses were recruited in this questionnaire survey. A pilot study was performed to ensure the reliability and validity of the translated questionnaire, which consisted of 14 items from the four dimensions of OLC, and 16 items from the four constructs of user acceptance of information technology, including performance expectancy, effort expectancy, social influence, and behavioral intention. Confirmatory factor analysis was applied in the main survey to evaluate the construct validity of the questionnaire. Structural equation modeling was used to test the hypothetical relationships between the four dimensions of user acceptance of information technology and the second-ordered OLC. Goodness of fit of the hypothetic model was also assessed.

Results

Performance expectancy, effort expectancy, and social influence positively influenced behavioral intention of users of the clinical information system (all p < 0.001) and accounted for 75% of its variation. The second-ordered OLC was positively associated with performance expectancy, effort expectancy, and social influence (all p < 0.001). However, the hypothetic relationship between perceived OLC and behavioral intention was not significant (p = 0.87). The fit statistical analysis indicated reasonable model fit to data (root mean square error of approximation = 0.07 and comparative fit index = 0.91).

Conclusion

Perceived OLC indirectly affects user behavioral intention through the mediation of performance expectancy, effort expectancy, and social influence in the operating room setting.

Keywords

behavioral intention; information systems; organizational learning;

1. Introduction

Quality improvement and cost reduction are two major challenges in the health-care industry. To improve the efficiency and effectiveness, the application of computer and information technologies in the health-care industry have expanded dramatically.1, 2, 3 Although the benefits of information technology to the health-care industry are well-known, adaptation of medical personnel to new information technologies in health-care systems has proven difficult.4, 5, 6 In fact, user acceptance of information technology plays a key role in adopting and applying new technologies in medical institutions but inadequate understanding of how personnel and institutions adopt to information technology may lead to the failure of the implementation of the new technology.⁷ Accordingly, investigating user acceptance of information technology in medical institutions and its influential factors is of scientific and practical importance while new information technology is to be implemented in clinical practice.

Research on acceptance and use of information technology has been conducted extensively and generated several theoretical models. In 2003, Venkatesh et al proposed the Unified Theory of Acceptance and Use of Technology (UTAUT),⁸ which integrated most constructs in the previous studies and explained more variation in behavioral intention to use information technology than did the other models. The basic UTAUT model consists of several components associated with behavioral intention to use information technology, including performance expectancy, effort expectancy, and social influence, and the model has been successfully applied in health-care studies related to the use of information technology.7, 9 The UTAUT model provides a useful tool to assess the chances of success of new technology introductions and explore the factors affecting user behavioral intention. It is also possible to evaluate interventions, which aim to increase user inclination to adopt and use new information systems in the context of UTAUT.

Organizational learning refers to the process by which the organizations learn.¹⁰ Organizational learning can help personnel to improve skills and knowledge and provide opportunities to find out better ways of teamwork.¹¹ Because continuous training and technical support to users are necessary for the introduction of new clinical information systems, organizational learning capability (OLC) may play some role in the adoption and use of new technologies. However, the impacts of OLC on user acceptance of information technology have not yet been theoretically modeled and empirically tested in the clinical context. To fill the gap in literature, we hypothesized that perceived OLC might have both direct and indirect effects on behavioral intention to use new clinical information systems, and ventured to design this questionnaire survey to evaluate the hypothetical relationships between perceived OLC and user acceptance of information technology by operating room nurse staffs.

2. Materials and methods

This questionnaire survey was conducted at the Taipei Veterans General Hospital after obtaining the approval from the Institutional Review board (VGHIRB No. 2010-09-014-IC). A new clinical information system was introduced for patient safety and to improve operating room management in March 2010 and this survey was conducted in November 2011 to investigate user acceptance of the new clinical information system and its influential factors. The system was designed to understand the process of clinical registry for systematic check and collecting clinical information on patients treated with a particular surgical procedure. The major users of this system were nurse anesthetists and operating room nurses, and therefore they we recruited in this questionnaire survey. All participants filled in the questionnaires anonymously to maintain confidentiality.

2.1. Measurement

The questionnaire included items from major dimensions of user acceptance of information technology and OLC was drawn up after extensive literature review.8, 10, 12, 13 We followed the guidelines proposed by Beaton et al¹⁴ to translate the selected questions for the questionnaire into corresponding Chinese versions. In brief, forward translation was performed by two translators who were then working together with a recording observer to synthesize the two translations and ensure the quality of the synthetic version. Afterward back-translation was performed to confirm the agreement between the back-translated version and the original version to avoid information bias. Finally, an expert committee was held to review all the translations and verify content validity of the translated questionnaire. A five-point Likert scale was used in all items in the questionnaire (1 = strong disagreement; 5 = strong agreement). The original version of questionnaire used in this study is presented in Appendix 1.

2.2. User acceptance of information technology measurement scale

Research on the acceptance of information technology has generated many competing models, but sets of acceptance determinants in these models were never consistent.8, 9 Accordingly, Venkatesh et al tried to formulate a unified model that integrates elements across different models and then empirically validated the unified model.⁸ They theorized that three constructs would play a significant role in serving as direct determinants of behavioral intention to use information technology in their unified theory of user acceptance of information technology: performance expectancy, effort expectancy, and social influence. In their study, the internal reliability of these four constructs ranged between 0.88 and 0.92. Performance expectancy is defined as the degree to which one believes that using information technology will help to enhance work performance. Effort expectancy refers to the perceived ease of using information technology. Social influence is understood as the extent to which a user perceives that important others believe he or she should use information technology. This theoretical framework was also adopted in this study.

2.3. OLC measurement scale

We used the OLC measurement instrument developed by Chiva et al.¹⁰ OLC emphasizes the importance of promoting factors for organizational learning or the organizational inclination to learn. According to the conceptualization of this scale, OLC consists of the skills and characteristics that enable an organization to learn. Five dimensions constitute the essential factors that represent the second-ordered OLC latent constructs.¹⁵ These dimensions are experimentation, risk taking, interaction with the external environment, dialog, and participative decision making. Experimentation is defined as the degree to which new ideas and suggestions are regarded and coped with sympathy. Risk taking refers to the tolerance of ambiguity, uncertainty, and errors. Interaction with the external environment denotes the extent of relationships with the external environment. Dialog is understood as a continuous collective inquiry into the processes, assumptions, and certainties that construct daily experience. Participative decision making means the degree of influence that the employees have in the organizational decision-making process. Chiva et al demonstrated that OLC is a second-order latent factor that strongly determines these five constructs and the Cronbach α coefficients of all these constructs were above 0.7 or close to this threshold. Our study also assumed this second-ordered OLC latent structure to establish the hypothetical relations.

2.4. Research model

The hypothetical relationships between the constructs of user acceptance of information technology and perceived OLC are presented in Fig. 1. It is postulated that perceived OLC has both direct effect and indirect influence on behavioral intention of end users through the mediation of performance expectancy, effort expectancy, and social influence. These hypothetical relationships can be summarized as follows:

(1)

Hypothesis 1: Performance expectancy is positively associated with behavioral intention.

(2)

Hypothesis 2: Effort expectancy is positively associated with behavioral intention.

(3)

Hypothesis 3: Social influence is positively associated with behavioral intention.

(4)

Hypothesis 4: OLC is positively associated with performance expectancy.

(5)

Hypothesis 5: OLC is positively associated with effort expectancy.

(6)

Hypothesis 6: OLC is positively associated with social influence.

(7)

Hypothesis 7: OLC is positively associated with behavioral intention.

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Fig. 1. The research model. The arrow lines marked from H1 to H7 represent the hypothetical relationships between OLC and constructs of the Unified Theory of Acceptance and Use of Technology model. For example, the arrow line directed from PE to BI corresponds to the Hypothesis 1 in our study, which indicates “Performance expectancy is positively associated with behavioral intention.” The other five arrow lines without mark on the right-hand side of this figure reflect the second-ordered latent structure of OLC, which indicates that the five major dimensions of OLC were mostly determined by the common latent factor, OLC. BI = behavioral intention; D = dialog; E = experimentation; EE = effort expectancy; IWEE = interaction with the external environment; OLC = organizational learning capability; PDM = participative decision making; PE = performance expectancy; RT = risk taking; SI = social influence.

2.5. Pilot study

A pilot study was conducted to confirm the reliability of our measurement tool. Fifty volunteer nurses from intensive care units in our hospital participated in this pilot study and completed the questionnaire survey. The Cronbach α values of constructs from user acceptance of information technology ranged from 0.68 to 0.93, and those from perceived OLC were between 0.81 and 0.95 (Appendix 2). Only one construct (performance expectancy) had reliability slightly lower than the acceptable value suggested by Nunnally and Bernstein.¹⁶ The pilot study confirmed internal reliability of the translated questionnaire and demonstrated the feasibility of this questionnaire survey.

2.6. Statistical analysis

The baseline characteristics of participants are presented as counts and percentages. All constructs were measured with multiple-item scales. Following basic descriptive analyses (examination for coding errors, normality, skewness, kurtosis, means, and standard deviations), the data were subjected to confirmatory factor analysis (CFA) to ensure reliability and construct validity.¹⁷ Afterward study hypotheses were tested using structural equation modeling (SEM) analysis. The maximum likelihood estimation method was used to estimate parameters and test hypothetical relationships. A p value less than 0.05 was considered statistically significant. Goodness-of-fit indices, including comparative fit index (CFI), incremental fit index (IFI), and root mean square error of approximation (RMSEA), were used to evaluate model fit. CFI and IFI values greater than 0.9, and RMSEA value less than 0.1 are considered as criteria of good fit to data.18, 19 CFA and SEM were performed using AMOS 18.0 (SPSS Inc., Chicago, IL, USA).

With respect to the sample size demand, a common rule for implementing SEM is that it should have at least approximately 100–200 participants to ensure stable solutions.²⁰ Accordingly, our sample met this criterion of minimal sample size requirement.

3. Results

Among the 284 eligible participants, 215 completed and returned the questionnaire (response rate = 75.7%). The respondents included 84 nurse anesthetists and 131 operating room nurses. Table 1 illustrates their baseline characteristics. Most of them were female (96.7%) and 81% of them had a bachelor or higher degree. With respect to the work experience, 49% of the respondents had worked for over 10 years in the hospital and 47% had worked for over 10 years in the current units.

Table 1. Baseline characteristics of the participants.

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3.1. Reliability and validity of the questionnaire

Table 2 shows the reliability of each dimension and correlations between constructs. The Cronbach α coefficients are all above 0.7.16, 20 CFA demonstrated construct validity of the measurement tool because all scale items loaded significantly on related constructs and the standardized factor loadings are all above the minimum recommended value of 0.4 (Table 3).²¹ Furthermore, dimensionality of the higher order OLC construct was confirmed with second-order CFA. All factor loadings of the first-order constructs on the second-order OLC were statistically significant and ranged between 0.78 and 0.95 (Fig. 2). The fit indexes indicated acceptable model fit and confirmed the scale dimensionality (CFI = 0.93; IFI = 0.93, and RMSEA = 0.07).

Table 2. Internal reliability of each construct and correlations between distinct constructs.a

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Table 3. Standardized factor loading of the items from confirmatory factor analysis.a

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Fig. 2. Empirical results of the research model. The numbers on the corresponding directed lines represent the estimated standardized regression coefficients of hypothetical relationships. The unbroken lines indicate statistical significant correlations between the corresponding dimensions (p < 0.05), and the broken line infers a nonsignificant relation. Note that the second-ordered latent structure of OLC is demonstrated in our analysis by the significantly high correlations between OLC and its five subordinate dimensions on the right-hand side of this figure. The “X” in boldface reflects that OLC was not directly associated with behavioral intention to use new clinical information systems (Hypothesis 7) in our study. All other hypothetic relationships (Hypotheses 1–6) are supported by the structural equation modeling analytic results. BI = behavioral intention; D = dialog; E = experimentation; EE = effort expectancy; IWEE = interaction with the external environment; OLC = organizational learning capability; PDM = participative decision making; PE = performance expectancy; RT = risk taking; SI = social influence.

3.2. Testing research hypotheses

Table 4 provides evidence of positive and significant links between behavioral intention and performance expectancy (r = 0.71), between behavioral intention and effort expectancy (r = 0.18), and between behavioral intention and social influence (r = 0.19), thus upholding Hypotheses 1, 2, and 3. These three constructs accounted for 75% of total variation of behavioral intention. Hypotheses 4, 5, and 6 could also be supported by the finding of positive and significant correlations between perceived OLC and performance expectancy (r = 0.53), between OLC and effort expectancy (r = 0.46), and between OLC and social influence (r = 0.61). However, Hypothesis 7 was not analytically supported because the correlation between perceived OLC and behavioral intention was trivial and not significant. The goodness-of-fit analysis revealed acceptable model fit of the final structural model (CFI = 0.91, IFI = 0.91, and RMSEA = 0.07). All analytical results of the SEM analysis are summarized in Fig. 2.

Table 4. Hypothetical relationships testing.

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4. Discussion

This study aimed to elucidate the relationships between perceived OLC and user acceptance of information technology. Although both issues have been extensively investigated in the past, there is still a lack of study exploring their relationships in the clinical setting, particularly in the operating room. Our study fills this gap and the results provide partial support for the theoretic framework presented in Fig. 1 and the underlying hypotheses. It is noted that behavioral intention to use new information systems is a function of perceived usefulness of new information systems (performance expectancy), ease of use (effort expectancy), and that important others believed that one should use new information systems (social influence). These three factors accounted for approximately three-fourths of the variance in behavioral intention to use new clinical information systems. Among these three factors, performance expectancy was the most powerful explanatory factor. This finding is consistent with many studies (e.g., Kijsanayotin et al⁷) in which performance expectancy has more influence than effort expectancy and social influence. Our study provides empirical evidence to support the UTAUT model proposed by Venkatesh et al⁸ in the clinical context. We have also demonstrated the relationships between perceived OLC and these three determinants of behavioral intention to use new clinical information systems. However, the direct link between perceived OLC and behavioral intention is not supported by the data.

Our study makes a contribution to the literature by supporting the perspective that perceived OLC can affect major determinants of behavioral intention to use new clinical information systems. This finding is important for clinical practitioners. They should keep these three dimensions in mind when setting their learning objectives of training programs related to the introduced clinical information systems. The UTAUT measurement instrument could also be used for audit purposes to assess the acceptance of information technology of users over time and evaluate the effectiveness of the training programs. In addition, the UTAUT and OLC measurement scales applied in this study were applied at the individual level.⁸ A methodological contribution is also made to the empirical validation of these two scales to assess user acceptance of information technology and perceived OLC among medical personnel. These perceptual measures for user acceptance of information technology and OLC may also be useful for carrying out internal audits in medical institutions.

Our findings have potential implications for the introduction of new clinical information systems. First, more effort should be exerted to stress the benefits of new information systems to their work performance because behavioral intention can be increased by means of promoting performance expectancy. Second, well-designed training programs and technical support are necessary to enhance effort expectancy and behavioral intention.⁷ Third, the team leaders and managers should participate in the promotion programs and instruct their members about the advantages of new systems. Fourth, the organizational learning programs should be directed at improving performance expectancy, effort expectancy, and social influence to increase user acceptance of information technology.

There are some limitations in our study. As most cross-sectional researches, the relationships were tested on a specific time point in our study. Although it is possible that the conditions at the time of data collection may remain unchanged, there are no guarantees that this will be the case. In addition, because we have carried out a single-center analysis, it must be stressed that single-center conclusions have to be considered with caution for further generalization. Therefore, it would be of interest to determine the applicability of these results to other medical institutions. An analysis involving multicenters would provide richer and more robust evidence for our findings. Another limitation is that our study did not evaluate potential moderating factors related to user acceptance of information technology²² or organizational learning.²³ Researchers can consider including these variables and assessing their effects on user acceptance of information technology in the future.

In conclusion, our study demonstrated that perceived OLC had positive effects on performance expectancy, effort expectancy, and social influence, which in turn contribute to the increase in the behavioral intention to use new clinical information systems among operating room nurse staff. However, there was no direct linkage between perceived OLC and behavioral intention, and perceived OLC affected user behavioral intention to use new clinical information systems by an indirect route. Our findings provided a promising outlook of how perceived OLC and user acceptance of information technology interact in the clinical context.

Acknowledgments

The authors are indebted to Jin-Lain Ming (Department of Nursing, Taipei Veterans General Hospital) for her kindly help in the data-collection processes. This study was supported by grants from the Taipei Veterans General Hospital (Grant No. V100B-026), and the Anesthesiology Research and Development Foundation (Grant No. ARDF10003), Taipei, Taiwan.

Appendix 1. The original version of questionnaire used in this study.

User acceptance of information technology by Venkatesh et al.⁸

Performance expectancy

Q1.

I would find the system useful in my job.

Q2.

Using the system enables me to accomplish tasks more quickly.

Q3.

Using the system increases my productivity.

Q4.

If I use the system, I will increase my chances of getting a raise.

Effort expectancy

Q5.

My interaction with the system would be clear and understandable.

Q6.

It would be easy for me to become skillful at using the system.

Q7.

I would find the system easy to use.

Q8.

Learning to operate the system is easy for me.

Social influence

Q9.

People who influence my behavior think that I should use the system.

Q10.

People who are important to me think that I should use the system.

Q11.

The senior management of this business has been helpful in the use of the system.

Q12.

In general, the organization has supported the use of the system.

Behavioral intention

Q13.

I intend to use the system in the future.

Q14.

I predict I would use the system in the future.

Q15.

I plan to use the system in the future.

Q16*.

I will use the system despite no obligation.

B.

Organizational learning capacity by Chiva et al.¹⁰

Experimentation

Q17.

People here receive support and encouragement when presenting new ideas.

Q18.

Initiative often receives a favorable response here, so people feel encouraged to generate new ideas.

Risk taking

Q19.

People are encouraged to take risks in this organization.

Q20.

People here often venture into unknown territory.

Interaction with the external environment

Q21.

It is part of the work of all staff to collect, bring back, and report information about what is going on outside the company.

Q22.

There are systems and procedures for receiving, collating, and sharing information from outside the company.

Q23.

People are encouraged to interact with the environment: competitors, customers, technological institutes, universities, suppliers, etc.

Dialog

Q24.

Employees are encouraged to communicate.

Q25.

There is a free and open communication within my work group.

Q26.

Managers facilitate communication.

Q27.

Cross-functional teamwork is a common practice here.

Participative decision making

Q28.

Managers in this organization frequently involve employees in important decisions.

Q29.

Policies are significantly influenced by the view of employees.

Q30.

People feel involved in main company decisions.

*The item “I will use the system despite no obligation” was added by the authors to evaluate whether the respondents would still use the system without obligation.

Appendix 2. Internal reliability of constructs from user acceptance of information technology and organizational learning capability.

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