Predicting acceptance of electronic medical records: is the technology acceptance model enough?
Article Type:
Medical records (Management)
Electronic records (Management)
Physicians (Surveys)
Physicians (Technology application)
Physicians (Practice)
Seeman, Elaine
Gibson, Shanan
Pub Date:
Name: SAM Advanced Management Journal Publisher: Society for the Advancement of Management Audience: Trade Format: Magazine/Journal Subject: Business; Business, general Copyright: COPYRIGHT 2009 Society for the Advancement of Management ISSN: 0036-0805
Date: Autumn, 2009 Source Volume: 74 Source Issue: 4
Event Code: 200 Management dynamics Computer Subject: Company business management; Technology application
Product Code: 8011000 Physicians & Surgeons NAICS Code: 621111 Offices of Physicians (except Mental Health Specialists)
Geographic Scope: United States Geographic Code: 1USA United States

Accession Number:
Full Text:
While health care organizations recognize the advantages of electronic medical records, they often do not use them. Why not? Physician acceptance seems to be the heart of the issue, but what explains their willingness or unwillingness to go electronic? Two theories address the problem: Davis's technology acceptance model and Ajzen's theory of planned behavior. A survey of physicians based on both theories and analyzed using multiple regression analyses showed that both theories explain attitudes toward adoption of electronic records, but the theory of planned behavior was more important.


The current push for universal health care coverage and health care reform has highlighted health information technologies as a means of cutting costs and improving efficiencies in the health care arena (Moment of Truth, 2009). The electronic medical record (EMR) integrates patient information systems so that patient demographic, financial, and medical information can be collected, accessed, transmitted and stored in a readily available digital format (Hough, Chen, and Lin, 2005; Steele, Gardner, Chandra, and Dillon, 2007). EMR technology represents a movement from paper-based care activities toward outcome-focused, evidenced-based processes (Mangalompalli, Rama, Muthivalian, Jain, and Parinam, 2007). This shift can be an agent for change and improvement by eliminating confusing or illegible handwritten order documentation, minimizing transcription errors, and fundamentally reducing clinical mistakes. Most important, EMR technology allows physicians fast access to appropriate patient information allowing prompt diagnosis and treatment (Chao, Jen, Chi, and Lin, 2007). In critical situations, such quick access saves lives (Steele, et al., 2007). As such, the electronic medical record enables physicians to make quicker, more informed decisions because they have all patient information at their fingertips when they need it.

While health care organizations recognize the advantages of EMR, adoption of the technology has been slow (Abdolrusulnia et. al, 2008). To date, less than 10% of American hospitals have implemented electronic medical record keeping as part of their technology strategy for health information (Gardner, 2007). In fact, predictions concerning physician adoption of the EMR indicate that policy makers' 2014 target for widespread implementation is unlikely (Ford, Manachemi and Phillips 2006). Reasons for the slow deployment include expenses related to upgrading existing paper systems, funding for additional workstations and resources, and the challenges associated with achieving and maintaining physician buy-in and acceptance. However, according to John Hammergren, CIO of McKesson, "It's really not a technological barrier. The systems are available and we can provide those interconnections. The issue is one of adoption. Are people really ready to do this? As long as it's easier to script it out and hand it to a voice-activated nurse, that's what the physician will do" (Colvin, 2007). We have based our study on this issue: physician acceptance of the electronic medical record.

Statement of the Problem

According to Al-Gahtani (2008), researchers have often addressed the issue of why individuals who would benefit from interactive information systems do not use them--an especially important question for medical organizations attempting to implement EMR systems. We posit that factors specific to physicians and the medical profession affect adoption. Further, we suggest that pinpointing what motivates physicians to accept technology will enable hospitals and medical practices to design systems and tailor implementation strategies toward factors that motivate adoption. The objective of this research is to identify those factors by comparing the degree to which two common theories, Davis's technology acceptance model (TAM) and Ajzen's theory of planned behavior, successfully explain variance in medical personnel's acceptance of electronic medical records (EMR) technologies.

Theoretical Background

The Technology acceptance model

Davis's (1989)'technology acceptance model (TAM) has been and remains an important and viable tool for researchers. Research based upon the TAM has offered valuable insights into how and why individuals accept or reject technology. However, many of the studies utilizing the TAM or some variation of it have focused on general user populations working in varying occupational settings and utilizing a wide spectrum of information technology solutions (Gefen and Straub, 1997; Taylor and Todd, 1995; Veiga, Floyd, and Dechant, 2001; Venkatesh and Morris, 2000). Only recently have researchers begun to study factors related to more specific, professional populations (Abdolrusulnia et. al, 2008, Al-Gahtani, 2008, Kamhawi, 2008, Yi, et al., 2006). Physicians and physician extenders (i.e., physician assistants and nurse practitioners) differ markedly from general users. They are highly educated, highly trained professionals working in stressful and highly politicized environments. Given the complexity of the health care industry and its unique occupational dynamics, we feel that the TAM in and of itself may not be adequate for explaining technology acceptance as it applies to medical practitioners.

The theory of planned behavior

Advocates of the theory of planned behavior (TPB) suggest that all behavior is motivated by individual decisions based on an individual's intention to perform that behavior. Intention to perform a behavior, in turn, is influenced by the individual's perceived control over the performance of that behavior, his or her attitude toward performing the behavior, and his or her perception of social norms (pressure or approval from important referent individuals to perform a behavior).

The theory of planned behavior asserts that behavioral control reflects an individual's belief regarding the ease of performing or completing a task. Behavior control is similar to the technology acceptance model's perceived ease-of-use construct. Indeed, the TAM was derived in part from the theory of planned behavior. However, the theory of planned behavior incorporates the individual's past experience as well as a sense of control into choosing a behavior (as opposed to having a behavior mandated).

According to the theory of planned behavior, individuals behave in accordance with their beliefs (Ajzen, 1988). This theory has considerable support for behaviors in medicine, education, business, and the general population. The theory implies that doctors' attitudes, their subjective norms, and perceived behavioral control are positively related to their planned and actual behavior concerning the acceptance of new organizational technology operationalized as an electronic medical records system. Indeed, prior research by Seeman and Gibson (2008) found that the constructs associated with the theory of planned behavior did, in fact, explain variance in medical personnel's' stated intention to utilize a newly implemented EMR system. It should be noted that Ajzen stressed and other researchers reiterated that relevant beliefs should be studied within the specific usage context (such as EMR systems) to determine the distinct factors causing the individual to engage in the behavior of interest (Kalify and Shen, 2008).

Taylor and Todd (1995) introduced a decomposed TPB that included constructs from the innovation literature, the original TPB, and the TAM. The decomposed TPB explores social norms and perceived behavioral control more deeply by breaking them down into individual belief dimensions. By including aspects not found in the TAM, they posited that the decomposed TBP would give more complete understanding of usage than does the TAM alone. In their study of students using software within a Computer Resource Center, the decomposed TPB explained 60% of the variance in intention compared with 57% for the pure TPB, and 52% for the TAM (Premkumar and Bhattacherjee, 2008). The importance of social norm is somewhat dependent on the stage of implementation. During the early stages of implementation, when users have limited direct experience, social norm is most important (Taylor and Todd, 1995).


Research setting, participants, and procedures

As part of an ongoing, multi-phase research endeavor examining the implementation of electronic medical records, faculty associated with both a medical school from a large regional university and a large multi-physician practice were asked to complete an anonymous survey regarding their perceptions of EMR implementation at their respective locations. Although a "convenience sample," the physicians sampled for the current study were chosen because they represented a combination of doctors who worked as university faculty and private practice physicians across multiple medical specialties. As such, they embodied a representative sample of the general physician population. Completed surveys (57% male, 43% female) were received from 102 of the physicians invited to participate. The average age of physician participants was 42.4, with an average of 13.8 years practicing medicine, 7.2 years at the current location, and 6.7 years in their current job position.

Survey instrument

The survey instrument used for the current study was based on questions derived from Davis's TAM model (Davis, 1989) and Ajzen's planned behavior model (Ajzen, 1988). Participants responded to questions measuring the central constructs of the TAM: the perceived ease of use of EMR technologies and the perceived usefulness of EMR; questions measuring the central constructs of the theory of planned behavior: perceived behavioral control, attitudes toward EMR technology; and perceived social pressure regarding EMR usage. In all instances, respondents used a 7-point Likert-type scale where one was "Not at All" and seven was "Very Much So."

To assess the criterion of technology acceptance, participants were asked to indicate the degree to which they concurred with a statement assessing their intention to utilize EMR technology in the future. This is highly consistent with previous technology acceptance studies that have utilized "intention to use technology" as indicative of technology acceptance.

All survey items are shown in Table 1 grouped by construct.


To examine the degree to which the technology acceptance model and the theory of planned behavior were associated with intentions to embrace EMR, two separate multiple regression procedures were conducted. Details of these are described in the Results section.


Two multiple regression analyses were conducted to predict technology acceptance. The first included the variables associated with the technology acceptance model, while the second included the variables associated with the theory of planned behavior.

For the TAM, the regression equation was significant, R2 = .617, adjusted R2 = .555, F (13, 101) = 9.991, p < .01. Likewise, the regression equation for the theory of planned behavior was significant, R2 = .721, adjusted R2 = .663, F (17, 101) = 12.469, p < .001. Based upon this information alone, it appears that the theory of planned behavior does the best job explaining variance in acceptance of EMR by medical personnel.

Next, a multiple regression analysis was conducted with both the technology acceptance model and theory of planned behavior variables as predictors. The linear combination of the measures was significantly related to technology acceptance, R2 = .800, adjusted R2 = .709, F (14, 101) = 8.765, p < .01. The theory of planned behavior measures predicted significantly over and above the technology acceptance model variables, R2 change = .183, F (17, 101) = 3.66, p < .01, and technology acceptance model variables also predicted significantly over and above theory of planned behavior measures, although to a lesser degree R2 change = .079, F (13, 101) = 1.911, p<.05. Based on these results, it appears that the best explanatory power is obtained by a linear combination of the variables associated with both of the theories, and that the constructs of the theory of planned behavior play a highly significant role in explaining EMR acceptance above and beyond what the technology acceptance model alone is capable of explaining.

Implications for Practice

While the main purpose of this research is to advance the development of a technology acceptance theory specific to the unique traits and working environment of physicians and related medical personnel, the results of this study have considerable implications for practicing managers given the task of improving physician acceptance of the electronic medical record. Because doctors' attitudes (toward technology and what is appropriate when providing medical care) and their perceived control over the EMR significantly influence their acceptance, managers should consider these factors when promoting the advantages and potential uses of EMR technology. According to Jensen and Aanestaid (2007), managers should be aware of how different groups experience and enact practices around the technology. A better understanding of physician acceptance will allow organizations to tailor technology implementation strategies in a way that dispels physicians' concerns through increased participation, training, or developmental activities. In this instance, the knowledge obtained provides the potential for structuring the technology initiative in such a way as to help ensure success.

Conclusions and Future Research

As pointed out by Hu et al. (1999), professionals might subtly differ in their acceptance of technology compared with individuals in an ordinary business setting. While the advantages of using the electronic medical record in physician decision making are clearly recognized, this study explores reasons beyond the TAM that explain the slow adoption of this technology. A major contribution of this research is the finding that the theory of planned behavior explains acceptance by this population beyond that which is explained by TAM alone.

Future research might consider how technology adoption by physicians is affected by other factors, such as culture. For example, perceived usefulness appears to be more important in Western Cultures while ease-of-use is more important to non-Western cultures in determining intention and actual use (Schepers and Wetzel, 2007). Given the diverse ethnicity of the physician population, studying the effect of ethnicity on EMR acceptance could be an important contribution.

Some recent IT research has begun to focus on the continuation of usage for accepted technologies (Premkumar and Bhattacherjee, 2008). Khalifa and Davison, (2008) have studied the applicability of the theory of planned behavior regarding continuance behavior. As the electronic medical record becomes more common, further research regarding factors influencing its use would make a contribution.

From an organizational perspective, the role of EMR as a source of medical organizational change (Jimmieson, Peach and White, 2008) offers an avenue of extending this research, particularly regarding how the theory of planned behavior might assist in managing this change. Finally,

Venkatesh and Balla (2008) have suggested that research regarding the role of interventions can aid in the decision making that may lead to increased acceptance and better use of information technology. As the electronic medical record moves from larger to smaller hospitals and practices, adoption decisions and intervention create further research avenues.


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Elaine Seeman, East Carolina University

Shanan Gibson, East Carolina University

Dr. Seeman has experience in database design, systems development, and software development and testing. Her research, which has been published in a number of journals, explores acceptance of medical information technologies and also public policy and regulatory issues related to 911. In addition to teaching, Dr. Gibson is a member of the Social Security Administration's Occupational Information Development Advisory Panel. Her research interests include entrepreneurship education, online training and education, and human resources management issue, and she has published recently in several journals.
Table 1. Survey Items

TAM Items

Perceived Ease of Use ([alpha] = .594)

I find EMR flexible to interact with.
I find EMR to be easy to use.
I find it easy to get EMR to do what I need it to do in my patient care
  and management.
It is easy for me to become skillful in use the EMR technology.
Learning to operate EMR is easy for me.
My interactions with EMR are clear and understandable.

Perceived Usefulness ([alpha] = .859)

The primary benefit of EMR is patient safety.
EMR is related to a physician's ethical responsibility to "do no harm."
I find EMR useful for my patient care and management.
Using EMR enhances my service effectiveness.
Using EMR improves my patient care and management.
Using EMR enables me to complete patient care more quickly.
Using EMR increases my productivity in patient care.

Theory of Planned Behavior Items

Perceived Behavioral Control (alpha = .72)

I know why EMR was/is being implemented at my organization.
Individual physicians have the ability to influence the decisions
  regarding EMR.
Individual physicians will influence the decisions regarding EMR.
I have the knowledge necessary to use EMR.
I have the resources necessary to use EMR.

Perceived Social Influence (alpha = .35)

Medical leadership believes that I/we should use EMR.
My feelings of responsibility toward my patients influence me to use
My peers think I/we should use EMR.
The culture here embraces EMR technology.

Attitudes Toward EMR (alpha = .87)

EMR will be successfully implemented at other organizational locations.
EMR is an appropriate tool for physicians to use.
I like the idea of using EMR.
I find EMR technology useful for my patient care and management.
Using EMR is a good idea.
Using EMR is pleasant
Using the EMR system is a wise idea.
I have embraced the EMR technology in my workplace.
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