Emmanuel Bazile


Why Was This Research Investigation Imperative?

Prior research provided evidence of failed EMR implementations due to resistance on the part of physicians, nurses, and clinical administrators. Only 25% of office-based physicians had basic EMR systems and only 10% have fully functional systems. Resistance to using EMR systems puts both hospitals and patients at greater risk of possible medical mishaps.


This is a quantitative method study that measured the relationships between six constructs, namely computer self-efficacy (CSE), perceived complexity (PC), attitude toward EMR (ATE), peer pressure (PP), anxiety (AXY), and resistance to use of technology (RES).

Study Methods

The study also measured four covariates: age, role in healthcare, years in healthcare, gender, and years of computer use. The study used Structural Equation Modeling (SEM) and an analysis of covariance (ANCOVA) to address the research hypotheses proposed.

Student Instrument

A Web-based survey instrument consisting of 45 items was used to assess the six constructs and demographics data. The data was collected from 350 healthcare professionals across the United States.


Construct validity and reliability was done using the Delphi method. A pilot study of 20 participants was conducted before the full data collection was done, resulting in some minor adjustments to the instrument. The analysis consisted of SEM using the R software and programming language.

Resistance to Use EMR Systems

Lack of a formal training program has been implicated as a reason for resistance to the use of computer systems in the work environment in a 1998 study by Jayasuriya. Although this was not taken into consideration in this research but was suggest by the chair for future work. Overcoming healthcare professional’s resistance to the implementation of EMR systems can often require diplomacy and persuasion, rather than mandates and requirements.

The objectives of the Study

To empirically assess the contributions of the independent variables of CSE, PC, ATE, PP, and AXY on the dependent variable of healthcare professionals’ resistance to EMR systems, while controlling for the demographic indicators of physicians’ age, gender, precise healthcare role and years in the profession.

The Literature Revealed

Very few studies empirically investigated the impact of CSE, PC, ATE, PP and AXY on the resistance to use EMR systems. The literature concluded that the addition of additional constructs could server as s better predictor to the Resistance to EMR systems.

Hypotheses Testing

To examine hypotheses 1 through 5, the paths from CSE, PC, ATE, PP, and Anxiety to medical professionals’ resistance to using EMR systems was examined. Each of the variables are latent variables within the model. The standardized regression weights were interpreted to examine the strength of the relationship between CSE, PC, ATE, PP, and Anxiety to medical professionals’ resistance to using EMR systems.

Study Results

The aim of this study was to determine if CSE, PC, ATE, AXY and PP have significant effect on RES.

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