Date of Award

Spring 2019

Access Type

Dissertation - Open Access

Degree Name

Doctor of Philosophy in Human Factors


College of Arts & Sciences

Committee Chair

Stephen Rice, Ph.D.

First Committee Member

Albert J. Boquet, Ph.D.

Second Committee Member

Christina Frederick, Ph.D.

Third Committee Member

Scott R. Winter, Ph.D.


INTRODUCTION: The purpose of the current dissertation is to better understand the factors which make an individual willing (or unwilling) to undergo robotic surgery. Though surgical feasibility and provider perceptions are often studied, little research has investigated how patients perceive robotic surgical systems.

METHOD: A two-stage approach was taken in order to build and validate a regression equation in order to predict an individual’s willingness to undergo robotic surgery based on several factors. Stage 1 employed a sample size of 1324 participants in order to build the model. Participants responded to a survey indicating their willingness to undergo robotic surgery, and answered questions related to their perceptions of the system, demographic information, and emotional responses. Stage 2 employed a sample size of 1335 participants, who responded to the exact same survey as Stage 2. The regression equation developed via Stage 1 was then tested using the participants from Stage 2 in order to validate the equation.

RESULTS: In Stage 1, a backward stepwise regression was conducted on the twenty-one predictive factors of interest (age, gender, income, education level, ethnicity, perceived complexity, perceived value, familiarity, wariness of new technologies, fear of surgery, personality factors (openness, conscientiousness, extraversion, agreeableness, neuroticism), and affect (in the form of the six universal emotions). Of these twenty-one factors, eight were indicated to be significant predictors: perceived value, familiarity, wariness of new technologies, fear of surgery, openness, anger, fear, and happiness. These factors accounted for 62.7% of the variance in the model (62.4% adjusted).

In Stage 2, several methods were used to validate the regression model, including: correlational analyses, a t-test, and calculation of the cross-validity coefficient. Correlational analyses indicated that the predicted scores of willingness in Stage 2 generated using the regression analyses were significantly correlated with the actual scores of willingness reported by participants. In addition, results of the t-test indicated that the predicted scores and actual scores were not significantly different. Further, the cross-validity coefficient was similar to the initial R2, indicating good fit of the model.

CONCLUSION: Results of the study indicate that perceived value, familiarity, wariness of new technologies, fear of surgery, openness, anger, fear, and happiness are all significant predictors of willingness to undergo robotic surgery. These results not only benefit the literature on technology acceptance and robotic surgery, but also have practical applications for the way these systems are designed and marketed, and the way that patients are educated.