CCE Theses and Dissertations

Date of Award

2019

Document Type

Dissertation

Degree Name

Doctor of Philosophy (PhD)

Department

College of Engineering and Computing

Advisor

Maxine Cohen

Committee Member

Ling Wang

Committee Member

Steven Terrell

Keywords

extraversion, FFM, personality traits, technology acceptance, UTAUT

Abstract

Technically-driven medical devices such as wireless implantable medical devices (WIMD) have become ubiquitous within healthcare. The use of these devices has changed the way nurses administer patient care. Consequently, the nursing workforce is large and diverse, and with it comes an expected disparity in personalities. Research involving human factors and technology acceptance in healthcare is not new. Yet due to the changing variables in the manner of which patient care is being administered, both in person and in the mechanism of treatment, recent research suggests that individual human factors such as personality traits may hold unknown implications involving more successful adoption of emerging technologies for patient care.

The purpose of this research was to empirically investigate the influence of personality traits on a nurse’s intention to use WIMDs for patient care. One hundred and two nurses from a tertiary teaching hospital in Michigan were surveyed to determine if their identifiable personality traits statistically related to their intention to use a WIMD. A predictive model was developed by combining constructs from the unified theory of acceptance and use of technology (UTAUT) model and the Five Factor personality trait model (FFM). The model used moderated multiple regression (MMR) to statistically identify if the personality traits of openness, conscientiousness, extraversion, agreeableness, and neuroticism, moderated one or more statistically significant relationships between 1) performance expectancy (PE) and intention to use (IU), 2) effort expectancy (EE) and IU, 3) and social influence (SI) and IU. It was predicted that PE, EE, and SI would show statistical significance on a nurse’s IU of a WIMD when moderated by one or more of the five personality traits. Results showed statistical significance between PE and IU, and EE and IU, but not between SI and IU, when moderated by extraversion. Results showed no statistical significance between PE and IU, EE and IU, or SI and IU when moderated by openness, conscientiousness, agreeableness, or neuroticism.

This research has contributed by conducting an investigation on individual human factors that may impact nurses’ intention to use emerging technologies; and by providing statistical evidence that may help to better predict the role personality traits have on a nurse’s adoption of WIMDs for patient care.

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