Date of Graduation


Document Type


Degree Name

Doctor of Philosophy in Business Administration (PhD)

Degree Level



Information Systems


Rajiv Sabherwal and Varun Grover

Committee Member

Zach Steelman

Second Committee Member

Connie Lamm


Immersive systems, Immersion, NeuroIS, EEG, Virtual reality, User satisfaction, User performance


Immersive systems (e.g., Virtual Reality) are at the forefront of the next generation of innovative technologies. Recent technological advancements have made them viable for businesses and individuals to adopt. For example, some realtors now offer virtual house tours in the absence of walk-ins. The concept of “immersion” is at the heart of these technologies. However, despite the fact that this concept has been studied for almost three decades, our understanding remains weak and inconsistent. Specifically, there remains a lack of consensus on what it is, its antecedents, and how it should be measured.

This dissertation includes two essays. In Essay 1, we build on prior literature to develop a holistic immersion model that incorporates sensory, cognitive, and affective factors and their interactions. An electroencephalography (EEG) lab study was conducted to measure subjects’ immersion while using technology in the lab and determine their engagement with technology in the presence of real-world distractors (e.g., iPhone text-sound). Findings suggest that immersion has a U-shape relationship with user performance such that, after a certain threshold, a unit increase in the users’ immersion level has an exponentially positive effect on their performance.

In Essay 2, we use the same study design and concept (immersion) to investigate the relationship between neurophysiological and psychometric measures of immersion. IS scholars have encouraged methodological investigations and triangulation using NeuroIS tools, yet there is a dearth of studies on how these tools interact and influence one another. Hence in Essay 2, our objectives are to (i) measure users’ experience of immersion using EEG and two psychometric-based methods (perceptual and observational); (ii) test these measures in a nomological network of antecedents of immersion and consequences of immersion; (iii) statistically compare and report how relationships differ across each measure; and (iv) build an aggregated measure of immersion using neurophysiological and psychometric tools and test its capabilities in explaining an outcome variable.