My dissertation is dedicated to my grandmother, Mrs. L. N. Papneja, and to my wife, Devshree.
My journey towards the dissertation began in 2003 during my undergraduate years, when I enrolled in a course offered by the AI Institute on Knowledge Based Systems. There, in the basement of the Boyd Graduate Studies Building, is where I built my first interactive agent using Exsys Corvid – a rule-based animal identification system with an inference engine that consisted of a knowledge base, logic blocks, and had an explanation facility that explained to the user how it had reached its conclusion. Having studied literature on information privacy during my doctoral studies, I came up with the idea of using explanations as a mechanism to reduce the user’s privacy concerns in interactions with conversational agents (chatbots). I identified self-disclosure as a dependent variable, and conducted a thorough literature review of self-disclosure to conversational agents. I synthesized my findings in the form of an emergent theoretical framework. Likewise, I studied literature on explanations, came up with the moderated-mediation theoretical model, and the arguments for the hypotheses, including the arguments relating to perceived relevance and informational justice.
I came up with the idea of using two different information sensitivity contexts, the two scenarios – health and shopping, the dozen or so questions for each scenario, and the two types of explanations for each question. I designed and conducted the explanations reliability test, reworded the explanations, and retested them. I found and adapted each measure, designed the online experiment, and created the pre- and post-experiment surveys using Qualtrics. I learned how to use IBM Watson Assistant, and built the six different chatbots needed for the six different experimental conditions. I filed for, and obtained (after a revision), the IRB approval.
In the middle of conducting my pilot study, I came across the two references – MacCallum et al. (1999) and Wolf et al. (2013) – that talked about sample size requirements in studies involving latent factors. Given that my empirical study had 16 latent factors, each with at least three measurement items, I realized that a sample size of 60 would not be enough. I increased the sample size to 150 and let the pilot study run for a few more days on the Prolific platform. After analyzing and writing up the results of the pilot in November 2021, I was told that I had executed on what I had promised as part of my proposal defense, and could use my pilot results as the results of my dissertation and be done. Instead, I chose to add a couple of questions to the sensitive scenario (and correspondingly to the non-sensitive scenario) in order to make the difference in perceived sensitivity between the sensitive and non-sensitive scenarios more stark. I then ran the main study on Prolific with a proper sample size of 650 (588 after exclusions).
My dissertation and Ph.D. are the result of several such self-realizations and self-efforts, and I planned, executed, and wrote every word of the ~33,000-word document as best as I could within imposed constraints. Any methodological rigor in my empirical study came only from reading other studies, and from my time as an Industrial Engineering major at Georgia Tech, a time I really missed throughout my Ph.D.
I am thankful to the Terry College of Business and the University of Georgia. I am thankful to my family and friends, and to former professors and classmates from the University of Notre Dame and at Georgia Tech for their constant encouragement and guidance. I am thankful to my wife, her parents, and her sisters for helping keep my morale up through difficult times. I am thankful to my former managers and co-workers at Hewitt Associates and Goldman Sachs for helping me realize the broader perspective, and to see the forest through the trees in difficult times.