Research Philosophy
The Roman Emperor and philosopher Marcus Aurelius is credited with saying, “Nothing has such power to broaden the mind as the ability to investigate systematically and truly all that comes under thy observation in life.” Having ruled from 161 to 180 AD, I don’t know with certainty if he said that or said it exactly the way it is quoted today. However, I find it fascinating that this train of thought existed then and is still relevant today. During my doctoral studies, I was trained as a post-positivist. More correctly, while researching schools, I selected one that embraced post-positivism without realizing it. To me, it is only natural that knowledge is gained through observation, including measurement, and must be repeatable. However, it must also be recognized that the mere act of observation can and often does alter the observation itself. The first thing that comes to mind relative to this is the double-slit experiment in physics. When determining if the light behaves as a wave or particle, the researchers learned that it behaves differently when the experiment is being measured—the results of quantum mechanical phenomena. Similarly, social scientists must deal with bias in their research that ultimately affects the outcome. While this can’t be changed, it must be acknowledged and accounted for.
The first step in conducting post-positivist research is grounding it in established theory. For example, I discuss my first research stream later in this document. That research stream uses three critical theories to understand the research framework regarding the antecedents, decisions, and outcomes model. First, the decisions and outcomes were evaluated against Innovation Diffusion Theory (IDT). Innovation Diffusion Theory (IDT) has proven to be particularly useful in understanding the adoption phase of new technology from an organizational perspective. Second, Adaptive Structuration Theory (AST) was utilized to understand how blockchain technology has evolved. This was useful in understanding why we have three phases (1.0, 2.0, and 3.0) of blockchain technology and could have more. Third, the antecedents were evaluated through the lens of Affordance-Actualization Theory. Affordance-Actualization Theory helped me understand the drivers that led to the creation of blockchain technology. This statement will continue with my research motivation, research stream one, research stream two, future research, and a conclusion.
Research Motivation
An unattributed quote often seen on t-shirts states, “It’s better to figure things out than to make things up.” While unattributed, it has always resonated with me. As an undergraduate, I would argue that the most influential class in my life was a philosophy class on logic and critical thinking. I realized during that class how easy it is to believe in things that aren’t based on fact or even logic. While I may not remember the formal names, I have retained the most common logical fallacies. It is interesting when I hear something on the news or a friend or family member says something. I wonder if it is true. Is there any hard evidence to support it? Is there even a logical train of thought that leads to it? I’ve learned over time never to ask those questions directly. Instead, I go back and try to find the source if I am interested. Something that I learned in that undergraduate class is that the majority of people never take the time to do it. It’s complicated and time-consuming, and the brain is wired to take shortcuts. It’s easier to believe something because the person who said it is somebody you trust. This curiosity and drive to find the truth is what motivates me. I don’t want to take shortcuts. I don’t want to explain things by making them up. I’d much rather figure them out for myself. Even though that is much harder, it is much more rewarding and often leads to more interesting outcomes than you imagine. Just take the case of the double-slit experiment in physics.
Research Stream One
My primary research stream relates to the emergence of Web 3.0. What technical and behavioral barriers need to be resolved, what outcomes will be achieved, and how will Web 3.0 ultimately impact business? Transformative technologies take decades to emerge fully and seldom are in the form of the initialize conceptualization. For example, TCP/IP protocols were created in 1981 due to the increasing complexity required to link US government and university computers nationwide. They replaced the previous Network Control Program (NCP) used by ARPANET. While published in 1981, work on the protocols began in 1973. Ultimately this led to what we know today as the modern internet. Initially as static content or Web 1.0 and ten years later, interactive content with a backend database or Web 2.0.
The next iteration of the internet will be characterized by users who are connected via a decentralized network and have access to their data. I was a practitioner who worked on Web development between Web 1.0 and Web 2.0. Helping create an online banking application for the organization I worked for. Today we find ourselves in a similar situation with Web 3.0 as we were with TCP/IP in 1981. Technology has evolved to the point where basic requirements for transformation are available. Technologies such as blockchain, natural language processing, machine learning, and artificial intelligence are converging to allow for the creation of Web 3.0.
Identifying barriers to their convergence provides practitioners with business opportunities and academics with exciting research topics. For example, how does blockchain technology eliminate the central authority and create new business models? As these barriers are resolved, the end state of Web 3.0 will start to take form, allowing for additional scholarly endeavors.
During my graduate program, I completed five research proposals. Of those five, four were related to blockchain technology, and one was related to wearable activity trackers. The first blockchain proposal identified a potential new antecedent for cryptocurrency bias. There may be a negative bias toward blockchain technology based on its association with cryptocurrency. The second was a structured literature review, resulting in a new research framework based on the antecedents, decisions, and outcomes model. The third developed and implemented a pilot study for that framework—the fourth involved semi-structured interviews to enhance the research framework and survey instrument further.
My dissertation proposal is complete, minus the final survey and analysis of the data. The first chapter will be an enhanced version of the structured literature review. The second chapter will contain the pilot survey, semi-structured interviews, and the final survey results. My primary research stream will expand my blockchain research into the related areas of Web 3.0. Web 3.0 is an idea for a new World Wide Web that includes blockchain, Meta, artificial intelligence and machine learning, trustless and permissionless networks, decentralization, and natural language processing (NLP). Gavin Wood, a co-founder of Ethereum, coined the term “Web 3.0” in 2014.
Research Stream Two
Beyond Web 3.0, my interests also include data science. When I completed my undergraduate degree, there was no major or even formal classes in data science. At least not that I was aware of. During my career in information technology, I have observed companies generate and collect large quantities of data. Yet, I have seen them struggle to use that data meaningfully. From a practitioner’s standpoint, the data quality is often poor and challenging to pull out of systems where they are stored. Like with Web 3.0, we are on the precipice of change with that. In many ways, the field of data analytics is further ahead than Web 3.0. There are now classes and majors devoted to data science. However, like Web 3.0, there are still barriers to adoption than are holding organizations back from obtaining actual value from using that data. As I had no formal training in data analytics as an undergraduate, I recently took online training and earned a Google Data Analytics Certificate. I plan to use this training and my practitioner experience to study barriers to adoption in this space, similar to what I am doing with Web 3.0.
Future Research
While less formalized, my final interest lies in cyber security. I hold professional certifications as a Certified Ethical Hacker (CEH) and Certified Information Systems Security Professional (CISSP), and I have established security functions within organizations and managed security professionals. While I have practitioner experience in this area, I have yet to explore gaps in the current research. As time permits, this will become my third research stream.
Conclusion
I must touch on the inevitable comparison between a Doctor of Philosophy (Ph.D.) and a doctorate of business administration DBA. Some believe that the DBA is a practitioner’s degree and that they are less trained to be researchers. In a previous section, I stated that I am a post-positivist. From this perspective, I know either perspective will have an inherent bias. With that in mind, I’d point to the published literature. MacLennan found no group differences in methodology choice, sample size, or research type related to the dissertation outcomes between degree types (MacLennan et al., 2018). Essentially, there is no material difference in the quality of the outcome between programs; DBAs produce similar quality dissertations to PhDs. Additional work by MacLennan and Simpson re-enforce these notions from an empirical standpoint (MacLennan et al., 2016; Simpson & Sommer, 2016). From a logical standpoint, I’d argue that differences exist, and as long as the quality of programs is similar, those differences will lead to better research as a whole. Having thirty years of practitioner experience, I have a unique background that will help me identify gaps in the current literature and seek out research problems relevant to organizations.
My education at UW Whitewater has given me the knowledge, skills, and abilities to be an engaged scholar. In addition, previous work experience has provided me with a practitioner’s view of organizational challenges and research opportunities. I look forward to researching and providing original scholarly content that fills gaps in Web 3.0, data science, and cyber security.
References
MacLennan, H. L., Piña, A. A., Hafford, P. F., & Moran, K. A. (2016). Doctor of Business Administration (D.B.A.): A viable credential for faculty in programmatically accredited business degree programs? International Journal of Doctoral Studies, 11, 217–226. https://doi.org/10.28945/3529
MacLennan, H., Piña, A., & Gibbons, S. (2018). Content analysis of DBA and Ph.D. dissertations in business. Journal of Education for Business, 93(4), 149–154. https://doi.org/10.1080/08832323.2018.1438983
Simpson, C., & Sommer, D. (2016). The Practice of Professional Doctorates: The Case of a U.K.-Based Distance DBA. Journal of Management Education, 40(5), 576–594. https://doi.org/10.1177/1052562916652643