I love to learn. There’s something unequivocally satisfying associated with acquiring new information, transforming it into knowledge and applying it to one’s perception of the world. As a learning enthusiast, I was awfully fortunate this past year. Not only was I was able to embark upon my first year of PhD studies (at the iSchool), but I was also able to start my own company and immerse myself in the Syracuse startup community. From a learning perspective, both opportunities have proven exceedingly valuable.

In response to my decision to embrace a relatively unusual, eclectic learning experience (i.e. PhD studies and starting a company), I have faced a number of questions regarding my ultimate motives. In other words, what is the end goal? Why am I scattered about trying to do both and not focusing solely upon one or the other? Do I want to do research and teach at a university, or do I want to build products/offer services and start companies? The truth is, I don’t have answers to these questions. For the time being, I am just following my instincts.

I appreciate individuals asking such tough questions — they get me thinking. Consequently, I have been considering the notion of learning in the context of similarities and distinctions between PhD studies and startup endeavors. The following identifies and juxtaposes my high-level thoughts on the educational characteristics of both.

Differences between PhD studies and startups

  • Abstract vs applied – Depending on the field, most PhD research is constructed at an abstract level. That is to say, the pursuit of knowledge is carried out apart from concrete realities, specific objects and distinct instances. Theories are often used as building blocks. They help to support collected data, and can also help to elicit new questions for future research. Conversely, startups revolve around application and practicality. Startup companies build new products and/or offer services to consumers that provide a practical gain in the physical world. Results are often tangible and complemented by monetary impacts.
  • Driven by precision vs driven by mistakes – To PhD students and faculty researchers, precision is critical throughout all phases of the research cycle: literature review, proposal, data collection, data analysis and writeup (note: this is just a high-level example in the context of the social sciences). Creating cogent knowledge is extremely challenging, and doing so effectively requires time, extensive collaboration and accuracy in all facets. In contrast, startups that seek to deliver products and/or services to consumers must iterate rapidly. To do so, ‘best guesses’ are made. Instead of spending excessive amounts of time investigating the likelihood of what *might* happen, imprecise pseudo-hypotheses are made and acted upon expeditiously. Learnings from mistakes made through iteration cycles are perceived as ‘truths’ in the context of a particular startups’ environment, and used as the building blocks to a successful business.
  • Perception of “truth”: quantitative/qualitative methods vs product/service impact and use – Perhaps ‘perception of “truth”‘ is poor diction. My friends in the PhD program who are positivists will likely contend that one can’t perceive truth. I tend to be more of a social constructionist, so I’ll keep it as is for arguments sake. Ultimately, what I am referring to is understanding of 1) one’s socially constructed version of fact/reality (social construction) or 2) one’s perception of fact/reality driven by empirical evidence (positivism). In the research world, a collection of research methods (i.e. quantitative and qualitative) are strategically used and manipulated in different ways to uncover new knowledge and understanding. In the startup world, knowledge and understanding are derived from whether or not consumers choose to leverage a product/service in the physical world. I.e. “Consumer group A chose to use product B in context C. Therefore, product B is well-suited to solve problem Z for consumer group A in context C.” This ties back to the abstract vs applied distinction noted above. Instead of relying upon abstract explorations/analyses with varying research methods, startups are focused on impacts and usage to construct understandings of facts/realities.

Similarities between PhD studies and startups:

  • Both environments breed creators – There is no doubt, both the research world and the startup world nurture creators that seek to uncover new understandings and solutions to problems. As mentioned before, researchers generally construct abstract knowledge whereas startups generally assemble applied solutions to problems. In both environments, individuals are encouraged to perceive the world through different lenses of understanding. That is to say, instead of accepting the unknown as a constant, individuals learn adept practices (relative to their field/business environment) to test the unknown through varying perspectives.
  • Both environments offer profoundly rich learning opportunities – Becoming a successful researcher or startup entrepreneur is awfully challenging. Personally, I haven’t begun to scratch the surface of ‘success’ in either capacity. Though, as mentioned before, I love to learn. Both environments offer incredibly valuable opportunities to learn from and be exposed to new things. For the time being, personally, I’m content to  align ‘success’ with just that.
  • Both environments promote questioning convention – Continuing on with the theme of ‘success’, becoming a successful researcher or startup entrepreneur often requires the ability to adroitly (and tactfully) question convention. Thoughts and perspectives of existant reality must be questioned and investigated so as to influence the future. A researcher can’t simply explore all phenomena with conventional tools and understandings of the past, just as a startup entrepreneur can’t always solve new problems with old solutions. As I touched on earlier, I tend to cling to a socially constructed philosophical worldview. Therefore, I make the underlying assumption that the world that exists today and the artifacts around us are the result of socially constructed norms that have evolved and promulgated over the years. In other words, (in my view) today’s perceptions of the world and existence are the result of a socially constructed past. My approach to questioning convention involves recognizing that socially constructed norms of the past/present can ultimately be manipulated and influence the future. The world of today doesn’t necessarily foreshadow the world of tomorrow.
  • Success in both environments is dependent upon effective collaboration – Again, becoming a successful researcher or startup entrepreneur is very difficult. In most cases, it’s not a job for an individual. Rather, both successful researchers and startup entrepreneurs rely extensively upon effective collaboration. In the case of research, a researcher must obtain as much feedback as possible from colleagues and folks within their community of interest. This is done to mollify concerns associated with bias and subjectivity. In the case of startups, entrepreneurs must collaborate extensively with their co-founders, user base and/or customer base to ensure they are providing the best solutions possible. Both communication and empathy are critical in both capacities.

In the end, one thing is certain — I’m very fortunate to have had the opportunity this past year to explore both the PhD research world as well as the practitioner startup world. I am extremely grateful to all of the supportive faculty at the iSchool for encouraging me to do so. It has been incredibly rewarding. From this year long interdisciplinary journey, I am confident I have a more thorough understanding of both, not simply juxtaposed side by side, but also as lone entities.

I am curious and excited to see where this newfound understanding takes me.

What are some other key characteristics of PhD studies and startups that I am missing? Do you disagree with any of the distinctions and similarities I have listed above?