A First Principles Approach to Product Development in Entrepreneurship
Bibliographic record
Abstract
The purpose of this dissertation is to explore an entrepreneurial framework that this dissertation calls A First Principles Approach to Product Development in Entrepreneurship. The goal is to improve safety for bicycle riders. This research project identifies, applies, and assesses product development methodologies in a way that is grounded in qualitative research. The framework that encompasses those methodologies was created to bridge the gaps between entrepreneurship, engineering design, and industrial design, where the framework was specifically applied to bicycle helmets and examined as a case study. The framework is intended for entrepreneurs, product developers, and researchers who are developing physical, capital-intensive products, where the key stakeholder who is describing a problem is also close to the purchasing decision. Parts of this dissertation describe a framework that is generalizable to other startups and other parts of this dissertation are specific to developing bike helmets. Failure rates in entrepreneurship can often be between 30 to 75%. In addition, there are examples in motorcycling, football, and bicycling where companies are only designing and developing personal protective equipment (PPE) to the industry standard with little or no consideration for other relevant injury biomechanics. In response, this dissertation explores a series of product development methodologies, in a manner that is grounded in qualitative research, with the goal of improving safety for the rider and reducing the risk of failure for launching a new venture. The first principles approach described in this dissertation begins with Customer Discovery which is a process of conducting interviews with key stakeholders, in order to challenge and support evolving hypothesis about a potential new venture. The process begins with deconstructing an initial idea into product, market, and customer risk hypotheses. Next, a process for creating and organizing questions, conducting problem interviews, and pivoting is described. During the problem interviews I found a Severe Problem of bicycle helmet fit for US Bike Parents. Then, I examined other aspects for identifying if an opportunity is worth pursuing, namely finding a gap in the marketplace, and observing industry trends. After conducting the problem interviews, I developed and conducted brainstorming sessions with participants who experienced the Severe Problem. The goal for conducting the brainstorming sessions was to dislodge entrenched product ideas, explore the solution landscape, and find a solution that solved the problem the best and was the best opportunity for commercialization. For this dissertation I determined that a custom fit, 3D printed bicycle helmet was an appropriate solution. Next, I developed and conducted solution interviews to determine if the solution solved the participants problem, identify additional product features, and explore various pricing strategies. Once a product brief was created, the next step was product development. Product development can have many variables which can affect how a new venture team should approach development. For this dissertation initial product development began with designing (ideating), printing (prototyping), and impact testing (testing) 3D printed mesostructures. However, during development the strategy was adjusted in response to production issues (iterating). Now that some initial product development was underway, the next step for a first principles approach includes a critical reflection on different aspects of the new venture. A critical reflection can include a feasibility analysis, which is a formative assessment of the potential new venture, an assessment of Customer Discovery, and the application of the Abstraction Ladder to evaluate the problem driving product development. For this dissertation, a critical reflection also included an evaluation of other product development methodologies, as well as continued application of the Abstraction Ladder to direct the critical evaluation of other foundational elements that relate to bicycle helmets and safety.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.006 | 0.032 |
| Scholarly communication | 0.013 | 0.010 |
| Open science | 0.003 | 0.009 |
| Research integrity | 0.004 | 0.006 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".