What influences the speed of prototyping? An empirical investigation of twenty software startups

It is essential for startups to quickly experiment business ideas by building tangible prototypes and collecting user feedback on them. As prototyping is an inevitable part of learning for early stage software startups, how fast startups can learn depends on how fast they can prototype. Despite of the importance, there is a lack of research about prototyping in software startups. In this study, we aimed at understanding what are factors influencing different types of prototyping activities. We conducted a multiple case study on twenty European software startups. The results are two folds, firstly we propose a prototype-centric learning model in early stage software startups. Secondly, we identify factors occur as barriers but also facilitators for prototyping in early stage software startups. The factors are grouped into (1) artifacts, (2) team competence, (3) collaboration, (4) customer and (5) process dimensions. To speed up a startups progress at the early stage, it is important to incorporate the learning objective into a well-defined collaborative approach of prototyping

💡 Research Summary

This paper investigates the factors that influence the speed of prototyping in early‑stage software startups. Recognizing that rapid experimentation and learning are essential for startups to validate business ideas, the authors note a gap in the literature concerning how quickly startups can produce and iterate on prototypes. To address this, they conducted a multiple‑case study of twenty European software startups, each less than three years old and operating across diverse domains such as fintech, health‑tech, and education. Data were gathered through semi‑structured interviews with founders, CTOs, designers, and other key personnel, complemented by on‑site observations and analysis of project artefacts (e.g., prototype specifications, feedback logs).

The study yields two primary contributions. First, the authors propose a “Prototype‑Centric Learning Model” that reframes the traditional requirements‑design‑implementation‑test pipeline. In this model, a clear learning objective is defined at the outset; a lightweight prototype is then designed and built quickly; user feedback is collected immediately; and the insights are fed back into the next iteration. The prototype itself is treated as a learning instrument rather than a final product, emphasizing speed and adaptability over completeness.

Second, the research identifies a set of factors that act either as barriers or facilitators to fast prototyping. These factors are organized into five dimensions:

1. Artifacts – The availability, reusability, and documentation quality of tools, libraries, UI templates, and deployment environments. Startups that leverage open‑source components, maintain well‑structured version control, and have ready‑made design assets can dramatically shorten development time.

2. Team Competence – Technical skill levels combined with a “prototyping mindset” that embraces rapid experimentation and tolerates failure. Multidisciplinary teams (development, design, business) that can translate ideas into functional prototypes quickly are shown to be a decisive advantage.

3. Collaboration – Internal communication practices (e.g., Slack, daily stand‑ups, sprint reviews) and the efficiency of decision‑making structures. External partnerships (design agencies, technology vendors) also matter; clear contracts and short feedback loops with partners accelerate iteration cycles.

4. Customer – The ease of accessing early users or beta testers and the speed at which they can provide concrete, actionable feedback. When customers are directly involved in testing prototypes and can articulate specific usage scenarios, the learning loop contracts significantly.

5. Process – The degree of automation and standardisation in the workflow from requirement capture to testing and deployment. Continuous Integration/Continuous Deployment (CI/CD) pipelines, automated UI testing, and cloud‑based deployment scripts reduce the “prototype‑to‑user” latency.

The authors emphasize that these dimensions do not operate in isolation; they interact in complex ways. For instance, high team competence can be undermined by poor collaboration processes, while robust artefacts and automated pipelines can compensate for modest skill levels, preserving a reasonable prototyping speed. The most effective acceleration strategy, according to the findings, is to embed the learning objective into a well‑defined, collaborative prototyping approach.

Practical implications include: (a) startups should explicitly state what they aim to learn from each prototype before development begins; (b) they should invest in reusable artefacts and automation to minimise repetitive work; (c) fostering a culture that values rapid feedback and cross‑functional collaboration is essential.

The paper acknowledges several limitations: the sample is confined to European startups, limiting generalisability; the study relies primarily on qualitative data, suggesting a need for quantitative validation (e.g., measuring cycle times, number of iterations); and it does not explore how prototyping dynamics evolve as startups mature beyond the early stage. Future research directions propose cross‑regional comparisons, longitudinal studies tracking prototyping speed over time, and the integration of metric‑driven performance dashboards.

In sum, the research provides a nuanced, empirically grounded view of what makes some startups prototype faster than others, offering both a conceptual model and actionable insights for entrepreneurs, investors, and scholars interested in the mechanics of early‑stage software innovation.