HyMap: eliciting hypotheses in early-stage software startups using cognitive mapping

TL;DR

This paper introduces HyMap, a cognitive mapping technique designed to help early-stage software startup founders systematically identify hypotheses, thereby facilitating experimentation and reducing risks during product development.

Contribution

It presents a novel visual language and process for eliciting hypotheses through cognitive mapping, tailored specifically for early-stage software startups.

Findings

HyMap is clear, easy to use, and effective in generating hypotheses.

The technique helps founders visualize their understanding of the product.

Evaluation with startups shows practical usefulness and positive feedback.

Abstract

Context: Software startups develop innovative, software-intensive products. Given the uncertainty associated with such an innovative context, experimentation is a valuable approach for these companies, especially in the early stages of the development, when implementing unnecessary features represents a higher risk for companies' survival. Nevertheless, researchers have argued that the lack of clearly defined practices led to limited adoption of experimentation. In this regard, the first step is to define the hypotheses based on which teams will create experiments. Objective: We aim to develop a systematic technique to identify hypotheses for early-stage software startups. Methods: We followed a Design Science approach consisted of three cycles in the construction phase, that involved seven startups in total, and an evaluation of the final artifact within three startups. Results: We developed the HyMap, a hypotheses elicitation technique based on cognitive mapping. It consists of a visual language to depict a cognitive map representing the founder's understanding of the product, and a process to elicit this map consisted of a series of questions the founder must answer. Our evaluation showed that the artifacts are clear, easy to use, and useful leading to hypotheses and facilitating founders to visualize their idea. Conclusion: Our study contributes to both descriptive and prescriptive bodies of knowledge. Regarding the first, it provides a better understanding of the guidance founders use to develop their startups and, for the latter, a technique to identify hypotheses in early-stage software startups.